Welcome to my Django app!
98 | {% endblock %} 99 |
5 |
17 | {% endblock %}
18 |
--------------------------------------------------------------------------------
/Algolyzer/quiz/migrations/0002_alter_useranswer_topic_and_more.py:
--------------------------------------------------------------------------------
1 | # Generated by Django 5.1.4 on 2025-01-12 14:50
2 |
3 | import django.db.models.deletion
4 | from django.conf import settings
5 | from django.db import migrations, models
6 |
7 |
8 | class Migration(migrations.Migration):
9 | dependencies = [
10 | ("quiz", "0001_initial"),
11 | migrations.swappable_dependency(settings.AUTH_USER_MODEL),
12 | ]
13 |
14 | operations = [
15 | migrations.AlterField(
16 | model_name="useranswer",
17 | name="topic",
18 | field=models.ForeignKey(
19 | on_delete=django.db.models.deletion.CASCADE, to="quiz.topic"
20 | ),
21 | ),
22 | migrations.AlterUniqueTogether(
23 | name="useranswer",
24 | unique_together={("user", "topic", "question")},
25 | ),
26 | ]
27 |
--------------------------------------------------------------------------------
/Algolyzer/playground/models.py:
--------------------------------------------------------------------------------
1 | from django.contrib.auth.models import User
2 | from django.db import models
3 |
4 |
5 | class PlaygroundTask(models.Model):
6 | STATUS_CHOICES = [
7 | ("COMPLETED", "Completed"),
8 | ("FAILED", "Failed"),
9 | ]
10 | user = models.ForeignKey(User, on_delete=models.CASCADE)
11 | model_name = models.CharField(max_length=100) # Selected ML model
12 | input_data = models.TextField() # Store user input (can be JSON)
13 | input_image = models.ImageField(upload_to="doodles/", blank=True, null=True)
14 | status = models.CharField(max_length=20, choices=STATUS_CHOICES, default="PENDING")
15 | result = models.TextField(blank=True, null=True) # ML output
16 | created_at = models.DateTimeField(auto_now_add=True)
17 | completed_at = models.DateTimeField(blank=True, null=True)
18 |
19 | def __str__(self):
20 | return f"Task {self.model_name} - {self.status}"
21 |
--------------------------------------------------------------------------------
/package.json:
--------------------------------------------------------------------------------
1 | {
2 | "name": "algolyzer",
3 | "version": "1.0.0",
4 | "description": "A platform for learning about AI/ML models and algorithms from theoretical to executing pre-trained models all at one place.",
5 | "main": "index.js",
6 | "scripts": {
7 | "tw_build": "npx tailwindcss build -i Algolyzer/static/input.css -o Algolyzer/static/output.css",
8 | "tw_watch": "npx tailwindcss -i Algolyzer/static/input.css -o Algolyzer/static/output.css --watch"
9 | },
10 | "repository": {
11 | "type": "git",
12 | "url": "git+https://github.com/Priyanshu-Batham/Algolyzer.git"
13 | },
14 | "keywords": [],
15 | "author": "",
16 | "license": "ISC",
17 | "bugs": {
18 | "url": "https://github.com/Priyanshu-Batham/Algolyzer/issues"
19 | },
20 | "homepage": "https://github.com/Priyanshu-Batham/Algolyzer#readme",
21 | "devDependencies": {
22 | "autoprefixer": "^10.4.20",
23 | "daisyui": "^4.12.23",
24 | "postcss": "^8.5.1",
25 | "tailwindcss": "^3.4.17"
26 | }
27 | }
28 |
--------------------------------------------------------------------------------
/docs/setup_production.md:
--------------------------------------------------------------------------------
1 | # Pre Requisites
2 | The project is built using following versions of software:
3 | 1. Docker & Docker-Compose
4 |
5 | ## Clone the Forked Repository
6 | ```bash
7 | git clone https://github.com/priyanshu-batham/Algolyzer.git
8 | cd Algolyzer
9 | ```
10 |
11 | ## (Optional) Setting up Google OAuth service
12 | Goto [Google Developer Console](https://console.developers.google.com). Make a new OAuth Client using the following URL as the Authorized Redirect URI:
13 | ```http://localhost:8000/accounts/google/login/callback/```
14 |
15 | ## Copy .env.sample into .env
16 |
17 | ```bash
18 | cp .env.sample .env
19 | ```
20 | ## IMPORTANT: Read the `.env` carefully and make sure to uncomment and comment necessary `CELERY_BROKER_URL` lines in `.env` as specified there.
