├── .dvc ├── .gitignore └── config ├── .dvcignore ├── .github └── workflows │ ├── codeql.yml │ └── pylint.yml ├── .gitignore ├── Dataset.csv.dvc ├── Dockerfile ├── Facial emotion.ipynb ├── README.md ├── data ├── BERT_semantic_segmentation_model.ipynb ├── Dataset.csv ├── SNLI_Corpus │ ├── snli_1.0_dev.csv │ └── snli_1.0_test.csv ├── data.tar.gz └── normalized_dataset.csv ├── dataset_normalization.ipynb ├── haarcascade_frontalface_default.xml ├── requirements.txt ├── server.py ├── similarity.ipynb ├── static ├── Mainpage_Background.png ├── Mainpage_section1.png ├── Mainpage_section21.png ├── Mainpage_section22.png ├── Mainpage_section23.png ├── Mainpage_section31.png ├── Mainpage_section32.png ├── Mainpage_section33.png ├── Mainpage_section41.png ├── Mainpage_section42.png ├── Mainpage_section43.png ├── Mockvue_logo.png ├── css │ ├── Instructions.css │ ├── Main_page.css │ ├── Text_Test.css │ ├── Text_Test_Results.css │ ├── Video_Test.css │ └── Video_Test_Results.css ├── favicon.ico └── script │ ├── Instructions.js │ ├── Main_page.js │ ├── Text_Test.js │ ├── Text_Test_Results.js │ ├── Video_Test.js │ └── Video_Test_Results.js └── templates ├── Instructions_text.html ├── Instructions_video.html ├── Main_page.html ├── Text_Test.html ├── Text_Test_Results.html ├── Video_Test.html └── Video_Test_Results.html /.dvc/.gitignore: -------------------------------------------------------------------------------- 1 | /config.local 2 | /tmp 3 | /cache 4 | -------------------------------------------------------------------------------- /.dvc/config: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MuhammadMillwala/MockVue-Mock-Job-Interview-System/f2e68d9f1cae60cfd39802c2b9ce24f653a4a4ab/.dvc/config -------------------------------------------------------------------------------- /.dvcignore: -------------------------------------------------------------------------------- 1 | # Add patterns of files dvc should ignore, which could improve 2 | # the performance. Learn more at 3 | # https://dvc.org/doc/user-guide/dvcignore 4 | -------------------------------------------------------------------------------- /.github/workflows/codeql.yml: -------------------------------------------------------------------------------- 1 | # For most projects, this workflow file will not need changing; you simply need 2 | # to commit it to your repository. 3 | # 4 | # You may wish to alter this file to override the set of languages analyzed, 5 | # or to provide custom queries or build logic. 6 | # 7 | # ******** NOTE ******** 8 | # We have attempted to detect the languages in your repository. Please check 9 | # the `language` matrix defined below to confirm you have the correct set of 10 | # supported CodeQL languages. 11 | # 12 | name: "CodeQL" 13 | 14 | on: 15 | push: 16 | branches: [ "main" ] 17 | pull_request: 18 | # The branches below must be a subset of the branches above 19 | branches: [ "main" ] 20 | schedule: 21 | - cron: '15 10 * * 3' 22 | 23 | jobs: 24 | analyze: 25 | name: Analyze 26 | runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }} 27 | timeout-minutes: ${{ (matrix.language == 'swift' && 120) || 360 }} 28 | permissions: 29 | actions: read 30 | contents: read 31 | security-events: write 32 | 33 | strategy: 34 | fail-fast: false 35 | matrix: 36 | language: [ 'javascript', 'python' ] 37 | # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python', 'ruby', 'swift' ] 38 | # Use only 'java' to analyze code written in Java, Kotlin or both 39 | # Use only 'javascript' to analyze code written in JavaScript, TypeScript or both 40 | # Learn more about CodeQL language support at https://aka.ms/codeql-docs/language-support 41 | 42 | steps: 43 | - name: Checkout repository 44 | uses: actions/checkout@v3 45 | 46 | # Initializes the CodeQL tools for scanning. 47 | - name: Initialize CodeQL 48 | uses: github/codeql-action/init@v2 49 | with: 50 | languages: ${{ matrix.language }} 51 | # If you wish to specify custom queries, you can do so here or in a config file. 52 | # By default, queries listed here will override any specified in a config file. 53 | # Prefix the list here with "+" to use these queries and those in the config file. 54 | 55 | # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs 56 | # queries: security-extended,security-and-quality 57 | 58 | 59 | # Autobuild attempts to build any compiled languages (C/C++, C#, Go, or Java). 60 | # If this step fails, then you should remove it and run the build manually (see below) 61 | - name: Autobuild 62 | uses: github/codeql-action/autobuild@v2 63 | 64 | # ℹ️ Command-line programs to run using the OS shell. 65 | # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun 66 | 67 | # If the Autobuild fails above, remove it and uncomment the following three lines. 68 | # modify them (or add more) to build your code if your project, please refer to the EXAMPLE below for guidance. 69 | 70 | # - run: | 71 | # echo "Run, Build Application using script" 72 | # ./location_of_script_within_repo/buildscript.sh 73 | 74 | - name: Perform CodeQL Analysis 75 | uses: github/codeql-action/analyze@v2 76 | with: 77 | category: "/language:${{matrix.language}}" 78 | -------------------------------------------------------------------------------- /.github/workflows/pylint.yml: -------------------------------------------------------------------------------- 1 | name: Pylint 2 | 3 | on: [push] 4 | 5 | jobs: 6 | build: 7 | runs-on: ubuntu-latest 8 | strategy: 9 | matrix: 10 | python-version: ["3.8", "3.9", "3.10"] 11 | steps: 12 | - uses: actions/checkout@v3 13 | - name: Set up Python ${{ matrix.python-version }} 14 | uses: actions/setup-python@v3 15 | with: 16 | python-version: ${{ matrix.python-version }} 17 | - name: Install dependencies 18 | run: | 19 | python -m pip install --upgrade pip 20 | pip install pylint 21 | pip install -r requirements.txt 22 | - name: Analysing the code with pylint 23 | run: | 24 | pylint --disable=all server.py 25 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | /Dataset.csv 2 | -------------------------------------------------------------------------------- /Dataset.csv.dvc: -------------------------------------------------------------------------------- 1 | outs: 2 | - md5: 5e845d18ff327fc39a78cdfa4b1af212 3 | size: 35191 4 | path: Dataset.csv 5 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | # Use the official Python base image 2 | FROM python:3.8-slim 3 | 4 | # Set the working directory in the container 5 | WORKDIR /app 6 | 7 | # Copy the requirements file 8 | COPY requirements.txt . 9 | 10 | # Install OpenGL libraries 11 | RUN apt-get update && \ 12 | apt-get install -y libgl1-mesa-glx 13 | 14 | 15 | # Install additional dependencies 16 | RUN apt-get update && \ 17 | apt-get install -y libglib2.0-0 18 | 19 | # Install the app dependencies 20 | RUN pip install --no-cache-dir -r requirements.txt 21 | 22 | # Install DVC dependencies 23 | RUN apt-get update && \ 24 | apt-get install -y git && \ 25 | pip install dvc 26 | 27 | # Install spaCy and download 'en_core_web_sm' model 28 | RUN pip install spacy && \ 29 | python -m spacy download en_core_web_sm 30 | 31 | # Copy the entire application to the container 32 | COPY . . 33 | 34 | # Set the environment variables 35 | ENV FLASK_APP=server.py 36 | ENV FLASK_RUN_HOST=0.0.0.0 37 | 38 | # Expose the port on which the app will run 39 | EXPOSE 5000 40 | 41 | # Run the app when the container starts 42 | CMD ["flask", "run"] -------------------------------------------------------------------------------- /Facial emotion.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 12, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import cv2\n", 10 | "import subprocess\n", 11 | "from deepface import DeepFace\n", 12 | "import speech_recognition as sr\n", 13 | "import unicodedata\n", 14 | "import string\n", 15 | "import re\n", 16 | "import os\n", 17 | "import time" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 16, 23 | "metadata": {}, 24 | "outputs": [ 25 | { 26 | "name": "stderr", 27 | "output_type": "stream", 28 | "text": [ 29 | "Action: emotion: 100%|██████████| 1/1 [00:00<00:00, 11.56it/s]\n", 30 | "Action: emotion: 100%|██████████| 1/1 [00:00<00:00, 23.25it/s]\n", 31 | "Action: emotion: 100%|██████████| 1/1 [00:00<00:00, 18.34it/s]\n", 32 | "Action: emotion: 100%|██████████| 1/1 [00:00<00:00, 14.38it/s]\n", 33 | "Action: emotion: 100%|██████████| 1/1 [00:00<00:00, 21.67it/s]\n", 34 | "Action: emotion: 100%|██████████| 1/1 [00:00<00:00, 15.50it/s]\n", 35 | "Action: emotion: 100%|██████████| 1/1 [00:00<00:00, 10.05it/s]\n", 36 | "Action: emotion: 100%|██████████| 1/1 [00:00<00:00, 20.83it/s]\n", 37 | "Action: emotion: 100%|██████████| 1/1 [00:00<00:00, 17.24it/s]\n", 38 | "Action: emotion: 100%|██████████| 1/1 [00:00<00:00, 14.93it/s]\n", 39 | "Action: emotion: 100%|██████████| 1/1 [00:00<00:00, 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100%|██████████| 1/1 [00:00<00:00, 9.30it/s]\n", 338 | "Action: emotion: 100%|██████████| 1/1 [00:00<00:00, 10.93it/s]\n", 339 | "Action: emotion: 100%|██████████| 1/1 [00:00<00:00, 16.95it/s]\n", 340 | "Action: emotion: 100%|██████████| 1/1 [00:00<00:00, 11.97it/s]\n", 341 | "Action: emotion: 100%|██████████| 1/1 [00:00<00:00, 13.79it/s]\n" 342 | ] 343 | } 344 | ], 345 | "source": [ 346 | "cap = cv2.VideoCapture('uploads/test.mp4')\n", 347 | "emotion_counts = {\n", 348 | " 'angry': 0,\n", 349 | " 'disgust': 0,\n", 350 | " 'fear': 0,\n", 351 | " 'happy': 0,\n", 352 | " 'sad': 0,\n", 353 | " 'surprise': 0,\n", 354 | " 'neutral': 0,\n", 355 | " 'no_face': 0,\n", 356 | "}\n", 357 | "faceCascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n", 358 | "\n", 359 | "while True:\n", 360 | " # read frame\n", 361 | " ret, frame = cap.read()\n", 362 | " if not ret:\n", 363 | " break\n", 364 | " else:\n", 365 | " # convert to grayscale\n", 366 | " gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n", 367 | "\n", 368 | " # detect faces in the image\n", 369 | " faces = faceCascade.detectMultiScale(\n", 370 | " gray,\n", 371 | " scaleFactor=1.3,\n", 372 | " minNeighbors=5,\n", 373 | " minSize=(30, 30),\n", 374 | " flags=cv2.CASCADE_SCALE_IMAGE\n", 375 | " )\n", 376 | "\n", 377 | " # loop over faces\n", 378 | " for (x, y, w, h) in faces:\n", 379 | " # extract face\n", 380 | " face = frame[y:y+h, x:x+w]\n", 381 | " # recognize emotion if a face is detected\n", 382 | " if len(face) > 0:\n", 383 | " try:\n", 384 | " result = DeepFace.analyze(face, actions=['emotion'], enforce_detection=True)\n", 385 | " if result[0]['dominant_emotion'] is not None:\n", 386 | " emotion_counts[result[0]['dominant_emotion']] += 1\n", 387 | " else:\n", 388 | " no_count['no_emotion'] += 1\n", 389 | " except ValueError as err:\n", 390 | " emotion_counts['no_face'] += 1\n", 391 | " # update the no_face count if no face is detected\n", 392 | " else:\n", 393 | " emotion_counts['no_face'] += 1\n", 394 | "\n", 395 | "emotion_counts['angry'] = emotion_counts['angry'] * 0.2\n", 396 | "emotion_counts['disgust'] = emotion_counts['disgust'] * 0.2\n", 397 | "emotion_counts['fear'] = emotion_counts['fear'] * 0.2\n", 398 | "emotion_counts['happy'] = emotion_counts['happy'] * 1.3\n", 399 | "emotion_counts['sad'] = emotion_counts['sad'] * 0.2\n", 400 | "emotion_counts['neutral'] = emotion_counts['neutral'] * 1\n", 401 | "emotion_counts['no_face'] = 0\n", 402 | "\n", 403 | "command = ['ffmpeg', '-i', 'uploads/test.mp4', '-vn', '-acodec', 'pcm_s16le', '-ar', '44100', '-ac', '2', 'uploads/audio.wav']\n", 404 | "subprocess.call(command)\n", 405 | "\n", 406 | "r = sr.Recognizer()\n", 407 | "audio_file = sr.AudioFile('uploads/audio.wav')\n", 408 | "with audio_file as source:\n", 409 | " audio = r.record(source)\n", 410 | " \n", 411 | "text = r.recognize_google(audio)\n", 412 | "\n", 413 | "\n", 414 | "text_list = text.split(\"for this question\")\n", 415 | "cleaned_list = []\n", 416 | "for text in text_list:\n", 417 | " text = text.translate(str.maketrans('', '', string.punctuation))\n", 418 | " text = re.sub(' +', ' ', text)\n", 419 | " cleaned_list.append(text)\n", 420 | "text_list = cleaned_list\n", 421 | "text_list = [text for text in text_list if text.strip()]\n", 422 | "normalized_list = [unicodedata.normalize('NFKD', text).encode('ASCII', 'ignore').decode('ASCII') for text in text_list]" 423 | ] 424 | }, 425 | { 426 | "cell_type": "code", 427 | "execution_count": null, 428 | "metadata": {}, 429 | "outputs": [ 430 | { 431 | "data": { 432 | "text/plain": [ 433 | "{'angry': 48.3,\n", 434 | " 'disgust': 0.2,\n", 435 | " 'fear': 23.0,\n", 436 | " 'happy': 20.4,\n", 437 | " 'sad': 30.099999999999998,\n", 438 | " 'surprise': 0,\n", 439 | " 'neutral': 45,\n", 440 | " 'no_face': 0}" 441 | ] 442 | }, 443 | "execution_count": 14, 444 | "metadata": {}, 445 | "output_type": "execute_result" 446 | } 447 | ], 448 | "source": [ 449 | "argmax(emotion_counts)" 450 | ] 451 | }, 452 | { 453 | "cell_type": "code", 454 | "execution_count": 15, 455 | "metadata": {}, 456 | "outputs": [ 457 | { 458 | "data": { 459 | "text/plain": [ 460 | "[' I am wearing a grey colored suit ', ' I am recording this on a laptop']" 461 | ] 462 | }, 463 | "execution_count": 15, 464 | "metadata": {}, 465 | "output_type": "execute_result" 466 | } 467 | ], 468 | "source": [ 469 | "normalized_list" 470 | ] 471 | }, 472 | { 473 | "cell_type": "code", 474 | "execution_count": 11, 475 | "metadata": {}, 476 | "outputs": [], 477 | "source": [ 478 | "cap.release()\n", 479 | "time.sleep(1) # Add a 1-second delay\n", 480 | "\n", 481 | "os.remove(\"uploads/test.mp4\")\n", 482 | "os.remove(\"uploads/audio.wav\")" 483 | ] 484 | }, 485 | { 486 | "cell_type": "code", 487 | "execution_count": null, 488 | "metadata": {}, 489 | "outputs": [], 490 | "source": [] 491 | } 492 | ], 493 | "metadata": { 494 | "kernelspec": { 495 | "display_name": "Python 3", 496 | "language": "python", 497 | "name": "python3" 498 | }, 499 | "language_info": { 500 | "codemirror_mode": { 501 | "name": "ipython", 502 | "version": 3 503 | }, 504 | "file_extension": ".py", 505 | "mimetype": "text/x-python", 506 | "name": "python", 507 | "nbconvert_exporter": "python", 508 | "pygments_lexer": "ipython3", 509 | "version": "3.10.5" 510 | }, 511 | "orig_nbformat": 4 512 | }, 513 | "nbformat": 4, 514 | "nbformat_minor": 2 515 | } 516 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MockVue 2 | 3 | ![logo](https://github.com/MuhammadMillwala/MockVue-Mock-Job-Interview-System/assets/106543900/57989c2c-5712-4c7e-a8fc-7f3710836341) 4 | 5 | 6 | MockVue is an AI-based mock job interview system aimed at helping job-seekers, especially fresh graduates, practice giving interviews in the field of Artificial Intelligence. The web application generates interview questions for the candidate, analyzes their competency to answer the questions, and assesses their behavior through facial expression, eye movement, and hand gesture analysis. A score is generated at the end, highlighting the candidate's strengths and weaknesses. 7 | 8 | ## Features 9 | 10 | - AI-generated interview questions specific to Artificial Intelligence. 11 | - Free to use for anyone interested in pursuing a career in AI. 12 | - Speech-to-text conversion for checking the correctness of dialogue spoken by the user. 13 | - Gesture and facial monitoring analysis to provide users with insight into real-world interviews in AI. 14 | - Candidate assessment score that shows the user's strengths and weaknesses. 15 | 16 | ![arch](https://github.com/MuhammadMillwala/MockVue-Mock-Job-Interview-System/assets/106543900/9f358a73-c8d2-452e-8205-c057a3ea124f) 17 | 18 | 19 | ## Scope 20 | 21 | - Question generation 22 | - Speech-to-text conversion 23 | - Object and gesture detection 24 | - Sentiment analysis 25 | - Candidate assessment score - their strengths and weaknesses 26 | 27 | ## Technology Stack 28 | 29 | - Python 30 | - Flask 31 | - TensorFlow 32 | - OpenCV 33 | 34 | ## Getting Started 35 | 36 | To run this application, you will need to have Python, Flask, TensorFlow, and OpenCV installed on your system. 37 | 38 | 1. Clone the repository: 39 | 40 | ``` 41 | git clone https://github.com/yourusername/MockVue.git 42 | ``` 43 | 44 | 2. Install the required packages: 45 | 46 | ``` 47 | pip install -r requirements.txt 48 | ``` 49 | 50 | 3. Start the Flask server: 51 | 52 | ``` 53 | python server.py 54 | ``` 55 | 56 | 4. Navigate to `localhost:5000` in your web browser. 57 | 58 | ## Contributing 59 | 60 | We welcome contributions from the community. To contribute, please fork the repository and submit a pull request. 61 | -------------------------------------------------------------------------------- /data/Dataset.csv: -------------------------------------------------------------------------------- 1 | Questions,Answers,, 2 | What is Artificial Intelligence?,Artificial Intelligence is an area of computer science that emphasizes the creation of intelligent machine that work and reacts like humans.,, 3 | What is an artificial intelligence Neural Networks?,"Artificial intelligence Neural Networks can model mathematically the way biological brain works, allowing the machine to think and learn the same way the humans do- making them capable of recognizing things like speech, objects and animals like we do.",, 4 | Give an explanation on the difference between strong AI and weak AI?,Strong AI makes strong claims that computers can be made to think on a level equal to humans while weak AI simply predicts that some features that are resembling to human intelligence can be incorporated to computer to make it more useful tools.,, 5 | Mention the difference between statistical AI and Classical AI ?,"Statistical AI is more concerned with “inductive” thought like given a set of pattern, induce the trend etc.  While, classical AI, on the other hand, is more concerned with “ deductive” thought given as a set of constraints, deduce a conclusion etc.",, 6 | Which is the best way to go for Game playing problem?,"Heuristic approach is the best way to go for game playing problem, as it will use the technique based on intelligent guesswork. For example, Chess between humans and computers as it will use brute force computation, looking at hundreds of thousands of positions.",, 7 | How is Machine Learning related to Artificial Intelligence?,"Artificial Intelligence is a technique that enables machines to mimic human behavior. Whereas, Machine Learning is a subset of Artificial Intelligence. It is the science of getting computers to act by feeding them data and letting them learn a few tricks on their own, without being explicitly programmed to do so",, 8 | What is Q-Learning?,The Q-learning is a Reinforcement Learning algorithm in which an agent tries to learn the optimal policy from its past experiences with the environment. The past experiences of an agent are a sequence of state-action-rewards:,, 9 | What is Deep Learning?,Deep learning imitates the way our brain works i.e. it learns from experiences. It uses the concepts of neural networks to solve complex problems.,, 10 | What are Bayesian Networks?," Bayesian network is a statistical model that represents a set of variables and their conditional dependencies in the form of a directed acyclic graph. On the occurrence of an event, Bayesian Networks can be used to predict the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could be used to study the relationship between diseases and symptoms. Given various symptoms, the Bayesian network is ideal for computing the probabilities of the presence of various diseases.",, 11 |  Explain the assessment that is used to test the intelligence of a machine.,"In artificial intelligence (AI), a Turing Test is a method of inquiry for determining whether or not a computer is capable of thinking like a human being.",, 12 | Explain reward maximization in Reinforcement Learning.,"The RL agent works based on the theory of reward maximization. This is exactly why the RL agent must be trained in such a way that, he takes the best action so that the reward is maximum.",, 13 | What is exploitation and exploration trade-off?,"An important concept in reinforcement learning is the exploration and exploitation trade-off. Exploration, like the name suggests, is about exploring and capturing more information about an environment. On the other hand, exploitation is about using the already known exploited information to heighten the rewards.Consider the fox and tiger example, where the fox eats only the meat (small) chunks close to him but he doesn’t eat the bigger meat chunks at the top, even though the bigger meat chunks would get him more rewards. If the fox only focuses on the closest reward, he will never reach the big chunks of meat, this is called exploitation. But if the fox decides to explore a bit, it can find the bigger reward i.e. the big chunk of meat. This is exploration.",, 14 | What are hyperparameters in Deep Neural Networks?,"Hyperparameters are variables that define the structure of the network. For example, variables such as the learning rate, define how the network is trained. They are used to define the number of hidden layers that must be present in a network. More hidden units can increase the accuracy of the network, whereas a lesser number of units may cause underfitting.",, 15 | How does data overfitting occur,Overfitting occurs when a statistical model or machine learning algorithm captures the noise of the data. This causes an algorithm to show low bias but high variance in the outcome. ,, 16 | Mention a technique that helps to avoid overfitting in a neural network.,Dropout is a type of regularization technique used to avoid overfitting in a neural network. It is a technique where randomly selected neurons are dropped during training. The Dropout value of a network must be chosen wisely. A value too low will result in a minimal effect and a value too high results in under-learning by the network.,, 17 | " What is Data Science? 