21 |
22 | 3. Run the docker containers
23 | ```bash
24 | docker-compose up --build
25 | ```
26 | ## Open a web browser and goto
27 | ```bash
28 | http://localhost:8000
29 | ```
30 | **Note - Always use `localhost:8000` instead of `127.0.0.1:8000` if you don't want access blocked from Google during development**
--------------------------------------------------------------------------------
/Algolyzer/quiz/migrations/0003_topic_description_topic_difficulty.py:
--------------------------------------------------------------------------------
1 | # Generated by Django 5.1.4 on 2025-01-31 10:28
2 |
3 | from django.db import migrations, models
4 |
5 |
6 | class Migration(migrations.Migration):
7 | dependencies = [
8 | ("quiz", "0002_alter_useranswer_topic_and_more"),
9 | ]
10 |
11 | operations = [
12 | migrations.AddField(
13 | model_name="topic",
14 | name="description",
15 | field=models.CharField(default="No description provided.", max_length=1000),
16 | ),
17 | migrations.AddField(
18 | model_name="topic",
19 | name="difficulty",
20 | field=models.CharField(
21 | choices=[
22 | ("Easy", "Easy"),
23 | ("Medium", "Medium"),
24 | ("Hard", "Hard"),
25 | ("Veteran", "Veteran"),
26 | ],
27 | default="Easy",
28 | help_text="Difficulty levels",
29 | max_length=7,
30 | ),
31 | ),
32 | ]
33 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
1 | MIT License
2 |
3 | Copyright (c) 2024 Priyanshu Batham
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
--------------------------------------------------------------------------------
/Algolyzer/quiz/management/commands/dump_quiz.py:
--------------------------------------------------------------------------------
1 | import os
2 |
3 | from django.core.management import call_command
4 | from django.core.management.base import BaseCommand
5 |
6 |
7 | class Command(BaseCommand):
8 | help = "Dump data for quiz models (Question and Topic) into a JSON file."
9 |
10 | def add_arguments(self, parser):
11 | parser.add_argument(
12 | "--output",
13 | type=str,
14 | default="./data/quiz_data.json",
15 | help="Output file path (default: ./data/quiz_data.json)",
16 | )
17 |
18 | def handle(self, *args, **kwargs):
19 | output_path = kwargs["output"]
20 | os.makedirs(os.path.dirname(output_path), exist_ok=True)
21 |
22 | self.stdout.write("Dumping quiz data into: {}".format(output_path))
23 |
24 | call_command(
25 | "dumpdata",
26 | "quiz.Question",
27 | "quiz.Topic",
28 | "--natural-primary",
29 | "--natural-foreign",
30 | "--indent",
31 | "4",
32 | stdout=open(output_path, "w"),
33 | )
34 |
35 | self.stdout.write(self.style.SUCCESS("Data dumped successfully!"))
36 |
--------------------------------------------------------------------------------
/Algolyzer/home/management/commands/create_superuser.py:
--------------------------------------------------------------------------------
1 | import os
2 |
3 | from django.contrib.auth import get_user_model
4 | from django.core.management.base import BaseCommand
5 |
6 |
7 | class Command(BaseCommand):
8 | help = "Create a superuser if it doesn't exist"
9 |
10 | def handle(self, *args, **kwargs):
11 | User = get_user_model()
12 | username = os.getenv("DJANGO_SUPERUSER_USERNAME")
13 | email = os.getenv("DJANGO_SUPERUSER_EMAIL")
14 | password = os.getenv("DJANGO_SUPERUSER_PASSWORD")
15 |
16 | if username and email and password:
17 | if not User.objects.filter(username=username).exists():
18 | User.objects.create_superuser(
19 | username=username, email=email, password=password
20 | )
21 | self.stdout.write(
22 | self.style.SUCCESS(f"Superuser {username} created successfully.")