18 | ",,,"Data Science is a field of computer science that explicitly deals with turning data into information and extracting meaningful insights out of it. The reason why Data Science is so popular is that the kind of insights it allows us to draw from the available data has led to some major innovations in several products and companies. Using these insights, we are able to determine the taste of a particular customer, the likelihood of a product succeeding in a market." 19 | Differentiate between Data Analytics and Data Science,,,"Data Analytics is a subset of Data Science. The goal of data analytics is to illustrate the precise details of retrieved insights. It requires just basic programming languages. It 20 | order to make decisions. It focuses on just finding the solutions. Data Science is a broad technology that includes various subsets such as Data Analytics, Data Mining, Data Visualization, etc. The goal of data science is to discover meaningful insights from massive dataset s and derive the best possible solutions to 21 | resolve business issues. Requires knowledge in advanced programming languages. Data Science not only focuses on finding the solutions but also predicts the future with 22 | past patterns or insights. A data scientist’s job is to provide insightful data visualizations from raw data that are easily understandable." 23 | "What do you understand about linear regression? 24 | ",,,"Linear regression helps in understanding the linear relationship between the dependent and the independent variables. Linear regression is a supervised learning algorithm, which helps in finding the linear relationship between two variables. One is the predictor or the independent variable and the other is the response or the dependent variable. In Linear Regression, we try to understand how the dependent variable changes w.r.t the independent variable. If there is only one independent variable, then it is called simple linear regression, and if there is more than one independent variable then it is known as" 25 | What do you understand by logistic regression?,,," 26 | Logistic  regression is a classification algorithm that can be used when the dependent variable is binary. Let’s take an example. Here, we are trying to determine whether it will rain or not based on temperature and humidity." 27 | What is Stemming & Lemmatization in NLP?,"Stemming algorithms work by cutting off the end or the beginning of the word, taking into account a list of common prefixes and suffixes that can be found in an inflected word. This indiscriminate cutting can be successful on some occasions, but not always. Lemmatization, on the other hand, takes into consideration the morphological analysis of the words. To do so, it is necessary to have detailed dictionaries which the algorithm can look through to link the form back to its lemma. 28 | 29 | ",, 30 | How is Computer Vision and AI related?,"Computer Vision is a field of Artificial Intelligence that is used to obtain information from images or multi-dimensional data. Machine Learning algorithms such as K-means is used for Image Segmentation, Support Vector Machine is used for Image Classification and so on. Therefore Computer Vision makes use of AI technologies to solve complex problems such as Object Detection, Image Processing, etc.",, 31 | Which is better for image classification? Supervised or unsupervised classification? Justify.,"In supervised classification, the images are manually fed and interpreted by the Machine Learning expert to create feature classes.",, 32 | ,"In unsupervised classification, the Machine Learning software creates feature classes based on image pixel values.",, 33 | ,,, 34 | ,"Therefore, it is better to choose supervised classification for image classification in terms of accuracy.",, 35 | "Finite difference filters in image processing are very susceptible to noise. To cope up with this, which method can you use so that there would be minimal distortions by noise?","Image Smoothing is one of the best methods used for reducing noise by forcing pixels to be more like their neighbors, this reduces any distortions caused by contrasts.",, 36 | How is Game theory and AI related?,"In the context of artificial intelligence(AI) and deep learning systems, game theory is essential to enable some of the key capabilities required in multi-agent environments in which different AI programs need to interact or compete in order to accomplish a goal.”",, 37 | What is “Generality” in AI?,Generality is the measure of ease with which the method can be adapted to different domains of application.,, 38 | What is a top-down parser?,A top-down parser begins by hypothesizing a sentence and successively predicting lower level constituents until individual pre-terminal symbols are written.,, 39 | Mention the difference between breadth first search and best first search in artificial intelligence?,"These are the two strategies which are quite similar. In best first search, we expand the nodes in accordance with the evaluation function. While, in breadth first search a node is expanded in accordance to the cost function of the parent node.",, 40 | What are frames and scripts in “Artificial Intelligence”?,"Frames are a variant of semantic networks which is one of the popular ways of presenting non-procedural knowledge in an expert system. A frame which is an artificial data structure is used to divide knowledge into substructure by representing “stereotyped situations’. Scripts are similar to frames, except the values that fill the slots must be ordered.  Scripts are used in natural language understanding systems to organize a knowledge base in terms of the situation that the system should understand.",, 41 | What is FOPL stands for and explain its role in Artificial Intelligence?,"FOPL stands for First Order Predicate Logic, Predicate Logic provides A language to express assertions about certain “World”. An inference system to deductive apparatus whereby we may draw conclusions from such assertion. A semantic based on set theory.",, 42 | For online search in ‘Artificial Intelligence’ which search agent operates by interleaving computation and action?,"In online search, it will first take action and then observes the environment.",, 43 | Which search algorithm will use a limited amount of memory in online search?,RBFE and SMA* will solve any kind of problem that A* can’t by using a limited amount of memory.,, 44 | In ‘Artificial Intelligence’ where you can use the Bayes rule?,"In Artificial Intelligence to answer the probabilistic queries conditioned on one piece of evidence, Bayes rule can be used.",, 45 | For building a Bayes model how many terms are required?,"For building a Bayes model in AI, three terms are required; they are one conditional probability and two unconditional probability.",, 46 | What is the consequence between a node and its predecessors while creating bayesian network?,"While creating Bayesian Network, the consequence between a node and its predecessors is that a node can be conditionally independent of its predecessors.",, 47 | To answer any query how the Bayesian network can be used?,"If a Bayesian Network is a representative of the joint distribution, then by summing all the relevant joint entries, it can solve any query.",, 48 | What combines inductive methods with the power of first order representations?,Inductive logic programming combines inductive methods with the power of first order representations.,, 49 | In Inductive Logic Programming what needed to be satisfied?,The objective of an Inductive Logic Programming is to come up with a set of sentences for the hypothesis such that the entailment constraint is satisfied.,, 50 | Which algorithm inverts a complete resolution strategy?,"‘Inverse Resolution’ inverts a complete resolution, as it is a complete algorithm for learning first order theories.",, 51 | In speech recognition what kind of signal is used?,"In speech recognition, Acoustic signal is used to identify a sequence of words.",, 52 | In speech recognition which model gives the probability of each word following each word?,Biagram model gives the probability of each word following each other word in speech recognition.,, 53 | What is Hidden Markov Model (HMMs) is used?,Hidden Markov Models are a ubiquitous tool for modelling time series data or to model sequence behaviour.  They are used in almost all current speech recognition systems.,, 54 | "In HMM, where does the additional variable is added?","While staying within the HMM network, the additional state variables can be added to a temporal model.",, 55 | " In Artificial Intelligence, what do semantic analyses used for?","In Artificial Intelligence, to extract the meaning from the group of sentences semantic analysis is used.",, 56 | What is meant by compositional semantics?,"The process of determining the meaning of P*Q from P,Q and* is known as Compositional Semantics.",, 57 | Which process makes different logical expression looks identical?,‘Unification’ process makes different logical expressions identical.  Lifted inferences require finding substitute which can make a different expression looks identical.  This process is called unification.,, 58 | Which is the most straight forward approach for planning algorithm?,State space search is the most straight forward approach for planning algorithm because it takes account of everything for finding a solution.,, 59 | What do you understand by Artificial Intelligence?,"Artificial intelligence is computer science technology that emphasizes creating intelligent machine that can mimic human behavior. Here Intelligent machines can be defined as the machine that can behave like a human, think like a human, and also capable of decision making. It is made up of two words, ""Artificial"" and ""Intelligence,"" which means the ""man-made thinking ability."" 60 | 61 | With artificial intelligence, we do not need to pre-program the machine to perform a task; instead, we can create a machine with the programmed algorithms, and it can work on its ow",, 62 | Why do we need Artificial Intelligence?,"The goal of Artificial intelligence is to create intelligent machines that can mimic human behavior. We need AI for today's world to solve complex problems, make our lives more smoothly by automating the routine work, saving the manpower, and to perform many more other tasks.",, 63 | What do you understand by the reward maximization?,"Reward maximization term is used in reinforcement learning, and which is a goal of the reinforcement learning agent. In RL, a reward is a positive feedback by taking action for a transition from one state to another. If the agent performs a good action by applying optimal policies, he gets a reward, and if he performs a bad action, one reward is subtracted. The goal of the agent is to maximize these rewards by applying optimal policies, which is termed as reward maximization.",, 64 | "What Are Intelligent Agents, and How Are They Used in AI?","Intelligent agents are autonomous entities that use sensors to know what is going on, and then use actuators to perform their tasks or goals. They can be simple or complex and can be programmed to learn to accomplish their jobs better. ",, 65 | "What Is Tensorflow, and What Is It Used For?","TensorFlowis an open-source software library initially developed by the Google Brain Team for use in machine learning and neural networks research. It is used for data-flow programming. TensorFlow makes it much easier to build certain AI features into applications, including natural language processing and speech recognition. ",, 66 | "What Is Machine Learning, and How Does It Relate to AI? ",Machine learning is a subset of AI. The idea is that machines will “learn” and get better at tasks over time rather than having humans continually having to input parameters. Machine learning is a practical application of AI. ,, 67 | "What Are Neural Networks, and How Do They Relate to AI? ","Neural networks are a class of machine learning algorithms. The neuron part of the neural is the computational component, and the network part is how the neurons are connected. Neural networks pass data among themselves, gathering more and more meaning as the data moves along. Because the networks are interconnected, more complex data can be processed more efficiently.",, 68 | Why Is Image Recognition a Key Function of AI?,"Humans are visual, and AI is designed to emulate human brains. Therefore, teaching machines to recognize and categorize images is a crucial part of AI. Image recognition also helps machines to learn (as in machine learning) because the more images that are processed, the better the software gets at recognizing and processing those images. ",, 69 | What Is Automatic Programming?,"Automatic programming is describing what a program should do, and then having the AI system “write” the program.",, 70 | What Are Constraint Satisfaction Problems?,"Constraint Satisfaction Problems (CSPs) are mathematical problems defined as a set of objects, the state of which must meet several constraints. CSPs are useful for AI because the regularity of their formulation offers commonality for analyzing and solving problems. ",, 71 | What Is Supervised Versus Unsupervised Learning?,"Supervised learning is a machine learning process in which outputs are fed back into a computer for the software to learn from, for more accurate results the next time. With supervised learning, the “machine” receives initial training to start. In contrast, unsupervised learning means a computer will learn without initial training to base its knowledge.",, 72 | What role does computer vision play in AI?,"Artificial intelligence (AI) is broken down into a number of subfields, one of which is known as computer vision. Computer vision is the process of teaching computers to understand and collect data from the visual environment, such as graphics. Therefore, AI technology is used by computer vision in order to address complicated challenges such as image analysis, object identification, and other similar issues.",, 73 | Where does Artificial Intelligence go from here?,"It is anticipated that artificial intelligence will continue to have a significant impact on a large number of people as well as almost every sector. Artificial intelligence has become the primary impetus behind the development of new technologies such as robots, the Internet of Things, and large data sets. AI is capable of making an ideal judgment in a split second, which is almost difficult for a person to do. Cancer treatment, cutting-edge global climate solutions, smart transportation, and space research are all being aided by AI. We don't expect it to renounce its position as the driving force behind computer innovation and progress any time soon. Artificial Intelligence will have a greater influence on the globe than any other technological advancement in human history.",, 74 | " What do you comprehend by the phrase ""reward maximization""?","Reinforcement learning uses the phrase ""reward maximization"" to describe the purpose of the agent, which is to maximize rewards. Real-world rewards are positive feedback for doing an action that results in a change in a state. A reward is given to the agent if he uses optimum policies to complete a good deed, and a reward is deducted if he fails to do so. Rewards are maximized by using the best rules possible, which is known as reward maximization.",, 75 | What is your comprehension of hyperparameters?,"The training process is controlled by hyperparameters. Model train performance is directly influenced by these factors, which may be changed to one's liking. They are made known in advance. Algorithm hyperparameters that have no influence on simulation results but can influence the efficiency and acquisition of skills are the other two categories of hyperparameters that may be inferred when accommodating the machine to the learning algorithm.",, 76 | What is a Chatbot?,"A chatbot is a computer program with artificial intelligence (AI) that can converse with humans using natural language processing. The communication may take place on a website, via an application, or through one of the several messaging applications. These chatbots, which are often referred to as digital assistants, are capable of interacting with people either via the exchange of text or by voice commands. The majority of companies now make extensive use of AI chatbots in order to provide round-the-clock, virtual customer service to their clientele.",, 77 | What is the future of Artificial Intelligence?,"Artificial Intelligence has affected many humans and almost every industry, and it is expected to continue to do so. Artificial Intelligence has been the main driver of emerging technologies like the Internet of Things, big data, and robotics. AI can harness the power of a massive amount of data and make an optimal decision in a fraction of seconds, which is almost impossible for a normal human. AI is leading areas that are important for mankind such as cancer research, cutting-edge climate change technologies, smart cars, and space exploration. It has taken the center stage of innovation and development of computing, and it is not ceding the stage in the foreseeable future. Artificial Intelligence is going to impact the world more than anything in the history of mankind.",, 78 | Explain cost function.," A cost function is a scalar function that helps identify how wrong an AI model is with regard to its ability to determine the relationship between X and Y. In other words, it tells us the neural network’s error factor. The neural network works better when the cost function is lower. For instance, it takes the output predicted by the neural network and the actual output and then computes how incorrect the model was in its prediction. So, the cost function will give a lower number if the predictions don’t differ too much from the actual values and vice-versa.",, 79 | Explain vanishing gradient.,"As more layers are added and the distance from the final layer increases, backpropagation is not as helpful in sending information to the lower layers. As a result, the information is sent back, and the gradients start disappearing and becoming small in relation to network weights.",, 80 | Mention the steps of the gradient descent algorithm.,"The gradient descent algorithm helps in optimization and in finding coefficients of parameters that help minimize the cost function. The steps that help achieve this are as follows: Step 1: Give weights (x,y) random values and then compute the error, also called SSE Step 2: Compute the gradient or the change in SSE when you change the value of the weights (x,y) by a small amount. This step helps us identify the direction in which we must move x and y to minimize SSE. Step 3: Adjust the weights with the gradients for achieving optimal values for the minimal SSE. Step 4: Change the weights for predicting and calculating the new error. Step 5: Repeat steps 2 and 3 till the time making more adjustments stops producing significant error reduction.",, 81 | "What is the difference between Artificial Intelligence, Machine Learning, and Deep Learning?","DL is a subset of ML, which is the subset of AI. Hence, AI is the all-encompassing concept that initially erupted in computer science. It was then followed by ML that thrived later, and lastly DL, that is now promising to escalate the advances of AI to another level.",, 82 |  Name some popular programming languages in AI.,"R, python lisp, prolog, java",, 83 |  Name some popular programming languages in AI.,An expert system is an Artificial Intelligence program that has an expert-level knowledge about a specific area of data and its utilisation to react appropriately. These systems tend to have the capability to substitute a human expert. ,, 84 | What is the Tower of Hanoi?,"Tower of Hanoi essentially is a mathematical puzzle that displays how recursion is utilised as a device in building up an algorithm to solve a specific problem. The Tower of Hanoi can be solved using a decision tree and a breadth-first search (BFS) algorithm in AI. With 3 disks, a puzzle can essentially be solved in 7 moves. However, the minimal number of moves required to solve a Tower of Hanoi puzzle is 2n − 1, where n is the number of disks.",, 85 | What is an A* Algorithm search method?,"A* is a computer algorithm in AI that is extensively used for the purpose of finding paths or traversing graphs – to obtain the most optimal route between nodes. It is widely used in solving pathfinding problems in video games. Considering its flexibility and versatility, it can be used in a wide range of contexts. A* is formulated with weighted graphs, which means it can find the best path involving the smallest cost in terms of distance and time. This makes A* an informed search algorithm for best-first search.",, 86 | What is a breadth-first search algorithm?,"BFS algorithm is used to search tree or graph data structures. It starts from the root node, proceeds through neighbouring nodes, and finally moves towards