23 | )
24 | else:
25 | self.stdout.write(
26 | self.style.WARNING(f"Superuser {username} already exists.")
27 | )
28 | else:
29 | self.stdout.write(
30 | self.style.ERROR(
31 | "Superuser credentials not provided. Skipping creation."
32 | )
33 | )
34 |
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/Algolyzer/templates/socialaccount/snippets/provider_list.html:
--------------------------------------------------------------------------------
1 | {% load allauth socialaccount %}
2 | {% get_providers as socialaccount_providers %}
3 | {% if socialaccount_providers %}
4 | {% element provider_list %}
5 | {% for provider in socialaccount_providers %}
6 | {% if provider.id == "google" %}
7 | {% provider_login_url provider process=process scope=scope auth_params=auth_params as href %}
8 |
9 | Sign in with Google
10 |
11 | {% elif provider.id == "facebook" %}
12 | {% provider_login_url provider process=process scope=scope auth_params=auth_params as href %}
13 |
14 | Sign in with Facebook
15 |
16 | {% else %}
17 | {% provider_login_url provider process=process scope=scope auth_params=auth_params as href %}
18 |
19 | {{ provider.name }}
20 |
21 | {% endif %}
22 | {% endfor %}
23 | {% endelement %}
24 | {% endif %}
25 |
26 |
--------------------------------------------------------------------------------
/Algolyzer/home/migrations/0001_initial.py:
--------------------------------------------------------------------------------
1 | # Generated by Django 5.1.4 on 2025-01-12 13:17
2 |
3 | import django.db.models.deletion
4 | from django.conf import settings
5 | from django.db import migrations, models
6 |
7 |
8 | class Migration(migrations.Migration):
9 | initial = True
10 |
11 | dependencies = [
12 | migrations.swappable_dependency(settings.AUTH_USER_MODEL),
13 | ]
14 |
15 | operations = [
16 | migrations.CreateModel(
17 | name="UserProfile",
18 | fields=[
19 | (
20 | "id",
21 | models.BigAutoField(
22 | auto_created=True,
23 | primary_key=True,
24 | serialize=False,
25 | verbose_name="ID",
26 | ),
27 | ),
28 | ("dob", models.DateField()),
29 | ("xp", models.PositiveIntegerField(default=0)),
30 | ("level", models.PositiveIntegerField(default=1)),
31 | (
32 | "user",
33 | models.OneToOneField(
34 | on_delete=django.db.models.deletion.CASCADE,
35 | to=settings.AUTH_USER_MODEL,
36 | ),
37 | ),
38 | ],
39 | ),
40 | ]
41 |
--------------------------------------------------------------------------------
/.github/workflows/ci.yml:
--------------------------------------------------------------------------------
1 | name: Python Lint, Format, and Tests
2 |
3 | on: [push]
4 |
5 | jobs:
6 | lint-format-test:
7 | runs-on: ubuntu-latest
8 |
9 | steps:
10 | - name: Checkout repository
11 | uses: actions/checkout@v3
12 |
13 | - name: Set up Node.js
14 | uses: actions/setup-node@v3
15 | with:
16 | node-version: 22.13.0
17 |
18 | - name: Install Node.js dependencies
19 | run: npm install
20 |
21 | - name: Build Tailwind CSS
22 | run: npm run tw_build
23 |
24 | - name: Set up Python
25 | uses: actions/setup-python@v4
26 | with:
27 | python-version: 3.12
28 |
29 | - name: Install dependencies
30 | run: |
31 | pip install -r requirements.txt
32 | pip install black isort flake8
33 |
34 | - name: Run Black (check)
35 | run: black --check .
36 |
37 | - name: Run Isort (check)
38 | run: isort --check-only .
39 |
40 | - name: Run Flake8
41 | run: flake8 .
42 |
43 | - name: Run Django Tests
44 | run: |
45 |
46 | # Run Django tests with manage.py
47 | export PYTHONPATH=./Algolyzer:$PYTHONPATH
48 | export DJANGO_SETTINGS_MODULE=Algolyzer.settings.development
49 | cd Algolyzer
50 | python manage.py test
51 |
--------------------------------------------------------------------------------
/Algolyzer/Algolyzer/urls.py:
--------------------------------------------------------------------------------
1 | """
2 | URL configuration for Algolyzer project.
3 |
4 | The `urlpatterns` list routes URLs to views. For more information please see:
5 | https://docs.djangoproject.com/en/5.1/topics/http/urls/
6 | Examples:
7 | Function views
8 | 1. Add an import: from my_app import views
9 | 2. Add a URL to urlpatterns: path('', views.home, name='home')
10 | Class-based views
11 | 1. Add an import: from other_app.views import Home
12 | 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
13 | Including another URLconf
14 | 1. Import the include() function: from django.urls import include, path
15 | 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
16 | """
17 |
18 | from django.contrib import admin
19 | from django.urls import include, path
20 |
21 | admin.site.site_header = "Algolyzer Admin"
22 | admin.site.site_title = "Algolyzer"
23 | admin.site.index_title = "Welcome to Algolyzer"
24 |
25 | urlpatterns = [
26 | # admin url
27 | path("admin/", admin.site.urls),
28 | # algolyzer urls
29 | path("", include("home.urls")),
30 | path("quiz/", include("quiz.urls")),
31 | path("study/", include("study.urls")),
32 | path("community/", include("community.urls")),
33 | path("playground/", include("playground.urls")),
34 | # allauth urls
35 | path("accounts/", include("allauth.urls")),
36 | ]
37 |
--------------------------------------------------------------------------------
/Algolyzer/playground/management/commands/download_models.py:
--------------------------------------------------------------------------------
1 | import os
2 |
3 | from django.core.management.base import BaseCommand
4 | from transformers import AutoModel, AutoTokenizer
5 |
6 | # Define models to download
7 | MODELS = {
8 | "sentiment-analysis": "finiteautomata/bertweet-base-sentiment-analysis",
9 | }
10 |
11 | # Define directory for saving models
12 | MODEL_DIR = os.path.join("playground", "aiml_models")
13 |
14 |
15 | class Command(BaseCommand):
16 | help = "Download Hugging Face models for use in Django views"
17 |
18 | def handle(self, *args, **kwargs):
19 | os.makedirs(MODEL_DIR, exist_ok=True)
20 | for task, model_name in MODELS.items():
21 | self.stdout.write(
22 | self.style.SUCCESS(f"Downloading {task} model: {model_name}...")