the next level of nodes. Till the arrangement is found and created, it produces one tree at any given moment. As this pursuit is capable of being executed by utilising the FIFO (first-in, first-out) data structure, this strategy gives the shortest path to the solution.",, 87 | What is a Depth-first Search Algorithm?,"Depth-first search (DFS) is an algorithm that is based on LIFO (last-in, first-out). Since recursion is implemented with LIFO stack data structure, the nodes are in a different order than in BFS. The path is stored in each iteration from root to leaf nodes in a linear fashion with space requirement.",, 88 | What is fuzzy logic?,Fuzzy logic is a subset of AI. It is a way of encoding human learning for artificial processing. It is represented as IF-THEN rules. ,, 89 | What is Ensemble Learning?,"Ensemble learning is a computational technique in which classifiers or experts are strategically formed and combined. It is used to improve classification, prediction, and function approximation of any model.",, 90 | Explain tree topology?,"As the name suggests, “Tree” topology has several connected elements arranged like the branches of a tree. The structure has at least three specific levels in the hierarchy. These are scalable and accessible while troubleshooting and are so preferred. A common drawback in this topology is the hindrance or malfunctioning of the primary node.",, 91 | Explain Karl Pearson’s coefficient of correlation?,Karl Pearson’s correlation coefficient is a measure of the strength of a linear association between two variables. It is denoted by r or rxy (where x and y are the two variables involved). This method of correlation draws a line of best fit through the data of two variables.,, 92 | How to select the best hyperparameters in a tree-based model?,Measure the performance over validation data.,, 93 | How would you describe ML to a non-technical person?,"ML is geared toward pattern recognition. A great example of this is your Facebook newsfeed and Netflix’s recommendation engine. In this scenario, ML algorithms observe patterns and learn from them. When you deploy an ML program, it will keep learning and improving with each attempt.",, 94 | What’s selection bias? What other types of biases could you encounter during sampling?,"When you’re dealing with a non-random sample, selection bias will occur due to flaws in the selection process. This happens when a subset of the data is consistently excluded because of a particular attribute. This exclusion will distort results and influence the statistical significance of the test. Other types of biases include survivorship bias and undercoverage bias. It’s important to always consider and reduce such biases because you’ll want your smart algorithms to make accurate predictions based on the data.",, 95 | What’s an eigenvalue? What about an eigenvector?,"The directions along which a particular linear transformation compresses, flips, or stretches is called eigenvalue. Eigenvectors are used to understand these linear transformations. For example, to make better sense of the covariance of the covariance matrix, the eigenvector will help identify the direction in which the covariances are going. The eigenvalues will express the importance of each feature. Eigenvalues and eigenvectors are both critical to computer vision and ML applications.",, 96 | "Would you use batch normalization? If so, can you explain why?","The idea here is to standardize the data before sending it to another layer. This approach helps reduce the impact of previous layers by keeping the mean and variance constant. It also makes the layers independent of each other to achieve rapid convergence. For example, when we normalize features from 0 to 1 or from 1 to 100, it helps accelerate the learning cycle.",, 97 | When is it necessary to update an algorithm?,You should update an algorithm when the underlying data source has been changed or whenever there’s a case of non-stationarity. The algorithm should also be updated when you want the model to evolve as data streams through the infrastructure.,, 98 | What would you do if data in a data set were missing or corrupted?,"Whenever data is missing or corrupted, you either replace it with another value or drop those rows and columns altogether. In Pandas, both isNull() and dropNA() are handy tools to find missing or corrupted data and drop those values. You can also use the fillna() method to fill the invalid values in a placeholder—for example, “0.”",, 99 | "What is transfer learning, and how does it compare to a neural architecture approach?","Transfer learning is a process wherein the learning from an already developed model is resued and built upon for a new task. Essentially, it is using pre-trained models and customizing them for your use case.",, 100 | How do you choose an algorithm for solving artificial intelligence and machine learning issues?,"Before choosing an algorithm for solving artificial intelligence and machine learning issues, I try to identify the issue completely. This helps me to understand the situation to a greater extent. It then allows me to determine the most suitable input and the algorithm. For solving the issue completely, it is necessary to choose an algorithm that is accurate, scalable and simple in nature. For me, it is important to finish the project within the time frame so I focus on analysing the data first and then choose the algorithm accordingly depending on training and build time.",, 101 | What is simulated annealing Algorithm?,"The process is of heating and cooling a metal to change its internal structure. Although, for modifying its physical properties is known as annealing. As soon as the metal cools, it forms a new structure. Also, metal is going to retain its newly obtained properties. Although, we have to keep the variable temperature in a simulated annealing process. First, we have to set high temperature. Then, left it to allow “cool” slowly with the proceeding algorithm. Further, if there is high temperature, algorithm accepts worse solutions with high frequency.",, 102 | What is Natural Language Processing?,We use English language to communicate between an intelligent system and N.L.P. Processing of Natural Language plays an important role in various systems.,, 103 | What is Fuzzy Logic Implementation?,"Basically, it can be implemented in systems with various sizes and capabilities. That should be range from mall micro-controllers to large. Also, it can be implemented in hardware, software, or a combination of both in AI.",, 104 | What are Expert Systems in AI?,"We can say these are computer applications. Also, with the help of this development, we can solve complex problems. It has level of human intelligence and expertise.",, 105 |  Give a brief introduction to robotics?,"Basically, robots have their specific aim. As they manipulate the objects. For Example-  by perceiving, picking, moving, modifying the physical properties of an object.",, 106 | How are artificial intelligence and machine learning different from one another?,"Machine learning and deep learning are the sub-sets of artificial intelligence. While AI is a smart operating system that uses the cognitiobn abilities of a human brain to solve complex problems, machine learning is a computer system that allows machines to learn from the data collected by surveys, among other things. Artificial intelligence processes are concerned with maximizing the chances of success. Meanwhile, machine learning’s main concern is accuracy and pattern, not success.",, 107 | What are some of the popular AI domains?,"The answer to this artificial intelligence interview question will include machine learning, neutral networks, robotics, expert systems, fuzzy logic systems, and natural language processing. These popular sub-sets or domains of artificial intelligence have gained prominence in the last decade.",, 108 | Which is better for image classification? Supervised or unsupervised classification? Justify.,"In supervised classification, the images are manually fed and interpreted by the Machine Learning expert to create feature classes. In unsupervised classification, the Machine Learning software creates feature classes based on image pixel values. Therefore, it is better to choose supervised classification for image classification in terms of accuracy.",, 109 | "inite difference filters in image processing are very susceptible to noise. To cope up with this, which method can you use so that there would be minimal distortions by noise?","Image Smoothing is one of the best methods used for reducing noise by forcing pixels to be more like their neighbors, this reduces any distortions caused by contrasts.",, 110 | What is a Confusion Matrix? How does it work?,"A confusion matrix is ​​a specific table that is commonly used to measure the performance of an algorithm. It is mostly used in supervised learning; in unsupervised learning, it's called the matching matrix. It has two parameters actual and predicted.",, 111 | -------------------------------------------------------------------------------- /data/data.tar.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MuhammadMillwala/MockVue-Mock-Job-Interview-System/f2e68d9f1cae60cfd39802c2b9ce24f653a4a4ab/data/data.tar.gz -------------------------------------------------------------------------------- /data/normalized_dataset.csv: -------------------------------------------------------------------------------- 1 | Questions,Answers 2 | what is artificial intelligence,artificial intelligence is an area of computer science that emphasizes the creation of intelligent machine that work and reacts like humans 3 | what is an artificial intelligence neural networks,artificial intelligence neural networks can model mathematically the way biological brain works allowing the machine to think and learn the same way the humans do making them capable of recognizing things like speech objects and animals like we do 4 | give an explanation on the difference between strong ai and weak ai,strong ai makes strong claims that computers can be made to think on a level equal to humans while weak ai simply predicts that some features that are resembling to human intelligence can be incorporated to computer to make it more useful tools 5 | mention the difference between statistical ai and classical ai,statistical ai is more concerned with inductive thought like given a set of pattern induce the trend etc while classical ai on the other hand is more concerned with deductive thought given as a set of constraints deduce a conclusion etc 6 | which is the best way to go for game playing problem,heuristic approach is the best way to go for game playing problem as it will use the technique based on intelligent guesswork for example chess between humans and computers as it will use brute force computation looking at hundreds of thousands of positions 7 | how is machine learning related to artificial intelligence,artificial intelligence is a technique that enables machines to mimic human behavior whereas machine learning is a subset of artificial intelligence it is the science of getting computers to act by feeding them data and letting them learn a few tricks on their own without being explicitly programmed to do so 8 | what is qlearning,the qlearning is a reinforcement learning algorithm in which an agent tries to learn the optimal policy from its past experiences with the environment the past experiences of an agent are a sequence of stateactionrewards 9 | what is deep learning,deep learning imitates the way our brain works ie it learns from experiences it uses the concepts of neural networks to solve complex problems 10 | what are bayesian networks,bayesian network is a statistical model that represents a set of variables and their conditional dependencies in the form of a directed acyclic graph on the occurrence of an event bayesian networks can be used to predict the likelihood that any one of several possible known causes was the contributing factor for example a bayesian network could be used to study the relationship between diseases and symptoms given various symptoms the bayesian network is ideal for computing the probabilities of the presence of various diseases 11 | explain the assessment that is used to test the intelligence of a machine,in artificial intelligence ai a turing test is a method of inquiry for determining whether or not a computer is capable of thinking like a human being 12 | explain reward maximization in reinforcement learning,the rl agent works based on the theory of reward maximization this is exactly why the rl agent must be trained in such a way that he takes the best action so that the reward is maximum 13 | what is exploitation and exploration tradeoff,an important concept in reinforcement learning is the exploration and exploitation tradeoff exploration like the name suggests is about exploring and capturing more information about an environment on the other hand exploitation is about using the already known exploited information to heighten the rewardsconsider the fox and tiger example where the fox eats only the meat small chunks close to him but he doesnt eat the bigger meat chunks at the top even though the bigger meat chunks would get him more rewards if the fox only focuses on the closest reward he will never reach the big chunks of meat this is called exploitation but if the fox decides to explore a bit it can find the bigger reward ie the big chunk of meat this is exploration 14 | what are hyperparameters in deep neural networks,hyperparameters are variables that define the structure of the network for example variables such as the learning rate define how the network is trained they are used to define the number of hidden layers that must be present in a network more hidden units can increase the accuracy of the network whereas a lesser number of units may cause underfitting 15 | how does data overfitting occur,overfitting occurs when a statistical model or machine learning algorithm captures the noise of the data this causes an algorithm to show low bias but high variance in the outcome 16 | mention a technique that helps to avoid overfitting in a neural network,dropout is a type of regularization technique used to avoid overfitting in a neural network it is a technique where randomly selected neurons are dropped during training the dropout value of a network must be chosen wisely a value too low will result in a minimal effect and a value too high results in underlearning by the network 17 | what is stemming lemmatization in nlp,stemming algorithms work by cutting off the end or the beginning of the word taking into account a list of common prefixes and suffixes that can be found in an inflected word this indiscriminate cutting can be successful on some occasions but not always lemmatization on the other hand takes into consideration the morphological analysis of the words to do so it is necessary to have detailed dictionaries which the algorithm can look through to link the form back to its lemma 18 | how is computer vision and ai related,computer vision is a field of artificial intelligence that is used to obtain information from images or multidimensional data machine learning algorithms such as kmeans is used for image segmentation support vector machine is used for image classification and so on therefore computer vision makes use of ai technologies to solve complex problems such as object detection image processing etc 19 | which is better for image classification supervised or unsupervised classification justify,in supervised classification the images are manually fed and interpreted by the machine learning expert to create feature classes 20 | finite difference filters in image processing are very susceptible to noise to cope up with this which method can you use so that there would be minimal distortions by noise,image smoothing is one of the best methods used for reducing noise by forcing pixels to be more like their neighbors this reduces any distortions caused by contrasts 21 | how is game theory and ai related,in the context of artificial intelligenceai and deep learning systems game theory is essential to enable some of the key capabilities required in multiagent environments in which different ai programs need to interact or compete in order to accomplish a goal 22 | what is generality in ai,generality is the measure of ease with which the method can be adapted to different domains of application 23 | what is a topdown parser,a topdown parser begins by hypothesizing a sentence and successively predicting lower level constituents until individual preterminal symbols are written 24 | mention the difference between breadth first search and best first search in artificial intelligence,these are the two strategies which are quite similar in best first search we expand the nodes in accordance with the evaluation function while in breadth first search a node is expanded in accordance to the cost function of the parent node 25 | what are frames and scripts in artificial intelligence,frames are a variant of semantic networks which is one of the popular ways of presenting nonprocedural knowledge in an expert system a frame which is an artificial data structure is used to divide knowledge into substructure by representing stereotyped situations scripts are similar to frames except the values that fill the slots must be ordered scripts are used in natural language understanding systems to organize a knowledge base in terms of the situation that the system should understand 26 | what is fopl stands for and explain its role in artificial intelligence,fopl stands for first order predicate logic predicate logic provides a language to express assertions about certain world an inference system to deductive apparatus whereby we may draw conclusions from such assertion a semantic based on set theory 27 | for online search in artificial intelligence which search agent operates by interleaving computation and action,in online search it will first take action and then observes the environment 28 | which search algorithm will use a limited amount of memory in online search,rbfe and sma will solve any kind of problem that a cant by using a limited amount of memory 29 | in artificial intelligence where you can use the bayes rule,in artificial intelligence to answer the probabilistic queries conditioned on one piece of evidence bayes rule can be used 30 | for building a bayes model how many terms are required,for building a bayes model in ai three terms are required they are one conditional probability and two unconditional probability 31 | what is the consequence between a node and its predecessors while creating bayesian network,while creating bayesian network the consequence between a node and its predecessors is that a node can be conditionally independent of its predecessors 32 | to answer any query how the bayesian network can be used,if a bayesian network is a representative of the joint distribution then by summing all the relevant joint entries it can solve any query 33 | what combines inductive methods with the power of first order representations,inductive logic programming combines inductive methods with the power of first order representations 34 | in inductive logic programming what needed to be satisfied,the objective of an inductive logic programming is to come up with a set of sentences for the hypothesis such that the entailment constraint is satisfied 35 | which algorithm inverts a complete resolution strategy,inverse resolution inverts a complete resolution as it is a complete algorithm for learning first order theories 36 | in speech recognition what kind of signal is used,in speech recognition acoustic signal is used to identify a sequence of words 37 | in speech recognition which model gives the probability of each word following each word,biagram model gives the probability of each word following each other word in speech recognition 38 | what is hidden markov model hmms is used,hidden markov models are a ubiquitous tool for modelling time series data or to model sequence behaviour they are used in almost all current speech recognition systems 39 | in hmm where does the additional variable is added,while staying within the hmm network the additional state variables can be added to a temporal model 40 | in artificial intelligence what do semantic analyses used for,in artificial intelligence to extract the meaning from the group of sentences semantic analysis is used 41 | what is meant by compositional semantics,the process of determining the meaning of pq from pq and is known as compositional semantics 42 | which process makes different logical expression looks identical,unification process makes different logical expressions identical lifted inferences require finding substitute which can make a different expression looks identical this process is called unification 43 | which is the most straight forward approach for planning algorithm,state space search is the most straight forward approach for planning algorithm because it takes account of everything for finding a solution 44 | what do you understand by artificial intelligence,artificial intelligence is computer science technology that emphasizes creating intelligent machine that can mimic human behavior here intelligent machines can be defined as the machine that can behave like a human think like a human and also capable of decision making it is made up of two words artificial and intelligence which means the manmade thinking ability with artificial intelligence we do not need to preprogram the machine to perform a task instead we can create a machine with the programmed algorithms and it can work on its ow 45 | why do we need artificial intelligence,the goal of artificial intelligence is to create intelligent machines that can mimic human behavior we need ai for todays world to solve complex problems make our lives more smoothly by automating the routine work saving the manpower and to perform many more other tasks 46 | what do you understand by the reward maximization,reward maximization term is used in reinforcement learning and which is a goal of the reinforcement learning agent in rl a reward is a positive feedback by taking action for a transition from one state to another if the agent performs a good action by applying optimal policies he gets a reward and if he performs a bad action one reward is subtracted the goal of the agent is to maximize these rewards by applying optimal policies which is termed as reward maximization 47 | what are intelligent agents and how are they used in ai,intelligent agents are autonomous entities that use sensors to know what is going on and then use actuators to perform their tasks or goals they can be simple or complex and can be programmed to learn to accomplish their jobs better 48 | what is tensorflow and what is it used for,tensorflowis an opensource software library initially developed by the google brain team for use in machine learning and neural networks research it is used for dataflow programming tensorflow makes it much easier to build certain ai features into applications including natural language processing and speech recognition 49 | what is machine learning and how does it relate to ai,machine learning is a subset of ai the idea is that machines will learn and get better at tasks over time rather than having humans continually having to input parameters machine learning is a practical application of ai 50 | what are neural networks and how do they relate to ai,neural networks are a class of machine learning algorithms the neuron part of the neural is the computational component and the network part is how the neurons are connected neural networks pass data among themselves gathering more and more meaning as the data moves along because the networks are interconnected more complex data can be processed more efficiently 51 | why is image recognition a key function of ai,humans are visual and ai is designed to emulate human brains therefore teaching machines to recognize and categorize images is a crucial part of ai image recognition also helps machines to learn as in machine learning because the more images that are processed the better the software gets at recognizing and processing those images 52 | what is automatic programming,automatic programming is describing what a program should do and then having the ai system write the program 53 | what are constraint satisfaction problems,constraint satisfaction problems csps are mathematical problems defined as a set of objects the state of which must meet several constraints csps are useful for ai because the regularity of their formulation offers commonality for analyzing and solving problems 54 | what is supervised versus unsupervised learning,supervised learning is a machine learning process in which outputs are fed back into a computer for the software to learn from for more accurate results the next time with supervised learning the machine receives initial training to start in contrast unsupervised learning means a computer will learn without initial training to base its knowledge 55 | what role does computer vision play in ai,artificial intelligence ai is broken down into a number of subfields one of which is known as computer vision computer vision is the process of teaching computers to understand and collect data from the visual environment such as graphics therefore ai technology is used by computer vision in order to address complicated challenges such as image analysis object identification and other similar issues 56 | where does artificial intelligence go from here,it is anticipated that artificial intelligence will continue to have a significant impact on a large number of people as well as almost every sector artificial intelligence has become the primary impetus behind the development of new technologies such as robots the internet of things and large data sets ai is capable of making an ideal judgment in a split second which is almost difficult for a person to do cancer treatment cuttingedge global climate solutions smart transportation and space research are all being aided by ai we dont expect it to renounce its position as the driving force behind computer innovation and progress any time soon artificial intelligence will have a greater influence on the globe than any other technological advancement in human history 57 | what do you comprehend by the phrase reward maximization,reinforcement learning uses the phrase reward maximization to describe the purpose of the agent which is to maximize rewards realworld rewards are positive feedback for doing an action that results in a change in a state a reward is given to the agent if he uses optimum policies to complete a good deed and a reward is deducted if he fails to do so rewards are maximized by using the best rules possible which is known as reward maximization 58 | what is your comprehension of hyperparameters,the training process is controlled by hyperparameters model train performance is directly influenced by these factors which may be changed to ones liking they are made known in advance algorithm hyperparameters that have no influence on simulation results but can influence the efficiency and acquisition of skills are the other two categories of hyperparameters that may be inferred when accommodating the machine to the learning algorithm 59 | what is a chatbot,a chatbot is a computer program with artificial intelligence ai that can converse with humans using natural language processing the communication may take place on a website via an application or through one of the several messaging applications these chatbots which are often referred to as digital assistants are capable of interacting with people either via the exchange of text or by voice commands the majority of companies now make extensive use of ai chatbots in order to provide roundtheclock virtual customer service to their clientele 60 | what is the future of artificial intelligence,artificial intelligence has affected many humans and almost every industry and it is expected to continue to do so artificial intelligence has been the main driver of emerging technologies like the internet of things big data and robotics ai can harness the power of a massive amount of data and make an optimal decision in a fraction of seconds which is almost impossible for a normal human ai is leading areas that are important for mankind such as cancer research cuttingedge climate change technologies smart cars and space exploration it has taken the center stage of innovation and development of computing and it is not ceding the stage in the foreseeable future artificial intelligence is going to impact the world more than anything in the history of mankind 61 | explain cost function,a cost function is a scalar function that helps identify how wrong an ai model is with regard to its ability to determine the relationship between x and y in other words it tells us the neural networks error factor the neural network works better when the cost function is lower for instance it takes the output predicted by the neural network and the actual output and then computes how incorrect the model was in its prediction so the cost function will give a lower number if the predictions dont differ too much from the actual values and viceversa 62 | explain vanishing gradient,as more layers are added and the distance from the final layer increases backpropagation is not as helpful in sending information to the lower layers as a result the information is sent back and the gradients start disappearing and becoming small in relation to network weights 63 | mention the steps of the gradient descent algorithm,the gradient descent algorithm helps in optimization and in finding coefficients of parameters that help minimize the cost function the steps that help achieve this are as follows step 1 give weights xy random values and then compute the error also called sse step 2 compute the gradient or the change in sse when you change the value of the weights xy by a small amount this step helps us identify the direction in which we must move x and y to minimize sse step 3 adjust the weights with the gradients for achieving optimal values for the minimal sse step 4 change the weights for predicting and calculating the new error step 5 repeat steps 2 and 3 till the time making more adjustments stops producing significant error reduction 64 | what is the difference between artificial intelligence machine learning and deep learning,dl is a subset of ml which is the subset of ai hence ai is the allencompassing concept that initially erupted in computer science it was then followed by ml that thrived later and lastly dl that is now promising to escalate the advances of ai to another level 65 | name some popular programming languages in ai,r python lisp prolog java 66 | name some popular programming languages in ai,an expert system is an artificial intelligence program that has an expertlevel knowledge about a specific area of data and its utilisation to react appropriately these systems tend to have the capability to substitute a human expert 67 | what is the tower of hanoi,tower of hanoi essentially is a mathematical puzzle that displays how recursion is utilised as a device in building up an algorithm to solve a specific problem the tower of hanoi can be solved using a decision tree and a breadthfirst search bfs algorithm in ai with 3 disks a puzzle can essentially be solved in 7 moves however the minimal number of moves required to solve a tower of hanoi puzzle is 2n 1 where n is the number of disks 68 | what is an a algorithm search method,a is a computer algorithm in ai that is extensively used for the purpose of finding paths or traversing graphs to obtain the most optimal route between nodes it is widely used in solving pathfinding problems in video games considering its flexibility and versatility it can be used in a wide range of contexts a is formulated with weighted graphs which means it can find the best path involving the smallest cost in terms of distance and time this makes a an informed search algorithm for bestfirst search 69 | what is a breadthfirst search algorithm,bfs algorithm is used to search tree or graph data structures it starts from the root node proceeds through neighbouring nodes and finally moves towards the next level of nodes till the arrangement is found and created it produces one tree at any given moment as this pursuit is capable of being executed by utilising the fifo firstin firstout data structure this strategy gives the shortest path to the solution 70 | what is a depthfirst search algorithm,depthfirst search dfs is an algorithm that is based on lifo lastin firstout since recursion is implemented with lifo stack data structure the nodes are in a different order than in bfs the path is stored in each iteration from root to leaf nodes in a linear fashion with space requirement 71 | what is fuzzy logic,fuzzy logic is a subset of ai it is a way of encoding human learning for artificial processing it is represented as ifthen rules 72 | what is ensemble learning,ensemble learning is a computational technique in which classifiers or experts are strategically formed and combined it is used to improve classification prediction and function approximation of any model 73 | explain tree topology,as the name suggests tree topology has several connected elements arranged like the branches of a tree the structure has at least three specific levels in the hierarchy these are scalable and accessible while troubleshooting and are so preferred a common drawback in this topology is the hindrance or malfunctioning of the primary node 74 | explain karl pearsons coefficient of correlation,karl pearsons correlation coefficient is a measure of the strength of a linear association between two variables it is denoted by r or rxy where x and y are the two variables involved this method of correlation draws a line of best fit through the data of two variables 75 | how to select the best hyperparameters in a treebased model,measure the performance over validation data 76 | how would you describe ml to a nontechnical person,ml is geared toward pattern recognition a great example of this is your facebook newsfeed and netflixs recommendation engine in this scenario ml algorithms observe patterns and learn from them when you deploy an ml program it will keep learning and improving with each attempt 77 | whats selection bias what other types of biases could you encounter during sampling,when youre dealing with a nonrandom sample selection bias will occur due to flaws in the selection process this happens when a subset of the data is consistently excluded because of a particular attribute this exclusion will distort results and influence the statistical significance of the test other types of biases include survivorship bias and undercoverage bias its important to always consider and reduce such biases because youll want your smart algorithms to make accurate predictions based on the data 78 | whats an eigenvalue what about an eigenvector,the directions along which a particular linear transformation compresses flips or stretches is called eigenvalue eigenvectors are used to understand these linear transformations for example to make better sense of the covariance of the covariance matrix the eigenvector will help identify the direction in which the covariances are going the eigenvalues will express the importance of each feature eigenvalues and eigenvectors are both critical to computer vision and ml applications 79 | would you use batch normalization if so can you explain why,the idea here is to standardize the data before sending it to another layer this approach helps reduce the impact of previous layers by keeping the mean and variance constant it also makes the layers independent of each other to achieve rapid convergence for example when we normalize features from 0 to 1 or from 1 to 100 it helps accelerate the learning cycle 80 | when is it necessary to update an algorithm,you should update an algorithm when the underlying data source has been changed or whenever theres a case of nonstationarity the algorithm should also be updated when you want the model to evolve as data streams through the infrastructure 81 | what would you do if data in a data set were missing or corrupted,whenever data is missing or corrupted you either replace it with another value or drop those rows and columns altogether in pandas both isnull and dropna are handy tools to find missing or corrupted data and drop those values you can also use the fillna method to fill the invalid values in a placeholderfor example 0 82 | what is transfer learning and how does it compare to a neural architecture approach,transfer learning is a process wherein the learning from an already developed model is resued and built upon for a new task essentially it is using pretrained models and customizing them for your use case 83 | how do you choose an algorithm for solving artificial intelligence and machine learning issues,before choosing an algorithm for solving artificial intelligence and machine learning issues i try to identify the issue completely this helps me to understand the situation to a greater extent it then allows me to determine the most suitable input and the algorithm for solving the issue completely it is necessary to choose an algorithm that is accurate scalable and simple in nature for me it is important to finish the project within the time frame so i focus on analysing the data first and then choose the algorithm accordingly depending on training and build time 84 | what is simulated annealing algorithm,the process is of heating and cooling a metal to change its internal structure although for modifying its physical properties is known as annealing as soon as the metal cools it forms a new structure also metal is going to retain its newly obtained properties although we have to keep the variable temperature in a simulated annealing process first we have to set high temperature then left it to allow cool slowly with the proceeding algorithm further if there is high temperature algorithm accepts worse solutions with high frequency 85 | what is natural language processing,we use english language to communicate between an intelligent system and nlp processing of natural language plays an important role in various systems 86 | what is fuzzy logic implementation,basically it can be implemented in systems with various sizes and capabilities that should be range from mall microcontrollers to large also it can be implemented in hardware software or a combination of both in ai 87 | what are expert systems in ai,we can say these are computer applications also with the help of this development we can solve complex problems it has level of human intelligence and expertise 88 | give a brief introduction to robotics,basically robots have their specific aim as they manipulate the objects for example by perceiving picking moving modifying the physical properties of an object 89 | how are artificial intelligence and machine learning different from one another,machine learning and deep learning are the subsets of artificial intelligence while ai is a smart operating system that uses the cognitiobn abilities of a human brain to solve complex problems machine learning is a computer system that allows machines to learn from the data collected by surveys among other things artificial intelligence processes are concerned with maximizing the chances of success meanwhile machine learnings main concern is accuracy and pattern not success 90 | what are some of the popular ai domains,the answer to this artificial intelligence interview question will include machine learning neutral networks robotics expert systems fuzzy logic systems and natural language processing these popular subsets or domains of artificial intelligence have gained prominence in the last decade 91 | which is better for image classification supervised or unsupervised classification justify,in supervised classification the images are manually fed and interpreted by the machine learning expert to create feature classes in unsupervised classification the machine learning software creates feature classes based on image pixel values therefore it is better to choose supervised classification for image classification in terms of accuracy 92 | inite difference filters in image processing are very susceptible to noise to cope up with this which method can you use so that there would be minimal distortions by noise,image smoothing is one of the best methods used for reducing noise by forcing pixels to be more like their neighbors this reduces any distortions caused by contrasts 93 | what is a confusion matrix how does it work,a confusion matrix is a specific table that is commonly used to measure the performance of an algorithm it is mostly used in supervised learning in unsupervised learning its called the matching matrix it has two parameters actual and predicted 94 | -------------------------------------------------------------------------------- /dataset_normalization.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "import string\n", 11 | "import re\n", 12 | "import numpy as np" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 2, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "df = pd.read_csv('data/Dataset.csv')" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 3, 27 | "metadata": {}, 28 | "outputs": [], 29 | "source": [ 30 | "df = df.fillna(value=np.nan)" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": 4, 36 | "metadata": {}, 37 | "outputs": [ 38 | { 39 | "data": { 40 | "text/html": [ 41 | "
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QuestionsAnswersUnnamed: 2Unnamed: 3
0What is Artificial Intelligence?Artificial Intelligence is an area of computer...NaNNaN
1What is an artificial intelligence Neural Netw...Artificial intelligence Neural Networks can mo...NaNNaN
2Give an explanation on the difference between ...Strong AI makes strong claims that computers c...NaNNaN
3Mention the difference between statistical AI ...Statistical AI is more concerned with “inducti...NaNNaN
4Which is the best way to go for Game playing ...Heuristic approach is the best way to go for g...NaNNaN
...............
94How are artificial intelligence and machine le...Machine learning and deep learning are the sub...NaNNaN
95What are some of the popular AI domains?The answer to this artificial intelligence int...NaNNaN
96Which is better for image classification? Supe...In supervised classification, the images are m...NaNNaN
97inite difference filters in image processing a...Image Smoothing is one of the best methods use...NaNNaN
98What is a Confusion Matrix? How does it work?A confusion matrix is ​​a specific table that ...NaNNaN
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99 rows × 4 columns