23 | )
24 |
25 | # Download model and tokenizer
26 | model = AutoModel.from_pretrained(model_name)
27 | tokenizer = AutoTokenizer.from_pretrained(model_name)
28 |
29 | # Save them in the specified directory
30 | model.save_pretrained(os.path.join(MODEL_DIR, model_name.replace("/", "_")))
31 | tokenizer.save_pretrained(
32 | os.path.join(MODEL_DIR, model_name.replace("/", "_"))
33 | )
34 |
35 | self.stdout.write(
36 | self.style.SUCCESS(f"✔ Model {model_name} downloaded successfully!\n")
37 | )
38 |
--------------------------------------------------------------------------------
/Algolyzer/community/models.py:
--------------------------------------------------------------------------------
1 | from django.contrib.auth import get_user_model
2 | from django.db import models
3 |
4 |
5 | class Category(models.Model):
6 | name = models.CharField(max_length=255, unique=True)
7 | description = models.TextField(blank=True)
8 |
9 | def __str__(self):
10 | return self.name
11 |
12 |
13 | class Post(models.Model):
14 | title = models.CharField(max_length=255)
15 | content = models.TextField()
16 | author = models.ForeignKey(get_user_model(), on_delete=models.CASCADE)
17 | created_at = models.DateTimeField(auto_now_add=True)
18 | updated_at = models.DateTimeField(auto_now=True)
19 | category = models.ForeignKey(Category, on_delete=models.CASCADE)
20 |
21 | def __str__(self):
22 | return self.title
23 |
24 |
25 | class Comment(models.Model):
26 | post = models.ForeignKey(Post, on_delete=models.CASCADE, related_name="comments")
27 | author = models.ForeignKey(get_user_model(), on_delete=models.CASCADE)
28 | content = models.TextField()
29 | created_at = models.DateTimeField(auto_now_add=True)
30 |
31 | def __str__(self):
32 | return f"Comment by {self.author.username} on {self.post.title}"
33 |
34 |
35 | class Vote(models.Model):
36 | VOTE_TYPES = [(1, "Upvote"), (-1, "Downvote")]
37 | post = models.ForeignKey(Post, on_delete=models.CASCADE, related_name="votes")
38 | user = models.ForeignKey(get_user_model(), on_delete=models.CASCADE)
39 | vote_type = models.IntegerField(choices=VOTE_TYPES)
40 |
41 | class Meta:
42 | unique_together = ("post", "user")
43 |
--------------------------------------------------------------------------------
/Dockerfile:
--------------------------------------------------------------------------------
1 | # Use Python slim image as base
2 | FROM python:3.12-slim
3 |
4 | # Set environment variables
5 | ENV PYTHONUNBUFFERED=1
6 |
7 | # Set the working directory
8 | WORKDIR /app
9 |
10 | # Install system dependencies
11 | RUN apt-get update && \
12 | apt-get install -y --no-install-recommends \
13 | curl \
14 | bash \
15 | nodejs \
16 | npm && \
17 | rm -rf /var/lib/apt/lists/* && \
18 | apt-get clean
19 |
20 | # Install specific npm version
21 | RUN npm install -g npm@10.9.2
22 |
23 | # Copy requirements.txt
24 | COPY ./requirements.txt /app/
25 |
26 | # Install Python dependencies
27 | RUN pip install --no-cache-dir -r requirements.txt gunicorn
28 |
29 | # Install Node.js dependencies (Tailwind, etc.)
30 | COPY ./package.json /app/
31 | RUN npm install
32 |
33 | # Copy project files
34 | COPY . /app/
35 |
36 | # Run tailwind build
37 | RUN npm run tw_build
38 |
39 | # Change working directory
40 | WORKDIR /app/Algolyzer/
41 |
42 | # Migrate database
43 | RUN python manage.py migrate
44 |
45 | # Seed the database
46 | RUN python manage.py loaddata data/*
47 |
48 | # Collect static files
49 | RUN python manage.py collectstatic --noinput
50 |
51 | # Download aiml models (thinking of pushing models to github and everywhere directly)