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" 147 | ], 148 | "text/plain": [ 149 | " Questions \\\n", 150 | "0 What is Artificial Intelligence? \n", 151 | "1 What is an artificial intelligence Neural Netw... \n", 152 | "2 Give an explanation on the difference between ... \n", 153 | "3 Mention the difference between statistical AI ... \n", 154 | "4 Which is the best way to go for Game playing ... \n", 155 | ".. ... \n", 156 | "94 How are artificial intelligence and machine le... \n", 157 | "95 What are some of the popular AI domains? \n", 158 | "96 Which is better for image classification? Supe... \n", 159 | "97 inite difference filters in image processing a... \n", 160 | "98 What is a Confusion Matrix? How does it work? \n", 161 | "\n", 162 | " Answers Unnamed: 2 Unnamed: 3 \n", 163 | "0 Artificial Intelligence is an area of computer... NaN NaN \n", 164 | "1 Artificial intelligence Neural Networks can mo... NaN NaN \n", 165 | "2 Strong AI makes strong claims that computers c... NaN NaN \n", 166 | "3 Statistical AI is more concerned with “inducti... NaN NaN \n", 167 | "4 Heuristic approach is the best way to go for g... NaN NaN \n", 168 | ".. ... ... ... \n", 169 | "94 Machine learning and deep learning are the sub... NaN NaN \n", 170 | "95 The answer to this artificial intelligence int... NaN NaN \n", 171 | "96 In supervised classification, the images are m... NaN NaN \n", 172 | "97 Image Smoothing is one of the best methods use... NaN NaN \n", 173 | "98 A confusion matrix is ​​a specific table that ... NaN NaN \n", 174 | "\n", 175 | "[99 rows x 4 columns]" 176 | ] 177 | }, 178 | "execution_count": 4, 179 | "metadata": {}, 180 | "output_type": "execute_result" 181 | } 182 | ], 183 | "source": [ 184 | "df" 185 | ] 186 | }, 187 | { 188 | "cell_type": "code", 189 | "execution_count": 5, 190 | "metadata": {}, 191 | "outputs": [], 192 | "source": [ 193 | "df = df.drop('Unnamed: 2', axis=1)\n", 194 | "df = df.drop('Unnamed: 3', axis=1)" 195 | ] 196 | }, 197 | { 198 | "cell_type": "code", 199 | "execution_count": 64, 200 | "metadata": {}, 201 | "outputs": [], 202 | "source": [ 203 | "df.fillna(value='None', inplace=True)" 204 | ] 205 | }, 206 | { 207 | "cell_type": "code", 208 | "execution_count": 7, 209 | "metadata": {}, 210 | "outputs": [], 211 | "source": [ 212 | "df = df.dropna()" 213 | ] 214 | }, 215 | { 216 | "cell_type": "code", 217 | "execution_count": 8, 218 | "metadata": {}, 219 | "outputs": [ 220 | { 221 | "data": { 222 | "text/html": [ 223 | "
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QuestionsAnswers
0What is Artificial Intelligence?Artificial Intelligence is an area of computer...
1What is an artificial intelligence Neural Netw...Artificial intelligence Neural Networks can mo...
2Give an explanation on the difference between ...Strong AI makes strong claims that computers c...
3Mention the difference between statistical AI ...Statistical AI is more concerned with “inducti...
4Which is the best way to go for Game playing ...Heuristic approach is the best way to go for g...
5How is Machine Learning related to Artificial ...Artificial Intelligence is a technique that en...
6What is Q-Learning?The Q-learning is a Reinforcement Learning alg...
7What is Deep Learning?Deep learning imitates the way our brain works...
8What are Bayesian Networks?Bayesian network is a statistical model that ...
9Explain the assessment that is used to test t...In artificial intelligence (AI), a Turing Test...
10Explain reward maximization in Reinforcement L...The RL agent works based on the theory of rewa...
11What is exploitation and exploration trade-off?An important concept in reinforcement learning...
12What are hyperparameters in Deep Neural Networks?Hyperparameters are variables that define the ...
13How does data overfitting occurOverfitting occurs when a statistical model or...
14Mention a technique that helps to avoid overfi...Dropout is a type of regularization technique ...
19What is Stemming & Lemmatization in NLP?Stemming algorithms work by cutting off the en...
20How is Computer Vision and AI related?Computer Vision is a field of Artificial Intel...
21Which is better for image classification? Supe...In supervised classification, the images are m...
25Finite difference filters in image processing ...Image Smoothing is one of the best methods use...
26How is Game theory and AI related?In the context of artificial intelligence(AI) ...
\n", 348 | "
" 349 | ], 350 | "text/plain": [ 351 | " Questions \\\n", 352 | "0 What is Artificial Intelligence? \n", 353 | "1 What is an artificial intelligence Neural Netw... \n", 354 | "2 Give an explanation on the difference between ... \n", 355 | "3 Mention the difference between statistical AI ... \n", 356 | "4 Which is the best way to go for Game playing ... \n", 357 | "5 How is Machine Learning related to Artificial ... \n", 358 | "6 What is Q-Learning? \n", 359 | "7 What is Deep Learning? \n", 360 | "8 What are Bayesian Networks? \n", 361 | "9  Explain the assessment that is used to test t... \n", 362 | "10 Explain reward maximization in Reinforcement L... \n", 363 | "11 What is exploitation and exploration trade-off? \n", 364 | "12 What are hyperparameters in Deep Neural Networks? \n", 365 | "13 How does data overfitting occur \n", 366 | "14 Mention a technique that helps to avoid overfi... \n", 367 | "19 What is Stemming & Lemmatization in NLP? \n", 368 | "20 How is Computer Vision and AI related? \n", 369 | "21 Which is better for image classification? Supe... \n", 370 | "25 Finite difference filters in image processing ... \n", 371 | "26 How is Game theory and AI related? \n", 372 | "\n", 373 | " Answers \n", 374 | "0 Artificial Intelligence is an area of computer... \n", 375 | "1 Artificial intelligence Neural Networks can mo... \n", 376 | "2 Strong AI makes strong claims that computers c... \n", 377 | "3 Statistical AI is more concerned with “inducti... \n", 378 | "4 Heuristic approach is the best way to go for g... \n", 379 | "5 Artificial Intelligence is a technique that en... \n", 380 | "6 The Q-learning is a Reinforcement Learning alg... \n", 381 | "7 Deep learning imitates the way our brain works... \n", 382 | "8  Bayesian network is a statistical model that ... \n", 383 | "9 In artificial intelligence (AI), a Turing Test... \n", 384 | "10 The RL agent works based on the theory of rewa... \n", 385 | "11 An important concept in reinforcement learning... \n", 386 | "12 Hyperparameters are variables that define the ... \n", 387 | "13 Overfitting occurs when a statistical model or... \n", 388 | "14 Dropout is a type of regularization technique ... \n", 389 | "19 Stemming algorithms work by cutting off the en... \n", 390 | "20 Computer Vision is a field of Artificial Intel... \n", 391 | "21 In supervised classification, the images are m... \n", 392 | "25 Image Smoothing is one of the best methods use... \n", 393 | "26 In the context of artificial intelligence(AI) ... " 394 | ] 395 | }, 396 | "execution_count": 8, 397 | "metadata": {}, 398 | "output_type": "execute_result" 399 | } 400 | ], 401 | "source": [ 402 | "df.head(20)" 403 | ] 404 | }, 405 | { 406 | "cell_type": "code", 407 | "execution_count": 9, 408 | "metadata": {}, 409 | "outputs": [], 410 | "source": [ 411 | "df['Answers'] = df['Answers'].fillna('').astype(str)\n", 412 | "df['Answers'] = df['Answers'].apply(lambda x: x.translate(str.maketrans('', '', string.punctuation)))\n", 413 | "df['Answers'] = df['Answers'].apply(lambda x: re.sub(r'[^\\w\\s]', '', x))\n", 414 | "df['Answers'] = df['Answers'].apply(lambda x: x.lower())\n", 415 | "df['Answers'] = df['Answers'].apply(lambda x: re.sub(r'\\s+', ' ', x).strip())\n", 416 | "\n", 417 | "df['Questions'] = df['Questions'].fillna('').astype(str)\n", 418 | "df['Questions'] = df['Questions'].apply(lambda x: x.translate(str.maketrans('', '', string.punctuation)))\n", 419 | "df['Questions'] = df['Questions'].apply(lambda x: re.sub(r'[^\\w\\s]', '', x))\n", 420 | "df['Questions'] = df['Questions'].apply(lambda x: x.lower())\n", 421 | "df['Questions'] = df['Questions'].apply(lambda x: re.sub(r'\\s+', ' ', x).strip())" 422 | ] 423 | }, 424 | { 425 | "cell_type": "code", 426 | "execution_count": 10, 427 | "metadata": {}, 428 | "outputs": [ 429 | { 430 | "data": { 431 | "text/html": [ 432 | "
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QuestionsAnswers
0what is artificial intelligenceartificial intelligence is an area of computer...
1what is an artificial intelligence neural netw...artificial intelligence neural networks can mo...
2give an explanation on the difference between ...strong ai makes strong claims that computers c...
3mention the difference between statistical ai ...statistical ai is more concerned with inductiv...
4which is the best way to go for game playing p...heuristic approach is the best way to go for g...
.........
94how are artificial intelligence and machine le...machine learning and deep learning are the sub...
95what are some of the popular ai domainsthe answer to this artificial intelligence int...
96which is better for image classification super...in supervised classification the images are ma...
97inite difference filters in image processing a...image smoothing is one of the best methods use...
98what is a confusion matrix how does it worka confusion matrix is a specific table that is...
\n", 512 | "