52 | RUN python manage.py download_models
53 |
54 | # Expose port 8000
55 | EXPOSE 8000
56 |
57 | # Use production settings instead of default development settings
58 | ENV DJANGO_SETTINGS_MODULE=Algolyzer.settings.production
59 |
60 | # Start the application
61 | CMD (celery -A Algolyzer worker --loglevel=info &) && python manage.py create_superuser && gunicorn --bind 0.0.0.0:8000 --access-logfile - --error-logfile - Algolyzer.wsgi:application
62 |
--------------------------------------------------------------------------------
/Algolyzer/home/migrations/0002_userprofile_address_userprofile_course_and_more.py:
--------------------------------------------------------------------------------
1 | # Generated by Django 5.1.4 on 2025-02-11 04:13
2 |
3 | import django.utils.timezone
4 | from django.db import migrations, models
5 |
6 |
7 | class Migration(migrations.Migration):
8 | dependencies = [
9 | ("home", "0001_initial"),
10 | ]
11 |
12 | operations = [
13 | migrations.AddField(
14 | model_name="userprofile",
15 | name="address",
16 | field=models.TextField(blank=True, null=True),
17 | ),
18 | migrations.AddField(
19 | model_name="userprofile",
20 | name="course",
21 | field=models.CharField(default="Undecided", max_length=100),
22 | ),
23 | migrations.AddField(
24 | model_name="userprofile",
25 | name="enrollment_date",
26 | field=models.DateField(default=django.utils.timezone.now),
27 | ),
28 | migrations.AddField(
29 | model_name="userprofile",
30 | name="full_name",
31 | field=models.CharField(default="Unknown", max_length=100),
32 | preserve_default=False,
33 | ),
34 | migrations.AddField(
35 | model_name="userprofile",
36 | name="gender",
37 | field=models.CharField(
38 | blank=True,
39 | choices=[("M", "Male"), ("F", "Female"), ("O", "Other")],
40 | max_length=1,
41 | null=True,
42 | ),
43 | ),
44 | migrations.AddField(
45 | model_name="userprofile",
46 | name="phone_number",
47 | field=models.CharField(blank=True, max_length=15, null=True),
48 | ),
49 | ]
50 |
--------------------------------------------------------------------------------
/Algolyzer/quiz/templates/quiz/question.html:
--------------------------------------------------------------------------------
1 | {% extends "base.html" %}
2 |
3 | {% block content %}
4 | Categories
6 |
7 | {% for category in categories %}
8 |
9 |
16 |
10 |
13 |
14 | {% endfor %}
15 | {{ category.name }}
11 |{{ category.description }}
12 |
5 | {% comment %} Steps {% endcomment %}
6 |
38 | {% endblock %}
--------------------------------------------------------------------------------
/Algolyzer/home/models.py:
--------------------------------------------------------------------------------
1 | from django.contrib.auth import get_user_model
2 | from django.db import models
3 |
4 |
5 | class UserProfile(models.Model):
6 | user = models.OneToOneField(get_user_model(), on_delete=models.CASCADE)
7 |
8 | # Personal Information
9 | full_name = models.CharField(max_length=100)
10 | dob = models.DateField()
11 | phone_number = models.CharField(max_length=15, blank=True, null=True)
12 | gender = models.CharField(
13 | max_length=1,
14 | choices=[("M", "Male"), ("F", "Female"), ("O", "Other")],
15 | blank=True,
16 | null=True,
17 | )
18 | address = models.TextField(blank=True, null=True)
19 |
20 | # Ranking System
21 | xp = models.PositiveIntegerField(default=0)
22 | level = models.PositiveIntegerField(default=1)
23 |
24 | def __str__(self):
25 | return self.user.username
26 |
27 | def add_xp(self, amount):
28 | """Add XP and check for level up"""