92 rows × 2 columns

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" 514 | ], 515 | "text/plain": [ 516 | " Questions \\\n", 517 | "0 what is artificial intelligence \n", 518 | "1 what is an artificial intelligence neural netw... \n", 519 | "2 give an explanation on the difference between ... \n", 520 | "3 mention the difference between statistical ai ... \n", 521 | "4 which is the best way to go for game playing p... \n", 522 | ".. ... \n", 523 | "94 how are artificial intelligence and machine le... \n", 524 | "95 what are some of the popular ai domains \n", 525 | "96 which is better for image classification super... \n", 526 | "97 inite difference filters in image processing a... \n", 527 | "98 what is a confusion matrix how does it work \n", 528 | "\n", 529 | " Answers \n", 530 | "0 artificial intelligence is an area of computer... \n", 531 | "1 artificial intelligence neural networks can mo... \n", 532 | "2 strong ai makes strong claims that computers c... \n", 533 | "3 statistical ai is more concerned with inductiv... \n", 534 | "4 heuristic approach is the best way to go for g... \n", 535 | ".. ... \n", 536 | "94 machine learning and deep learning are the sub... \n", 537 | "95 the answer to this artificial intelligence int... \n", 538 | "96 in supervised classification the images are ma... \n", 539 | "97 image smoothing is one of the best methods use... \n", 540 | "98 a confusion matrix is a specific table that is... \n", 541 | "\n", 542 | "[92 rows x 2 columns]" 543 | ] 544 | }, 545 | "execution_count": 10, 546 | "metadata": {}, 547 | "output_type": "execute_result" 548 | } 549 | ], 550 | "source": [ 551 | "df" 552 | ] 553 | }, 554 | { 555 | "cell_type": "code", 556 | "execution_count": 11, 557 | "metadata": {}, 558 | "outputs": [ 559 | { 560 | "data": { 561 | "text/html": [ 562 | "
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QuestionsAnswers
0what is artificial intelligenceartificial intelligence is an area of computer...
1what is an artificial intelligence neural netw...artificial intelligence neural networks can mo...
2give an explanation on the difference between ...strong ai makes strong claims that computers c...
3mention the difference between statistical ai ...statistical ai is more concerned with inductiv...
4which is the best way to go for game playing p...heuristic approach is the best way to go for g...
5how is machine learning related to artificial ...artificial intelligence is a technique that en...
6what is qlearningthe qlearning is a reinforcement learning algo...
7what is deep learningdeep learning imitates the way our brain works...
8what are bayesian networksbayesian network is a statistical model that r...
9explain the assessment that is used to test th...in artificial intelligence ai a turing test is...
10explain reward maximization in reinforcement l...the rl agent works based on the theory of rewa...
11what is exploitation and exploration tradeoffan important concept in reinforcement learning...
12what are hyperparameters in deep neural networkshyperparameters are variables that define the ...
13how does data overfitting occuroverfitting occurs when a statistical model or...
14mention a technique that helps to avoid overfi...dropout is a type of regularization technique ...
19what is stemming lemmatization in nlpstemming algorithms work by cutting off the en...
20how is computer vision and ai relatedcomputer vision is a field of artificial intel...
21which is better for image classification super...in supervised classification the images are ma...
25finite difference filters in image processing ...image smoothing is one of the best methods use...
26how is game theory and ai relatedin the context of artificial intelligenceai an...
\n", 687 | "
" 688 | ], 689 | "text/plain": [ 690 | " Questions \\\n", 691 | "0 what is artificial intelligence \n", 692 | "1 what is an artificial intelligence neural netw... \n", 693 | "2 give an explanation on the difference between ... \n", 694 | "3 mention the difference between statistical ai ... \n", 695 | "4 which is the best way to go for game playing p... \n", 696 | "5 how is machine learning related to artificial ... \n", 697 | "6 what is qlearning \n", 698 | "7 what is deep learning \n", 699 | "8 what are bayesian networks \n", 700 | "9 explain the assessment that is used to test th... \n", 701 | "10 explain reward maximization in reinforcement l... \n", 702 | "11 what is exploitation and exploration tradeoff \n", 703 | "12 what are hyperparameters in deep neural networks \n", 704 | "13 how does data overfitting occur \n", 705 | "14 mention a technique that helps to avoid overfi... \n", 706 | "19 what is stemming lemmatization in nlp \n", 707 | "20 how is computer vision and ai related \n", 708 | "21 which is better for image classification super... \n", 709 | "25 finite difference filters in image processing ... \n", 710 | "26 how is game theory and ai related \n", 711 | "\n", 712 | " Answers \n", 713 | "0 artificial intelligence is an area of computer... \n", 714 | "1 artificial intelligence neural networks can mo... \n", 715 | "2 strong ai makes strong claims that computers c... \n", 716 | "3 statistical ai is more concerned with inductiv... \n", 717 | "4 heuristic approach is the best way to go for g... \n", 718 | "5 artificial intelligence is a technique that en... \n", 719 | "6 the qlearning is a reinforcement learning algo... \n", 720 | "7 deep learning imitates the way our brain works... \n", 721 | "8 bayesian network is a statistical model that r... \n", 722 | "9 in artificial intelligence ai a turing test is... \n", 723 | "10 the rl agent works based on the theory of rewa... \n", 724 | "11 an important concept in reinforcement learning... \n", 725 | "12 hyperparameters are variables that define the ... \n", 726 | "13 overfitting occurs when a statistical model or... \n", 727 | "14 dropout is a type of regularization technique ... \n", 728 | "19 stemming algorithms work by cutting off the en... \n", 729 | "20 computer vision is a field of artificial intel... \n", 730 | "21 in supervised classification the images are ma... \n", 731 | "25 image smoothing is one of the best methods use... \n", 732 | "26 in the context of artificial intelligenceai an... " 733 | ] 734 | }, 735 | "execution_count": 11, 736 | "metadata": {}, 737 | "output_type": "execute_result" 738 | } 739 | ], 740 | "source": [ 741 | "df.head(20)" 742 | ] 743 | }, 744 | { 745 | "cell_type": "code", 746 | "execution_count": 12, 747 | "metadata": {}, 748 | "outputs": [], 749 | "source": [ 750 | "df.to_csv('data/normalized_dataset.csv', index=False)" 751 | ] 752 | }, 753 | { 754 | "cell_type": "code", 755 | "execution_count": null, 756 | "metadata": {}, 757 | "outputs": [], 758 | "source": [] 759 | } 760 | ], 761 | "metadata": { 762 | "kernelspec": { 763 | "display_name": "Python 3", 764 | "language": "python", 765 | "name": "python3" 766 | }, 767 | "language_info": { 768 | "codemirror_mode": { 769 | "name": "ipython", 770 | "version": 3 771 | }, 772 | "file_extension": ".py", 773 | "mimetype": "text/x-python", 774 | "name": "python", 775 | "nbconvert_exporter": "python", 776 | "pygments_lexer": "ipython3", 777 | "version": "3.10.5" 778 | }, 779 | "orig_nbformat": 4 780 | }, 781 | "nbformat": 4, 782 | "nbformat_minor": 2 783 | } 784 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | flask 3 | opencv-python 4 | deepface 5 | pandas 6 | spacy 7 | librosa 8 | soundfile 9 | SpeechRecognition 10 | -------------------------------------------------------------------------------- /server.py: -------------------------------------------------------------------------------- 1 | """ 2 | This module contains a Flask server implementation for handling text and video-based questions and answers. 3 | """ 4 | 5 | from __future__ import print_function # In python 2.7 6 | import numpy as np 7 | import pickle 8 | import os 9 | from flask import Flask,redirect,url_for,render_template,request,jsonify 10 | from flask import Response 11 | import time 12 | import os 13 | # !python -m spacy download en_core_web_md 14 | import spacy 15 | nlp = spacy.load("en_core_web_md") 16 | import cv2 17 | import subprocess 18 | from deepface import DeepFace 19 | import pandas as pd 20 | import json 21 | import sys 22 | from werkzeug.utils import secure_filename 23 | import librosa 24 | import soundfile as sf 25 | import speech_recognition as speechrecognizer 26 | import speech_recognition as sr 27 | 28 | import math 29 | 30 | 31 | 32 | app = Flask(__name__) 33 | df = pd.read_csv('data/normalized_dataset.csv') 34 | similarities = [] 35 | Questions_Arr = [] 36 | Correct_Answer_Arr = [] 37 | User_Answers = [] 38 | All_Video_Details = [] 39 | All_Text_Details = [] 40 | 41 | UPLOAD_FOLDER = 'uploads' 42 | ALLOWED_EXTENSIONS = {'ogg', 'wav', 'mp3'} 43 | app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER 44 | 45 | 46 | camera = cv2.VideoCapture(0) 47 | emotion_counts = { 48 | 'angry': 0, 49 | 'disgust': 0, 50 | 'fear': 0, 51 | 'happy': 0, 52 | 'sad': 0, 53 | 'surprise': 0, 54 | 'neutral': 0, 55 | 'no_face': 0, 56 | } 57 | faceCascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') 58 | 59 | def sentences_similarity(sentence1,sentence2): 60 | """ 61 | Calculate the similarity between two sentences using spaCy. 62 | """ 63 | doc1 = nlp(sentence1) 64 | doc2 = nlp(sentence2) 65 | similarity = doc1.similarity(doc2) 66 | return similarity 67 | 68 | 69 | @app.route('/') 70 | def index(): 71 | """ 72 | Render the main page. 73 | """ 74 | global similarities 75 | global Questions_Arr 76 | global Correct_Answer_Arr 77 | global User_Answers 78 | global All_Text_Details 79 | 80 | similarities = [] 81 | Questions_Arr = [] 82 | Correct_Answer_Arr = [] 83 | User_Answers = [] 84 | All_Text_Details = [] 85 | 86 | global emotion_counts 87 | global All_Video_Details 88 | 89 | 90 | All_Video_Details = [] 91 | emotion_counts = { 92 | 'angry': 0, 93 | 'disgust': 0, 94 | 'fear': 0, 95 | 'happy': 0, 96 | 'sad': 0, 97 | 'surprise': 0, 98 | 'neutral': 0, 99 | 'no_face': 0, 100 | } 101 | return render_template('Main_page.html') 102 | 103 | @app.route('/home') 104 | def home(): 105 | global similarities 106 | global Questions_Arr 107 | global Correct_Answer_Arr 108 | global User_Answers 109 | global All_Text_Details 110 | 111 | similarities = [] 112 | Questions_Arr = [] 113 | Correct_Answer_Arr = [] 114 | User_Answers = [] 115 | All_Text_Details = [] 116 | 117 | global emotion_counts 118 | global All_Video_Details 119 | 120 | 121 | All_Video_Details = [] 122 | emotion_counts = { 123 | 'angry': 0, 124 | 'disgust': 0, 125 | 'fear': 0, 126 | 'happy': 0, 127 | 'sad': 0, 128 | 'surprise': 0, 129 | 'neutral': 0, 130 | 'no_face': 0, 131 | } 132 | return render_template('Main_page.html') 133 | 134 | @app.route('/about') 135 | def about(): 136 | """ 137 | Render the about page. 138 | """ 139 | return render_template('Main_page.html') 140 | 141 | @app.route('/text_test_instructions') 142 | def text_test_instructions(): 143 | """ 144 | Render the instructions for the text test. 145 | """ 146 | return render_template('Instructions_text.html') 147 | 148 | @app.route('/video_test_instructions') 149 | def video_test_instructions(): 150 | """ 151 | Render the instructions for the video test. 152 | """ 153 | return render_template('Instructions_video.html') 154 | 155 | @app.route('/Text_Test') 156 | def Text_Test(): 157 | """ 158 | Render the text test page. 159 | """ 160 | return render_template('Text_Test.html') 161 | 162 | @app.route('/Text_Test_Results') 163 | def Text_Test_Results(): 164 | """ 165 | Render the text test results page. 166 | """ 167 | return render_template('Text_Test_Results.html') 168 | 169 | @app.route('/Video_Test') 170 | def Video_Test(): 171 | """ 172 | Render the video test page. 173 | """ 174 | return render_template('Video_Test.html') 175 | 176 | @app.route('/Video_Test_Results') 177 | def Video_Test_Results(): 178 | """ 179 | Render the video test results page. 180 | """ 181 | return render_template('Video_Test_Results.html') 182 | 183 | 184 | @app.route('/favicon.ico') 185 | def favicon(): 186 | """ 187 | Serve the favicon. 188 | """ 189 | return send_from_directory(os.path.join(app.root_path, 'static'),'favicon.ico', mimetype='image/vnd.microsoft.icon') 190 | 191 | @app.route('/Questions') 192 | def Text_Questions(): 193 | """ 194 | Get random questions from the dataset. 195 | """ 196 | global Questions_Arr 197 | global Correct_Answer_Arr 198 | random_rows = df.sample(n=10) 199 | Questions_Arr = random_rows['Questions'].tolist() 200 | Correct_Answer_Arr = random_rows['Answers'].tolist() 201 | return Questions_Arr 202 | 203 | @app.route('/Text_Answers/', methods=['POST']) 204 | def text_answers(Qindex): 205 | """ 206 | Receive and process user answers for text questions. 207 | """ 208 | global All_Text_Details 209 | global Questions_Arr 210 | global Correct_Answer_Arr 211 | 212 | answer = request.data.decode('utf-8') 213 | temp_list = [] 214 | temp_list.append(Questions_Arr[Qindex]) 215 | temp_list.append(Correct_Answer_Arr[Qindex]) 216 | temp_list.append(answer) 217 | temp_list.append(sentences_similarity(str(answer),str(Correct_Answer_Arr[Qindex]))) 218 | All_Text_Details.append(temp_list) 219 | return jsonify(answer) 220 | 221 | def allowed_file(filename): 222 | """ 223 | Check if the file extension is allowed. 224 | """ 225 | return '.' in filename and \ 226 | filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS 227 | 228 | @app.route('/upload-audio/', methods=['POST']) 229 | def upload_audio(Qindex): 230 | """ 231 | Handle audio file upload and process it. 232 | """ 233 | if 'file' not in request.files: 234 | return jsonify({'error': 'No file part'}), 400 235 | 236 | file = request.files['file'] 237 | if file.filename == '': 238 | return jsonify({'error': 'No selected file'}), 400 239 | 240 | if file and allowed_file(file.filename): 241 | filename = secure_filename(file.filename) 242 | file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename)) 243 | audio, speechrecognizer = librosa.load("uploads/audio"+str(Qindex)+".mp3") 244 | sf.write("uploads/audio"+str(Qindex)+".wav", audio, speechrecognizer) 245 | # open the audio file using the recognizer 246 | r = sr.Recognizer() 247 | with sr.AudioFile("uploads/audio"+str(Qindex)+".wav") as source: 248 | # listen to the file and store the audio data in a variable 249 | audio_data = r.record(source) 250 | # use the recognizer to convert speech to text 251 | text = r.recognize_google(audio_data) 252 | os.remove("uploads/audio"+str(Qindex)+".mp3") 253 | os.remove("uploads/audio"+str(Qindex)+".wav") 254 | global Questions_Arr 255 | global Correct_Answer_Arr 256 | global All_Video_Details 257 | Similarity = sentences_similarity(str(text), str(Correct_Answer_Arr[Qindex])) 258 | temp_list = [] 259 | temp_list.append(Questions_Arr[Qindex]) 260 | temp_list.append(Correct_Answer_Arr[Qindex]) 261 | temp_list.append(text) 262 | temp_list.append(Similarity) 263 | All_Video_Details.append(temp_list) 264 | return jsonify({'success': text}), 200 265 | else: 266 | return jsonify({'error': 'Invalid file type'}), 400 267 | 268 | 269 | @app.route('/video_results') 270 | def video_results(): 271 | temp_Emotion_Counts = emotion_counts 272 | temp_Results = All_Video_Details 273 | 274 | temp_Emotion_Counts['angry'] = emotion_counts['angry'] * 0.2 275 | temp_Emotion_Counts['disgust'] = emotion_counts['disgust'] * 0.2 276 | temp_Emotion_Counts['fear'] = emotion_counts['fear'] * 0.2 277 | temp_Emotion_Counts['happy'] = emotion_counts['happy'] * 1.3 278 | temp_Emotion_Counts['sad'] = emotion_counts['sad'] * 0.2 279 | temp_Emotion_Counts['neutral'] = emotion_counts['neutral'] * 1 280 | temp_Emotion_Counts['no_face'] = 0 281 | 282 | new_temp = [] 283 | new_temp.append(temp_Results) 284 | new_temp.append(list(temp_Emotion_Counts.values())) 285 | return jsonify(new_temp) 286 | 287 | 288 | @app.route('/text_results') 289 | def text_results(): 290 | # All_Text_Details_temp = [] 291 | All_Text_Details_temp = All_Text_Details 292 | return jsonify(All_Text_Details_temp) 293 | 294 | def generate_frames(): 295 | global camera 296 | while True: 297 | if camera is not None: 298 | success, frame = camera.read() 299 | if not success: 300 | break 301 | else: 302 | break 303 | 304 | ret, buffer = cv2.imencode('.jpg', frame) 305 | frame_copy = frame 306 | frame = buffer.tobytes() 307 | 308 | # convert to grayscale 309 | gray = cv2.cvtColor(frame_copy, cv2.COLOR_BGR2GRAY) 310 | 311 | # detect faces in the image 312 | faces = faceCascade.detectMultiScale( 313 | gray, 314 | scaleFactor=1.3, 315 | minNeighbors=5, 316 | minSize=(30, 30), 317 | flags=cv2.CASCADE_SCALE_IMAGE 318 | ) 319 | 320 | # loop over faces 321 | for (x, y, w, h) in faces: 322 | # extract face 323 | face = frame_copy[y:y+h, x:x+w] 324 | # recognize emotion if a face is detected 325 | if len(face) > 0: 326 | try: 327 | result = DeepFace.analyze(face, actions=['emotion'], enforce_detection=True) 328 | if result[0]['dominant_emotion'] is not None: 329 | emotion_counts[result[0]['dominant_emotion']] += 1 330 | else: 331 | no_count['no_emotion'] += 1 332 | except ValueError as err: 333 | emotion_counts['no_face'] += 1 334 | # update the no_face count if no face is detected 335 | else: 336 | emotion_counts['no_face'] += 1 337 | 338 | yield(b'--frame\r\n' 339 | b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n') 340 | 341 | 342 | @app.route('/videofeed') 343 | def video_feed(): 344 | return Response(generate_frames(), mimetype='multipart/x-mixed-replace; boundary=frame') 345 | 346 | 347 | @app.route('/start') 348 | def start(): 349 | global camera 350 | if camera is None: 351 | camera = cv2.VideoCapture(0) 352 | return 'Camera started' 353 | 354 | @app.route('/start_again') 355 | def start_again(): 356 | global camera 357 | if camera is None: 358 | camera = cv2.VideoCapture(0) 359 | while not camera.isOpened(): # Wait until the camera is ready 360 | pass 361 | return 'Camera started' 362 | 363 | @app.route('/stop') 364 | def stop(): 365 | global camera 366 | if camera is not None: 367 | camera.release() 368 | camera = None 369 | camera = cv2.VideoCapture(0) 370 | return 'Camera stopped' 371 | 372 | if __name__=='__main__': 373 | app.run(debug=True,port=5000,host='0.0.0.0') 374 | -------------------------------------------------------------------------------- /static/Mainpage_Background.