29 | self.xp += amount
30 | self.check_level_up()
31 | self.save()
32 |
33 | def check_level_up(self):
34 | """Update level based on XP thresholds"""
35 | level_thresholds = [0, 100, 300, 600, 1000, 1500, 2100, 2800, 3600, 4500]
36 | # If user has more exp than our thresholds itself then dynamically create new thresholds
37 | while len(level_thresholds) <= self.level:
38 | level_thresholds.append(
39 | level_thresholds[-1] + (self.level * 500)
40 | ) # Custom scaling
41 |
42 | for i, threshold in enumerate(level_thresholds):
43 | if self.xp >= threshold:
44 | self.level = i + 1
45 |
46 | def get_next_level_xp(self):
47 | """Returns XP required for the next level"""
48 | level_thresholds = [0, 100, 300, 600, 1000, 1500, 2100, 2800, 3600, 4500]
49 | if self.level < len(level_thresholds):
50 | return level_thresholds[self.level] - self.xp
51 | return None # If max level is reached
52 |
--------------------------------------------------------------------------------
/Algolyzer/Algolyzer/settings/production.py:
--------------------------------------------------------------------------------
1 | from .base import *
2 |
3 | DEBUG = True
4 |
5 | ALLOWED_HOSTS = os.getenv("ALLOWED_HOSTS", "").split(",")
6 | CSRF_TRUSTED_ORIGINS = os.getenv(
7 | "CSRF_TRUSTED_ORIGINS",
8 | "http://localhost,http://127.0.0.1,http://localhost:8000,http://127.0.0.1:8000",
9 | ).split(",")
10 |
11 |
12 | DATABASES = {
13 | "default": {
14 | "ENGINE": "django.db.backends.sqlite3",
15 | "NAME": BASE_DIR / "db.sqlite3",
16 | }
17 | }
18 |
19 | EMAIL_BACKEND = "django.core.mail.backends.console.EmailBackend"
20 |
21 | # Using whitenoise to serve static files (should use nginx)
22 | MIDDLEWARE.insert(1, "whitenoise.middleware.WhiteNoiseMiddleware")
23 | STATICFILES_STORAGE = "whitenoise.storage.CompressedManifestStaticFilesStorage"
24 |
25 | # CSRF_COOKIE_SECURE = False # Since you're using HTTP (for now)
26 | # SESSION_COOKIE_SECURE = False # Since you're using HTTP (for now)
27 |
28 | # To be done like this in future:
29 | # from .base import *
30 |
31 | # DEBUG = False
32 |
33 | # ALLOWED_HOSTS = os.getenv("ALLOWED_HOSTS", "").split(",")
34 |
35 | # for https:
36 | # SECURE_PROXY_SSL_HEADER = ("HTTP_X_FORWARDED_PROTO", "https")
37 | # SESSION_COOKIE_SECURE = True
38 | # CSRF_COOKIE_SECURE = True
39 |
40 | # DATABASES = {
41 | # "default": {
42 | # "ENGINE": "django.db.backends.postgresql",
43 | # "NAME": os.getenv("DB_NAME"),
44 | # "USER": os.getenv("DB_USER"),
45 | # "PASSWORD": os.getenv("DB_PASSWORD"),
46 | # "HOST": os.getenv("DB_HOST"),
47 | # "PORT": os.getenv("DB_PORT", "5432"),
48 | # }
49 | # }
50 |
51 | # SECURE_BROWSER_XSS_FILTER = True
52 | # SECURE_CONTENT_TYPE_NOSNIFF = True
53 | # SESSION_COOKIE_SECURE = True
54 | # CSRF_COOKIE_SECURE = True
55 | # X_FRAME_OPTIONS = "DENY"
56 |
57 | # EMAIL_BACKEND = "django.core.mail.backends.smtp.EmailBackend"
58 | # EMAIL_HOST = os.getenv("EMAIL_HOST")
59 | # EMAIL_PORT = os.getenv("EMAIL_PORT")
60 | # EMAIL_USE_TLS = True
61 | # EMAIL_HOST_USER = os.getenv("EMAIL_HOST_USER")
62 | # EMAIL_HOST_PASSWORD = os.getenv("EMAIL_HOST_PASSWORD")
63 |
--------------------------------------------------------------------------------
/Algolyzer/study/migrations/0001_initial.py:
--------------------------------------------------------------------------------
1 | # Generated by Django 5.1.4 on 2025-01-13 13:31
2 |
3 | import django.db.models.deletion
4 | from django.db import migrations, models
5 |
6 |
7 | class Migration(migrations.Migration):
8 | initial = True
9 |
10 | dependencies = []
11 |
12 | operations = [