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MuhammadMillwala/MockVue-Mock-Job-Interview-System/f2e68d9f1cae60cfd39802c2b9ce24f653a4a4ab/static/Mainpage_Background.png -------------------------------------------------------------------------------- /static/Mainpage_section1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MuhammadMillwala/MockVue-Mock-Job-Interview-System/f2e68d9f1cae60cfd39802c2b9ce24f653a4a4ab/static/Mainpage_section1.png 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font-family:arial; 9 | } 10 | 11 | div.section 12 | { 13 | /* padding: 60px 40px; */ 14 | min-height: 100vh; 15 | scroll-snap-align : start; 16 | margin: 0px; 17 | padding: 0px; 18 | } 19 | 20 | #section1{ 21 | /* background-color: #fad390; */ 22 | display: grid; 23 | background-image: url("../Mainpage_Background.png"); 24 | background-size: cover; 25 | background-position: center; 26 | margin: 0px; 27 | padding: 0px; 28 | } 29 | 30 | 31 | .navbar { 32 | display: flex; 33 | align-items: center; 34 | justify-content: space-between; 35 | height: 100px; 36 | padding: 0 4px; 37 | /* border-bottom: 1px solid #ccc; */ 38 | background-color: #f4efe9; 39 | position: absolute; 40 | } 41 | 42 | .logo { 43 | flex-shrink: 0; 44 | } 45 | 46 | .logo img { 47 | height: 90px; 48 | } 49 | 50 | .links { 51 | display: flex; 52 | align-items: center; 53 | height: 100%; 54 | } 55 | 56 | .links a { 57 | margin: 0 20px; 58 | color: #5d5e70; 59 | text-decoration: none; 60 | font-family: "LEMON MILK"; 61 | font-weight: 100; 62 | font-size: 3rem; 63 | } 64 | 65 | .links a:hover { 66 | color: #954f3a; 67 | } 68 | 69 | .links a.active { 70 | font-weight: 400; 71 | } 72 | 73 | .links::before { 74 | content: ""; 75 | display: block; 76 | height: 100%; 77 | width: 1px; 78 | margin-right: 20px; 79 | } 80 | 81 | .rectangle { 82 | width: 1200px; 83 | height: 5px; 84 | background-color: #5d5e70; 85 | margin: 20px; 86 | } 87 | 88 | .btn { 89 | display: inline-block; 90 | padding: 10px 20px; 91 | border-radius: 50px; 92 | border: 4px solid #954f3a; 93 | color: #954f3a; 94 | background: transparent; 95 | font-family: "LEMON MILK"; 96 | font-size: 2rem; 97 | font-weight:lighter; 98 | text-align: center; 99 | text-decoration: none; 100 | transition: all 0.3s ease; 101 | margin: 20px; 102 | width: 150px; /* set width of buttons */ 103 | height: 43px; 104 | } 105 | .btn:hover { 106 | background: #954f3a; 107 | color: #fff; 108 | border-color: #954f3a; 109 | } 110 | 111 | 112 | #section1{ 113 | padding-top: 100px; 114 | text-align: center; 115 | } 116 | 117 | .section_title{ 118 | color: #954f3a; 119 | font-family: "LEMON MILK"; 120 | font-weight: bold; 121 | font-size: 6rem; 122 | margin-bottom: -20px; 123 | } 124 | 125 | .section_text{ 126 | padding-top: 20px; 127 | color: #f4efe9; 128 | font-family: "LEMON MILK"; 129 | font-style: normal; 130 | font-size: 1.05rem; 131 | text-align: left; 132 | padding-left: 30.3%; 133 | } 134 | 135 | .hidden_ins{ 136 | opacity: 0; 137 | filter: blur(5px); 138 | transition: all 1s; 139 | } 140 | 141 | .show_ins{ 142 | opacity: 1; 143 | filter: blur(0); 144 | } -------------------------------------------------------------------------------- /static/css/Main_page.css: -------------------------------------------------------------------------------- 1 | html{ 2 | scroll-snap-type : y mandatory; 3 | font-size: 100%; 4 | } 5 | 6 | body { 7 | margin: 0; 8 | font-family:arial; 9 | } 10 | 11 | div.section 12 | { 13 | /* padding: 60px 40px; */ 14 | min-height: 100vh; 15 | scroll-snap-align : start; 16 | margin: 0px; 17 | padding: 0px; 18 | } 19 | /* div.section h2 20 | { 21 | font-size: 30px; 22 | text-align: center; 23 | } */ 24 | #section1{ 25 | /* background-color: #fad390; */ 26 | display: grid; 27 | background-image: url("../Mainpage_Background.png"); 28 | background-size: cover; 29 | background-position: center; 30 | margin: 0px; 31 | padding: 0px; 32 | } 33 | #section2{ 34 | display: grid; 35 | background-image: url("../Mainpage_Background.png"); 36 | background-size: cover; 37 | background-position: center; 38 | } 39 | #section3{ 40 | display: grid; 41 | background-image: url("../Mainpage_Background.png"); 42 | background-size: cover; 43 | background-position: center; 44 | } 45 | #section4{ 46 | background-image: url("../Mainpage_Background.png"); 47 | background-size: cover; 48 | background-position: center; 49 | } 50 | 51 | .navbar { 52 | display: flex; 53 | align-items: center; 54 | justify-content: space-between; 55 | height: 100px; 56 | padding: 0 4px; 57 | /* border-bottom: 1px solid #ccc; */ 58 | background-color: #f4efe9; 59 | position: absolute; 60 | } 61 | 62 | .logo { 63 | flex-shrink: 0; 64 | } 65 | 66 | .logo img { 67 | height: 90px; 68 | } 69 | 70 | .links { 71 | display: flex; 72 | align-items: center; 73 | height: 100%; 74 | } 75 | 76 | .links a { 77 | margin: 0 20px; 78 | color: #5d5e70; 79 | text-decoration: none; 80 | font-family: "LEMON MILK"; 81 | font-weight: 100; 82 | font-size: 3rem; 83 | } 84 | 85 | .links a:hover { 86 | color: #954f3a; 87 | } 88 | 89 | .links a.active { 90 | font-weight: 400; 91 | } 92 | 93 | .links::before { 94 | content: ""; 95 | display: block; 96 | height: 100%; 97 | width: 1px; 98 | margin-right: 20px; 99 | } 100 | 101 | .rectangle { 102 | width: 1200px; 103 | height: 5px; 104 | background-color: #5d5e70; 105 | margin: 20px; 106 | } 107 | 108 | #section1{ 109 | padding-top: 100px; 110 | } 111 | 112 | #section1_image img{ 113 | height: 700px; 114 | padding-right: 250px; 115 | padding-top: 50px; 116 | } 117 | 118 | 119 | #section1_text p{ 120 | text-align:center; 121 | margin-left: 250px; 122 | } 123 | .section1_title{ 124 | padding-top: 40px; 125 | color: #f4efe9; 126 | font-family: "LEMON MILK"; 127 | font-weight: bold; 128 | font-size: 6rem; 129 | padding-left: 50px; 130 | padding-bottom: 0px; 131 | margin-bottom: 10px; 132 | margin-top: 100px; 133 | } 134 | .section1_tagline{ 135 | color: #954f3a; 136 | font-family: "LEMON MILK"; 137 | font-size: 3.2rem; 138 | padding-left: 50px; 139 | margin-top: -30px; 140 | margin-bottom: 10px; 141 | } 142 | 143 | .section1_text{ 144 | color: #f4efe9; 145 | font-family: "LEMON MILK"; 146 | font-style: normal; 147 | font-size: 1.05rem; 148 | padding-left: 50px; 149 | } 150 | 151 | 152 | #mainpage_buttons{ 153 | padding-top: 0px; 154 | padding-left: 330px; 155 | } 156 | .btn { 157 | display: inline-block; 158 | padding: 10px 20px; 159 | border-radius: 50px; 160 | border: 4px solid #954f3a; 161 | color: #954f3a; 162 | background: transparent; 163 | font-family: "LEMON MILK"; 164 | font-size: 2rem; 165 | font-weight:lighter; 166 | text-align: center; 167 | text-decoration: none; 168 | transition: all 0.3s ease; 169 | margin: 20px; 170 | width: 150px; /* set width of buttons */ 171 | height: 43px; 172 | } 173 | .btn:hover { 174 | background: #954f3a; 175 | color: #fff; 176 | border-color: #954f3a; 177 | } 178 | 179 | 180 | .section2_text{ 181 | text-align: center; 182 | } 183 | 184 | 185 | .section2n3_title{ 186 | align-self: center; 187 | color: #f4efe9; 188 | font-family: "LEMON MILK"; 189 | font-weight: bold; 190 | font-size: 6rem; 191 | padding-top: 50px; 192 | margin-bottom: -70px; 193 | } 194 | 195 | .section2n3_tagline{ 196 | color: #954f3a; 197 | font-family: "LEMON MILK"; 198 | font-size: 3.2rem; 199 | margin-bottom: -10px; 200 | } 201 | .section2n3_text{ 202 | color: #f4efe9; 203 | font-family: "LEMON MILK"; 204 | font-style: normal; 205 | font-size: 1.05rem; 206 | } 207 | 208 | .section2imgs{ 209 | margin-top: -100px; 210 | height: 100px; 211 | display: flex; 212 | justify-content: center; 213 | } 214 | 215 | .section2imgs img{ 216 | margin-left: 100px; 217 | margin-right: 100px; 218 | } 219 | 220 | #section2_underimage_text{ 221 | display: flex; 222 | color: #f4efe9; 223 | font-family: "LEMON MILK"; 224 | font-size: 1.5rem; 225 | justify-content: center; 226 | } 227 | 228 | #section2_underimage_text{ 229 | margin-left: 20px; 230 | margin-top: -150px; 231 | } 232 | 233 | #section2_underimage_text p:nth-child(1){ 234 | margin-right: 230px; 235 | } 236 | 237 | #section2_underimage_text p:nth-child(2){ 238 | margin-left: -20px; 239 | margin-right: 130px; 240 | } 241 | 242 | #section2_underimage_text p:nth-child(3){ 243 | margin-left: 90px; 244 | margin-right: 50px; 245 | } 246 | 247 | .section2n3_title{ 248 | padding-top: 80px; 249 | } 250 | 251 | #section3_text{ 252 | text-align: center; 253 | } 254 | 255 | .section3imgs{ 256 | margin-top: -180px; 257 | justify-content: center; 258 | height: 400px; 259 | filter: drop-shadow(0px 0px 10px rgba(0, 0, 0, 1)); 260 | transition: heigh 0.5s ease; 261 | } 262 | 263 | .section3imgs img:nth-child(1):hover{ 264 | height: 450px; 265 | } 266 | 267 | .section3imgs img:nth-child(2):hover{ 268 | height: 450px; 269 | } 270 | 271 | .section3imgs img:nth-child(3):hover{ 272 | height: 450px; 273 | } 274 | 275 | .section4_left{ 276 | padding-top: 5%; 277 | padding-left: 70px; 278 | justify-content:center; 279 | height: 700px; 280 | } 281 | 282 | .section4_right{ 283 | color: #f4efe9; 284 | font-family: "LEMON MILK"; 285 | font-weight: bold; 286 | font-size: 10rem; 287 | } 288 | 289 | .section4_right p{ 290 | margin-bottom: -200px; 291 | } 292 | 293 | .section4_left img{ 294 | filter: brightness(70%) grayscale(100%); 295 | transition: filter 0.5s ease; 296 | } 297 | 298 | .section4_left img:hover { 299 | filter: brightness(100%); 300 | transform:scale(1.1); 301 | } 302 | 303 | 304 | 305 | .hidden p{ 306 | opacity: 0; 307 | filter: blur(5px); 308 | transform: translateX(-100%); 309 | transition: all 1s; 310 | } 311 | 312 | .show p{ 313 | opacity: 1; 314 | filter: blur(0); 315 | transform: translateX(0); 316 | } 317 | 318 | .hidden a{ 319 | opacity: 0; 320 | filter: blur(5px); 321 | transform: translateY(-50%); 322 | transition: all 1s; 323 | } 324 | 325 | .show a{ 326 | opacity: 1; 327 | filter: blur(0); 328 | transform: translateY(0); 329 | } 330 | 331 | 332 | .hidden img{ 333 | opacity: 0; 334 | transform: translateY(60%); 335 | transition: all 1s; 336 | } 337 | 338 | .show img{ 339 | opacity: 1; 340 | transform: translateX(0); 341 | } 342 | 343 | 344 | #section1 :nth-child(1){ 345 | transition-delay: 100ms; 346 | } 347 | #section1 :nth-child(2){ 348 | transition-delay: 200ms; 349 | } 350 | #section1 :nth-child(3){ 351 | transition-delay: 300ms; 352 | } 353 | #section1 :nth-child(4){ 354 | transition-delay: 400ms; 355 | } 356 | #section1 :nth-child(5){ 357 | transition-delay: 500ms; 358 | } 359 | #section1 :nth-child(6){ 360 | transition-delay: 600ms; 361 | } 362 | #section1 :nth-child(7){ 363 | transition-delay: 700ms; 364 | } 365 | #section1 :nth-child(8){ 366 | transition-delay: 800ms; 367 | } 368 | 369 | #section1 :nth-child(9){ 370 | transition-delay: 1000ms; 371 | } 372 | 373 | 374 | 375 | #section2 :nth-child(1){ 376 | transition-delay: 100ms; 377 | } 378 | #section2 :nth-child(2){ 379 | transition-delay: 200ms; 380 | } 381 | #section2 :nth-child(3){ 382 | transition-delay: 300ms; 383 | } 384 | #section2 :nth-child(4){ 385 | transition-delay: 400ms; 386 | } 387 | #section2 :nth-child(5){ 388 | transition-delay: 500ms; 389 | } 390 | #section2 :nth-child(6){ 391 | transition-delay: 600ms; 392 | } 393 | #section2 :nth-child(7){ 394 | transition-delay: 700ms; 395 | } 396 | #section2 :nth-child(8){ 397 | transition-delay: 800ms; 398 | } 399 | 400 | #section2 :nth-child(9){ 401 | transition-delay: 1000ms; 402 | } 403 | 404 | #section2 :nth-child(10){ 405 | transition-delay: 1400ms; 406 | } 407 | 408 | #section2 :nth-child(11){ 409 | transition-delay: 1600ms; 410 | } 411 | 412 | 413 | 414 | #section3 :nth-child(1){ 415 | transition-delay: 500ms; 416 | } 417 | #section3 :nth-child(2){ 418 | transition-delay: 600ms; 419 | } 420 | #section3 :nth-child(3){ 421 | transition-delay: 700ms; 422 | } 423 | 424 | #section4 :nth-child(1){ 425 | transition-delay: 100ms; 426 | } 427 | 428 | #section4 :nth-child(2){ 429 | transition-delay: 200ms; 430 | } 431 | 432 | #section4 :nth-child(3){ 433 | transition-delay: 300ms; 434 | } 435 | 436 | #section4 :nth-child(4){ 437 | transition-delay: 400ms; 438 | } 439 | -------------------------------------------------------------------------------- /static/css/Text_Test.css: -------------------------------------------------------------------------------- 1 | html{ 2 | scroll-snap-type : y mandatory; 3 | font-size: 100%; 4 | } 5 | 6 | body { 7 | margin: 0; 8 | font-family:arial; 9 | } 10 | 11 | div.section 12 | { 13 | /* padding: 60px 40px; */ 14 | min-height: 100vh; 15 | scroll-snap-align : start; 16 | margin: 0px; 17 | padding: 0px; 18 | } 19 | 20 | #section1{ 21 | /* background-color: #fad390; */ 22 | display: grid; 23 | background-image: url("../Mainpage_Background.png"); 24 | background-size: cover; 25 | background-position: center; 26 | margin: 0px; 27 | padding: 0px; 28 | } 29 | 30 | 31 | .navbar { 32 | display: flex; 33 | align-items: center; 34 | justify-content: space-between; 35 | height: 100px; 36 | width: 100%; 37 | /* border-bottom: 1px solid #ccc; */ 38 | background-color: #f4efe9; 39 | position: absolute; 40 | } 41 | 42 | .logo { 43 | flex-shrink: 0; 44 | } 45 | 46 | .logo img { 47 | height: 90px; 48 | } 49 | 50 | .links { 51 | display: flex; 52 | align-items: center; 53 | height: 100%; 54 | } 55 | 56 | .links a { 57 | margin: 0 20px; 58 | color: #5d5e70; 59 | text-decoration: none; 60 | font-family: "LEMON MILK"; 61 | font-weight: 100; 62 | font-size: 3rem; 63 | } 64 | 65 | .links a:hover { 66 | color: #954f3a; 67 | } 68 | 69 | .links a.active { 70 | font-weight: 400; 71 | } 72 | 73 | .links::before { 74 | content: ""; 75 | display: block; 76 | height: 100%; 77 | width: 1px; 78 | margin-right: 20px; 79 | } 80 | 81 | .rectangle { 82 | width: 1403px; 83 | height: 5px; 84 | background-color: #5d5e70; 85 | margin: 20px; 86 | } 87 | 88 | 89 | #section1{ 90 | padding-top: 100px; 91 | text-align: center; 92 | } 93 | 94 | 95 | .container { 96 | display: flex; 97 | flex-direction: column; 98 | justify-content: center; 99 | height: 100vh; 100 | } 101 | 102 | .question-box { 103 | display: flex; 104 | flex-direction: column; 105 | align-items: center; 106 | justify-content: center; 107 | margin-bottom: 20px; 108 | } 109 | 110 | .timer-box { 111 | position: absolute; 112 | margin-top: -925px; 113 | width: 100%; 114 | height: 20px; 115 | background-color: lightgray; 116 | } 117 | 118 | .timer { 119 | height: 100%; 120 | background-color: #954f3a; 121 | } 122 | 123 | #question-text{ 124 | margin-top: -230px; 125 | color: #f4efe9; 126 | font-family: "LEMON MILK"; 127 | font-weight: bold; 128 | font-size: 5rem; 129 | } 130 | 131 | .input-box { 132 | position: absolute; 133 | bottom: 0; 134 | left: 50%; 135 | transform: translateX(-50%); 136 | display: flex; 137 | justify-content: space-between; 138 | align-items: center; 139 | width: 80%; 140 | height: 100px; 141 | background-color: transparent; 142 | border: 5px solid #954f3a; 143 | border-radius: 10px 10px 0 0; 144 | } 145 | 146 | .input-box input { 147 | width: calc(100% - 80px); 148 | height: 79%; 149 | padding: 10px; 150 | border: none; 151 | border-radius: 7px 0 0 0; 152 | font-size: 16px; 153 | } 154 | 155 | .input-box button { 156 | width: 80px; 157 | height: 100%; 158 | padding: 10px; 159 | background-color: transparent; 160 | border: 2px solid #954f3a; 161 | border-left: none; 162 | cursor: pointer; 163 | transition: background-color 0.3s ease; 164 | } 165 | 166 | .input-box button:hover { 167 | background-color: #954f3a; 168 | } 169 | 170 | .input-box button svg { 171 | display: block; 172 | margin: 0 auto; 173 | width: 20px; 174 | height: 20px; 175 | } -------------------------------------------------------------------------------- /static/css/Text_Test_Results.css: -------------------------------------------------------------------------------- 1 | html{ 2 | scroll-snap-type : y mandatory; 3 | font-size: 100%; 4 | } 5 | 6 | body { 7 | margin: 0; 8 | font-family:arial; 9 | } 10 | 11 | div.section 12 | { 13 | /* padding: 60px 40px; */ 14 | min-height: 100vh; 15 | scroll-snap-align : start; 16 | margin: 0px; 17 | padding: 0px; 18 | } 19 | 20 | #section1{ 21 | /* background-color: #fad390; */ 22 | display: grid; 23 | background-image: url("../Mainpage_Background.png"); 24 | background-size: cover; 25 | background-position: center; 26 | margin: 0px; 27 | padding: 0px; 28 | } 29 | 30 | 31 | .navbar { 32 | display: flex; 33 | align-items: center; 34 | justify-content: space-between; 35 | height: 100px; 36 | width: 100%; 37 | /* border-bottom: 1px solid #ccc; */ 38 | background-color: #f4efe9; 39 | position: absolute; 40 | } 41 | 42 | .logo { 43 | flex-shrink: 0; 44 | } 45 | 46 | .logo img { 47 | height: 90px; 48 | } 49 | 50 | .links { 51 | display: flex; 52 | align-items: center; 53 | height: 100%; 54 | } 55 | 56 | .links a { 57 | margin: 0 20px; 58 | color: #5d5e70; 59 | text-decoration: none; 60 | font-family: "LEMON MILK"; 61 | font-weight: 100; 62 | font-size: 3rem; 63 | } 64 | 65 | .links a:hover { 66 | color: #954f3a; 67 | } 68 | 69 | .links a.active { 70 | font-weight: 400; 71 | } 72 | 73 | .links::before { 74 | content: ""; 75 | display: block; 76 | height: 100%; 77 | width: 1px; 78 | margin-right: 20px; 79 | } 80 | 81 | .rectangle { 82 | width: 1403px; 83 | height: 5px; 84 | background-color: #5d5e70; 85 | margin: 20px; 86 | } 87 | 88 | 89 | #section1{ 90 | padding-top: 100px; 91 | text-align: center; 92 | } 93 | 94 | 95 | 96 | .blue { color: #185875; } 97 | .yellow { color: #FFF842; } 98 | 99 | .container th h1 { 100 | font-weight: bold; 101 | font-size: 1em; 102 | text-align: left; 103 | color: #185875; 104 | } 105 | 106 | .container td { 107 | font-weight: normal; 108 | font-size: 1em; 109 | -webkit-box-shadow: 0 2px 2px -2px #0E1119; 110 | -moz-box-shadow: 0 2px 2px -2px #0E1119; 111 | box-shadow: 0 2px 2px -2px #0E1119; 112 | } 113 | 114 | .container { 115 | text-align: center; 116 | overflow: hidden; 117 | width: 80%; 118 | margin: auto; 119 | display: table; 120 | border-radius: 40px; 121 | 122 | } 123 | 124 | .container td, .container th { 125 | padding-bottom: 2%; 126 | padding-top: 2%; 127 | padding-left:2%; 128 | padding-right: 2% ; 129 | } 130 | 131 | /* Background-color of the odd rows */ 132 | .container tr:nth-child(odd) { 133 | text-align: center; 134 | color: #9f9f9f; 135 | background-color: #323c502b; 136 | } 137 | 138 | /* Background-color of the even rows */ 139 | .container tr:nth-child(even) { 140 | text-align: center; 141 | color: #9f9f9f; 142 | background-color: #323c502b; 143 | } 144 | 145 | .container th { 146 | background-color: #98989881; 147 | text-align: center; 148 | } 149 | 150 | .container td:first-child { color: #9f9f9f; } 151 | 152 | .container tr:hover { 153 | color: #f4efe9; 154 | background-color: #343434; 155 | -webkit-box-shadow: 0 6px 6px -6px #0E1119; 156 | -moz-box-shadow: 0 6px 6px -6px #0E1119; 157 | box-shadow: 0 6px 6px -6px #0E1119; 158 | } 159 | 160 | .container td:hover { 161 | background-color: #575757; 162 | color: #ffffff; 163 | /* font-weight: bold; */ 164 | 165 | /* box-shadow: #7F7C21 -1px 1px, #7F7C21 -2px 2px, #7F7C21 -3px 3px, #7F7C21 -4px 4px, #7F7C21 -5px 5px, #7F7C21 -6px 6px; */ 166 | /* transform: translate3d(6px, -6px, 0); */ 167 | 168 | transition-delay: 0s; 169 | transition-duration: 0.4s; 170 | transition-property: all; 171 | transition-timing-function: line; 172 | } 173 | 174 | @media (max-width: 800px) { 175 | .container td:nth-child(4), 176 | .container th:nth-child(4) { display: none; } 177 | } -------------------------------------------------------------------------------- /static/css/Video_Test.css: -------------------------------------------------------------------------------- 1 | html{ 2 | scroll-snap-type : y mandatory; 3 | font-size: 100%; 4 | } 5 | 6 | body { 7 | margin: 0; 8 | font-family:arial; 9 | } 10 | 11 | div.section 12 | { 13 | /* padding: 60px 40px; */ 14 | min-height: 100vh; 15 | scroll-snap-align : start; 16 | margin: 0px; 17 | padding: 0px; 18 | } 19 | 20 | #section1{ 21 | /* background-color: #fad390; */ 22 | display: grid; 23 | background-image: url("../Mainpage_Background.png"); 24 | background-size: cover; 25 | background-position: center; 26 | margin: 0px; 27 | padding: 0px; 28 | } 29 | 30 | 31 | .navbar { 32 | display: flex; 33 | align-items: center; 34 | justify-content: space-between; 35 | height: 100px; 36 | width: 100%; 37 | /* border-bottom: 1px solid #ccc; */ 38 | background-color: #f4efe9; 39 | position: absolute; 40 | } 41 | 42 | .logo { 43 | flex-shrink: 0; 44 | } 45 | 46 | .logo img { 47 | height: 90px; 48 | } 49 | 50 | .links { 51 | display: flex; 52 | align-items: center; 53 | height: 100%; 54 | } 55 | 56 | .links a { 57 | margin: 0 20px; 58 | color: #5d5e70; 59 | text-decoration: none; 60 | font-family: "LEMON MILK"; 61 | font-weight: 100; 62 | font-size: 3rem; 63 | } 64 | 65 | .links a:hover { 66 | color: #954f3a; 67 | } 68 | 69 | .links a.active { 70 | font-weight: 400; 71 | } 72 | 73 | .links::before { 74 | content: ""; 75 | display: block; 76 | height: 100%; 77 | width: 1px; 78 | margin-right: 20px; 79 | } 80 | 81 | .rectangle { 82 | width: 1403px; 83 | height: 5px; 84 | background-color: #5d5e70; 85 | margin: 20px; 86 | } 87 | 88 | 89 | #section1{ 90 | padding-top: 100px; 91 | text-align: center; 92 | } 93 | 94 | 95 | #timer-container { 96 | margin-top: 0px; 97 | width: 100%; 98 | height: 20px; 99 | background-color: lightgray; 100 | } 101 | 102 | #timer { 103 | height: 100%; 104 | background-color: #954f3a; 105 | } 106 | 107 | 108 | #question{ 109 | color: #f4efe9; 110 | font-family: "LEMON MILK"; 111 | font-weight: bold; 112 | font-size: 2rem; 113 | } 114 | 115 | 116 | #video_buttons{ 117 | padding-top: 0px; 118 | padding-left: 0px; 119 | } 120 | 121 | .btn { 122 | display: inline-block; 123 | padding: 10px 20px; 124 | border-radius: 50px; 125 | border: 4px solid #954f3a; 126 | color: #954f3a; 127 | background: transparent; 128 | font-family: "LEMON MILK"; 129 | font-size: 1rem; 130 | font-weight:lighter; 131 | text-align: center; 132 | text-decoration: none; 133 | transition: all 0.3s ease; 134 | margin: 20px; 135 | width: 150px; /* set width of buttons */ 136 | height: 80px; 137 | } 138 | .btn:hover { 139 | background: #954f3a; 140 | color: #fff; 141 | border-color: #954f3a; 142 | } 143 | 144 | 145 | img { 146 | display: block; 147 | margin: auto; 148 | } 149 | -------------------------------------------------------------------------------- /static/css/Video_Test_Results.css: -------------------------------------------------------------------------------- 1 | html{ 2 | /* scroll-snap-type : y mandatory; */ 3 | font-size: 100%; 4 | } 5 | 6 | body { 7 | margin: 0; 8 | font-family:arial; 9 | } 10 | 11 | div.section 12 | { 13 | /* padding: 60px 40px; */ 14 | min-height: 100vh; 15 | scroll-snap-align : start; 16 | margin: 0px; 17 | padding: 0px; 18 | } 19 | 20 | #section1{ 21 | /* background-color: #fad390; */ 22 | display: grid; 23 | background-image: url("../Mainpage_Background.png"); 24 | background-size: cover; 25 | background-position: center; 26 | margin: 0px; 27 | padding: 0px; 28 | } 29 | 30 | 31 | .navbar { 32 | display: flex; 33 | align-items: center; 34 | justify-content: space-between; 35 | height: 100px; 36 | width: 100%; 37 | /* border-bottom: 1px solid #ccc; */ 38 | background-color: #f4efe9; 39 | position: absolute; 40 | } 41 | 42 | .logo { 43 | flex-shrink: 0; 44 | } 45 | 46 | .logo img { 47 | height: 90px; 48 | } 49 | 50 | .links { 51 | display: flex; 52 | align-items: center; 53 | height: 100%; 54 | } 55 | 56 | .links a { 57 | margin: 0 20px; 58 | color: #5d5e70; 59 | text-decoration: none; 60 | font-family: "LEMON MILK"; 61 | font-weight: 100; 62 | font-size: 3rem; 63 | } 64 | 65 | .links a:hover { 66 | color: #954f3a; 67 | } 68 | 69 | .links a.active { 70 | font-weight: 400; 71 | } 72 | 73 | .links::before { 74 | content: ""; 75 | display: block; 76 | height: 100%; 77 | width: 1px; 78 | margin-right: 20px; 79 | } 80 | 81 | .rectangle { 82 | width: 1403px; 83 | height: 5px; 84 | background-color: #5d5e70; 85 | margin: 20px; 86 | } 87 | 88 | 89 | #section1{ 90 | padding-top: 100px; 91 | text-align: center; 92 | } 93 | 94 | #chart_div{ 95 | height:300px; 96 | padding-bottom: 20px; 97 | display: flex; 98 | justify-content: center; 99 | align-items: center; 100 | } 101 | 102 | #myChart { 103 | margin: auto; 104 | } 105 | 106 | 107 | .blue { color: #185875; } 108 | .yellow { color: #FFF842; } 109 | 110 | .container th h1 { 111 | font-weight: bold; 112 | font-size: 1em; 113 | text-align: left; 114 | color: #185875; 115 | } 116 | 117 | .container td { 118 | font-weight: normal; 119 | font-size: 1em; 120 | -webkit-box-shadow: 0 2px 2px -2px #0E1119; 121 | -moz-box-shadow: 0 2px 2px -2px #0E1119; 122 | box-shadow: 0 2px 2px -2px #0E1119; 123 | } 124 | 125 | .container { 126 | text-align: center; 127 | overflow: hidden; 128 | width: 80%; 129 | margin: auto; 130 | display: table; 131 | border-radius: 40px; 132 | 133 | } 134 | 135 | .container td, .container th { 136 | padding-bottom: 2%; 137 | padding-top: 2%; 138 | padding-left:2%; 139 | padding-right: 2% ; 140 | } 141 | 142 | /* Background-color of the odd rows */ 143 | .container tr:nth-child(odd) { 144 | text-align: center; 145 | background-color: #323c502b; 146 | } 147 | 148 | /* Background-color of the even rows */ 149 | .container tr:nth-child(even) { 150 | text-align: center; 151 | background-color: #323c502b; 152 | } 153 | 154 | .container th { 155 | background-color: #98989881; 156 | text-align: center; 157 | } 158 | 159 | .container td:first-child { color: #000000; } 160 | 161 | .container tr:hover { 162 | background-color: #343434; 163 | -webkit-box-shadow: 0 6px 6px -6px #0E1119; 164 | -moz-box-shadow: 0 6px 6px -6px #0E1119; 165 | box-shadow: 0 6px 6px -6px #0E1119; 166 | } 167 | 168 | .container td:hover { 169 | background-color: #575757; 170 | color: #ffffff; 171 | /* font-weight: bold; */ 172 | 173 | /* box-shadow: #7F7C21 -1px 1px, #7F7C21 -2px 2px, #7F7C21 -3px 3px, #7F7C21 -4px 4px, #7F7C21 -5px 5px, #7F7C21 -6px 6px; */ 174 | transform: translate3d(6px, -6px, 0); 175 | 176 | transition-delay: 0s; 177 | transition-duration: 0.4s; 178 | transition-property: all; 179 | transition-timing-function: line; 180 | } 181 | 182 | @media (max-width: 800px) { 183 | .container td:nth-child(4), 184 | .container th:nth-child(4) { display: none; } 185 | } -------------------------------------------------------------------------------- /static/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MuhammadMillwala/MockVue-Mock-Job-Interview-System/f2e68d9f1cae60cfd39802c2b9ce24f653a4a4ab/static/favicon.ico -------------------------------------------------------------------------------- /static/script/Instructions.js: -------------------------------------------------------------------------------- 1 | const observer = new IntersectionObserver((entries) => { 2 | entries.forEach((entry) => { 3 | console.log(entry) 4 | if (entry.isIntersecting){ 5 | entry.target.classList.add('show_ins'); 6 | } 7 | else{ 8 | entry.target.classList.remove('show_ins'); 9 | } 10 | }); 11 | }); 12 | 13 | const hiddenElements = document.querySelectorAll('.hidden_ins'); 14 | hiddenElements.forEach((el) => observer.observe(el)); -------------------------------------------------------------------------------- /static/script/Main_page.js: -------------------------------------------------------------------------------- 1 | const observer = new IntersectionObserver((entries) => { 2 | entries.forEach((entry) => { 3 | console.log(entry) 4 | if (entry.isIntersecting){ 5 | entry.target.classList.add('show'); 6 | } 7 | else{ 8 | entry.target.classList.remove('show'); 9 | } 10 | }); 11 | }); 12 | 13 | const hiddenElements = document.querySelectorAll('.hidden'); 14 | hiddenElements.forEach((el) => observer.observe(el)); -------------------------------------------------------------------------------- /static/script/Text_Test.js: -------------------------------------------------------------------------------- 1 | let questions = []; 2 | let userAnswers = []; 3 | let currentQuestionIndex = 0; 4 | let timeLeft = 60; 5 | let timerInterval; 6 | const questionText = document.getElementById('question-text'); 7 | const answerInput = document.getElementById('answer-input'); 8 | const submitBtn = document.getElementById('submit-btn'); 9 | const timer = document.querySelector('.timer'); 10 | 11 | let Question_number = 0; 12 | // Fetch questions from server 13 | fetch('Questions') 14 | .then(response => response.json()) 15 | .then(data => { 16 | questions = data; 17 | console.log(questions ) 18 | resetTimer(); 19 | startTimer(); 20 | showQuestion(); 21 | }) 22 | .catch(error => console.error(error)); 23 | 24 | // Start the timer 25 | function startTimer() { 26 | if (timerInterval) { 27 | clearInterval(timerInterval); 28 | } 29 | 30 | timerInterval = setInterval(() => { 31 | timeLeft--; 32 | timer.style.width = `${(timeLeft / 60) * 100}%`; 33 | if (timeLeft <= 0) { 34 | clearInterval(timerInterval); 35 | userAnswers.push('timeout'); 36 | checkAnswer() 37 | // currentQuestionIndex++; 38 | // if (currentQuestionIndex < questions.length) { 39 | // resetTimer(); 40 | // startTimer(); 41 | // showQuestion(); 42 | // } else { 43 | // window.location.href = "/Text_Test_Results"; 44 | // } 45 | } 46 | }, 1000); 47 | } 48 | 49 | // Reset the timer 50 | function resetTimer() { 51 | timeLeft = 60; 52 | timer.style.width = '100%'; 53 | } 54 | 55 | // Handle form submission 56 | submitBtn.addEventListener('click', (e) => { 57 | e.preventDefault(); 58 | checkAnswer(); 59 | }); 60 | 61 | answerInput.addEventListener('keyup', (e) => { 62 | if (e.key === 'Enter') { 63 | checkAnswer(); 64 | } 65 | }); 66 | 67 | function showQuestion() { 68 | questionText.innerText = questions[currentQuestionIndex]; 69 | answerInput.value = ''; 70 | } 71 | 72 | function checkAnswer() { 73 | const userAnswer = answerInput.value; 74 | answerInput.value = ''; // Clear the answer input field 75 | currentQuestionIndex++; 76 | 77 | // Send the user's answer to the server 78 | fetch('Text_Answers/' + Question_number, { 79 | method: 'POST', 80 | headers: { 81 | 'Content-Type': 'text/plain' 82 | }, 83 | body: userAnswer 84 | }) 85 | .then(response => { 86 | if (response.ok) { 87 | 88 | console.log(response); 89 | Question_number += 1; 90 | } else { 91 | console.error('Failed to submit answer.'); 92 | } 93 | 94 | // Check if there are more questions to show 95 | if (currentQuestionIndex < questions.length) { 96 | resetTimer(); 97 | startTimer(); 98 | showQuestion(); 99 | } else { 100 | window.location.href = "/Text_Test_Results"; 101 | } 102 | }) 103 | .catch(error => console.error(error)); 104 | } 105 | 106 | -------------------------------------------------------------------------------- /static/script/Text_Test_Results.js: -------------------------------------------------------------------------------- 1 | fetch('text_results') 2 | .then(response => response.json()) 3 | .then(data => { 4 | // Do something with the retrieved data 5 | // console.log(data); 6 | var tableContainer = document.getElementById("myTable"); 7 | 8 | // Create a table element 9 | var table = document.createElement("table"); 10 | 11 | // Create the table headings row 12 | var headingsRow = document.createElement("tr"); 13 | var blankHeading = document.createElement("th"); 14 | // headingsRow.appendChild(blankHeading); // add blank cell to top left corner 15 | for (var i = 0; i < 4; i++) { 16 | var heading = document.createElement("th"); 17 | if(i==0) 18 | { 19 | var headingText = document.createTextNode("Question"); 20 | heading.appendChild(headingText); 21 | headingsRow.appendChild(heading); 22 | } 23 | if(i==1) 24 | { 25 | var headingText = document.createTextNode("Prefered Answer"); 26 | heading.appendChild(headingText); 27 | headingsRow.appendChild(heading); 28 | } 29 | if(i==2) 30 | { 31 | var headingText = document.createTextNode("Your Answer"); 32 | heading.appendChild(headingText); 33 | headingsRow.appendChild(heading); 34 | } 35 | if(i==3) 36 | { 37 | var headingText = document.createTextNode("Similarity"); 38 | // headingText = (headingText * 100).toFixed(2) + '%' 39 | heading.appendChild(headingText); 40 | headingsRow.appendChild(heading); 41 | } 42 | } 43 | table.appendChild(headingsRow); 44 | 45 | // Loop through the arrays and create table rows and cells 46 | for (var i = 0; i < 10; i++) { 47 | var row = document.createElement("tr"); 48 | for (var j = 0; j < 4; j++) { 49 | var cell = document.createElement("td"); 50 | var cellText = ''; 51 | if(j==3){ 52 | var similarity = parseFloat(data[i][j]); 53 | var similarityPercentage = (similarity * 100).toFixed(2) + '%'; 54 | cellText = document.createTextNode(similarityPercentage); 55 | } 56 | else 57 | { 58 | cellText = document.createTextNode(data[i][j]); 59 | } 60 | // console.log(cellText) 61 | cell.appendChild(cellText); 62 | row.appendChild(cell); 63 | } 64 | // console.log("newrow") 65 | table.appendChild(row); 66 | } 67 | 68 | // Append the table to the container div element 69 | tableContainer.appendChild(table); 70 | }) 71 | .catch(error => console.error(error)); -------------------------------------------------------------------------------- /static/script/Video_Test.js: -------------------------------------------------------------------------------- 1 | let start_button = document.querySelector("#start-record"); 2 | const timer = document.getElementById('timer'); 3 | const question = document.getElementById('question'); 4 | const nextBtn = document.getElementById('next'); 5 | nextBtn.disabled=true; 6 | 7 | let timer_duration = 10 * 60 * 1000; // 10 minutes in milliseconds 8 | let questions = ['QUESTIONS']; 9 | let timeLeft = 600; 10 | let timerInterval; 11 | 12 | let question_number = 0; 13 | 14 | let recorder; 15 | let chunks = []; 16 | 17 | 18 | async function startCamera() { 19 | fetch('start') 20 | .then(response => response.text()) 21 | .then(message => console.log(message)); 22 | document.getElementById('video-stream').src = "videofeed"; 23 | } 24 | 25 | 26 | function stopCamera() { 27 | fetch('stop') 28 | .then(response => response.text()) 29 | .then(message => console.log(message)); 30 | document.getElementById('video-stream').src = ""; 31 | } 32 | 33 | 34 | function startRecording() { 35 | navigator.mediaDevices.getUserMedia({ 36 | audio: { 37 | volume: 1.0 // set the audio volume to maximum 38 | } 39 | }) 40 | .then(stream => { 41 | recorder = new MediaRecorder(stream); 42 | recorder.addEventListener('dataavailable', event => { 43 | chunks.push(event.data); 44 | }); 45 | recorder.start(); 46 | }) 47 | .catch(error => { 48 | console.error(error); 49 | }); 50 | } 51 | 52 | function stopRecording() { 53 | return new Promise(resolve => { 54 | recorder.addEventListener('stop', () => { 55 | const blob = new Blob(chunks); 56 | const formData = new FormData(); 57 | formData.append('file', blob, 'audio'+ question_number +'.mp3'); 58 | fetch('/upload-audio/' + question_number, { method: 'POST', body: formData }) 59 | .then(response => response.json()) 60 | .then(data => { 61 | console.log(data); 62 | question_number += 1; 63 | resolve(); 64 | }) 65 | .catch(error => { 66 | console.error(error); 67 | resolve(); 68 | }); 69 | }); 70 | }); 71 | } 72 | 73 | function startTimer() { 74 | timerInterval = setInterval(() => { 75 | timeLeft--; 76 | timer.style.width = `${(timeLeft / 600) * 100}%`; 77 | if (timeLeft <= 0) 78 | { 79 | stopCamera(); 80 | window.location.href = "/Video_Test_Results"; 81 | } 82 | }, 1000); 83 | } 84 | 85 | 86 | fetch('Questions') 87 | .then(response => response.json()) 88 | .then(data => 89 | { 90 | questions = questions.concat(data); 91 | showQuestion(0); 92 | }); 93 | 94 | async function showQuestion(index) 95 | { 96 | if (index < questions.length) 97 | { 98 | question.textContent = questions[index]; 99 | currentQuestionIndex = index; 100 | } else { 101 | stopCamera(); 102 | question.textContent = "Processing" 103 | await new Promise(r => setTimeout(r, 10000)); 104 | window.location.href = "/Video_Test_Results"; 105 | } 106 | } 107 | 108 | nextBtn.addEventListener('click', async () => { 109 | stopRecording(); 110 | recorder.stop(); 111 | chunks = []; 112 | showQuestion(currentQuestionIndex + 1); 113 | startRecording(); 114 | }); 115 | 116 | start_button.addEventListener('click', async function() { 117 | await startCamera(); 118 | startTimer(); 119 | showQuestion(1); 120 | startRecording(); 121 | nextBtn.disabled = false; 122 | start_button.disabled = true; 123 | }); -------------------------------------------------------------------------------- /static/script/Video_Test_Results.js: -------------------------------------------------------------------------------- 1 | fetch('video_results') 2 | .then(response => response.json()) 3 | .then(data => { 4 | // Do something with the retrieved data 5 | console.log(data); 6 | var ctx = document.getElementById("myChart").getContext("2d"); 7 | // var data2 = [10, 20, 30, 40, 50, 60, 70, 80]; 8 | var myChart = new Chart(ctx, { 9 | type: 'doughnut', 10 | data: { 11 | labels: ['angry', 'dusgust', 'fear', 'happy', 'sad', 'surprise', 'neutral', 'no_face'], 12 | datasets: [{ 13 | data: data[1], 14 | backgroundColor: [ 15 | 'rgba(255, 99, 132, 1)', 16 | 'rgba(54, 162, 235, 1)', 17 | 'rgba(255, 206, 86, 1)', 18 | 'rgba(75, 192, 192, 1)', 19 | 'rgba(153, 102, 255, 1)', 20 | 'rgba(255, 159, 64, 1)', 21 | 'rgba(145, 195, 219, 1)', 22 | 'rgba(180, 221, 220, 1)' 23 | ], 24 | hoverOffset: 4 25 | }] 26 | }, 27 | options: { 28 | title: { 29 | display: true, 30 | text: 'Pie Chart', 31 | responsive: true, 32 | maintainAspectRatio: false, 33 | } 34 | } 35 | }); 36 | 37 | // Get the container div element 38 | var tableContainer = document.getElementById("myTable"); 39 | 40 | // Create a table element 41 | var table = document.createElement("table"); 42 | 43 | // Create the table headings row 44 | var headingsRow = document.createElement("tr"); 45 | var blankHeading = document.createElement("th"); 46 | // headingsRow.appendChild(blankHeading); // add blank cell to top left corner 47 | for (var i = 0; i < 4; i++) { 48 | var heading = document.createElement("th"); 49 | if(i==0) 50 | { 51 | var headingText = document.createTextNode("Question"); 52 | heading.appendChild(headingText); 53 | headingsRow.appendChild(heading); 54 | } 55 | if(i==1) 56 | { 57 | var headingText = document.createTextNode("Prefered Answer"); 58 | heading.appendChild(headingText); 59 | headingsRow.appendChild(heading); 60 | } 61 | if(i==2) 62 | { 63 | var headingText = document.createTextNode("Your Answer"); 64 | heading.appendChild(headingText); 65 | headingsRow.appendChild(heading); 66 | } 67 | if(i==3) 68 | { 69 | var headingText = document.createTextNode("Similarity"); 70 | // headingText = (headingText * 100).toFixed(2) + '%' 71 | heading.appendChild(headingText); 72 | headingsRow.appendChild(heading); 73 | } 74 | } 75 | table.appendChild(headingsRow); 76 | 77 | // Loop through the arrays and create table rows and cells 78 | for (var i = 0; i < data[0].length; i++) { 79 | var row = document.createElement("tr"); 80 | for (var j = 0; j < data[0][i].length; j++) { 81 | var cell = document.createElement("td"); 82 | var cellText = ''; 83 | if(j==3){ 84 | var similarity = parseFloat(data[0][i][j]); 85 | var similarityPercentage = (similarity * 100).toFixed(2) + '%'; 86 | cellText = document.createTextNode(similarityPercentage); 87 | } 88 | else 89 | { 90 | cellText = document.createTextNode(data[0][i][j]); 91 | } 92 | cell.appendChild(cellText); 93 | row.appendChild(cell); 94 | } 95 | table.appendChild(row); 96 | } 97 | 98 | // Append the table to the container div element 99 | tableContainer.appendChild(table); 100 | }) 101 | .catch(error => console.error(error)); -------------------------------------------------------------------------------- /templates/Instructions_text.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Mockvue 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
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18 | Home 19 | About 20 |
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INSTRUCTIONS

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Read the question carefully before answering

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You will have a total of 10 questions

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For each question you will have one minute to answer

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Timer can be seen below the navbar. The brown bar represents the time remaining

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Failure to answer the question within the time limit will be marked as 0

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Answers should be to the point

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Pay special attention to the grammar

32 | PROCEED 33 |
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35 | 36 | 37 | 38 | -------------------------------------------------------------------------------- /templates/Instructions_video.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Mockvue 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
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INSTRUCTIONS

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Look straight at the camera

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Make sure you are in a well lit environment

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Make sure there is no background noise

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Speak with clarity

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Before answering every question, use the phrase "for this question"

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You will have total of 10 questions

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You will have total of 10 minutes to answer all questions

32 | PROCEED 33 |
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35 | 36 | 37 | 38 | -------------------------------------------------------------------------------- /templates/Main_page.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Mockvue 5 | 6 | 7 | 8 | 9 | 10 | 11 |
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working

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