13 | migrations.CreateModel(
14 | name="Category",
15 | fields=[
16 | (
17 | "id",
18 | models.BigAutoField(
19 | auto_created=True,
20 | primary_key=True,
21 | serialize=False,
22 | verbose_name="ID",
23 | ),
24 | ),
25 | ("name", models.CharField(max_length=255)),
26 | (
27 | "parent",
28 | models.ForeignKey(
29 | blank=True,
30 | null=True,
31 | on_delete=django.db.models.deletion.CASCADE,
32 | related_name="subcategories",
33 | to="study.category",
34 | ),
35 | ),
36 | ],
37 | ),
38 | migrations.CreateModel(
39 | name="Module",
40 | fields=[
41 | (
42 | "id",
43 | models.BigAutoField(
44 | auto_created=True,
45 | primary_key=True,
46 | serialize=False,
47 | verbose_name="ID",
48 | ),
49 | ),
50 | ("title", models.CharField(max_length=255)),
51 | ("content", models.TextField()),
52 | ("created_at", models.DateTimeField(auto_now_add=True)),
53 | (
54 | "category",
55 | models.ForeignKey(
56 | on_delete=django.db.models.deletion.CASCADE, to="study.category"
57 | ),
58 | ),
59 | ],
60 | ),
61 | ]
62 |
--------------------------------------------------------------------------------
/Algolyzer/playground/migrations/0001_initial.py:
--------------------------------------------------------------------------------
1 | # Generated by Django 5.1.4 on 2025-02-10 16:23
2 |
3 | import django.db.models.deletion
4 | from django.conf import settings
5 | from django.db import migrations, models
6 |
7 |
8 | class Migration(migrations.Migration):
9 | initial = True
10 |
11 | dependencies = [
12 | migrations.swappable_dependency(settings.AUTH_USER_MODEL),
13 | ]
14 |
15 | operations = [
16 | migrations.CreateModel(
17 | name="PlaygroundTask",
18 | fields=[
19 | (
20 | "id",
21 | models.BigAutoField(
22 | auto_created=True,
23 | primary_key=True,
24 | serialize=False,
25 | verbose_name="ID",
26 | ),
27 | ),
28 | ("celery_task_id", models.CharField(max_length=255, unique=True)),
29 | ("model_name", models.CharField(max_length=100)),
30 | ("input_data", models.TextField()),
31 | (
32 | "status",
33 | models.CharField(
34 | choices=[
35 | ("PENDING", "Pending"),
36 | ("PROCESSING", "Processing"),
37 | ("COMPLETED", "Completed"),
38 | ("FAILED", "Failed"),
39 | ],
40 | default="PENDING",
41 | max_length=20,
42 | ),
43 | ),
44 | ("result", models.TextField(blank=True, null=True)),
45 | ("created_at", models.DateTimeField(auto_now_add=True)),
46 | ("completed_at", models.DateTimeField(blank=True, null=True)),
47 | (
48 | "user",
49 | models.ForeignKey(
50 | on_delete=django.db.models.deletion.CASCADE,
51 | to=settings.AUTH_USER_MODEL,
52 | ),
53 | ),
54 | ],
55 | ),
56 | ]
57 |
--------------------------------------------------------------------------------
/Algolyzer/quiz/admin.py:
--------------------------------------------------------------------------------
1 | from django.contrib import admin
2 |
3 | from .models import Question, QuizProgress, Topic, UserAnswer
4 |
5 |
6 | # Inline for Questions
7 | class QuestionInline(admin.TabularInline):
8 | model = Question
9 | extra = 1 # Number of empty forms to display
10 |
11 |
12 | # Customize admin interface for Topic
13 | class TopicAdmin(admin.ModelAdmin):
14 | list_display = ("id", "name", "difficulty")
15 | search_fields = ("name",)
16 | list_filter = ("difficulty",)
17 | inlines = [QuestionInline]
18 |
19 |
20 | # Customize admin interface for Question
21 | class QuestionAdmin(admin.ModelAdmin):
22 | list_display = ("id", "topic", "text", "correct_answer")
23 | list_filter = ("topic", "topic__difficulty")
24 | search_fields = ("text",)
25 | fieldsets = (
26 | (None, {"fields": ("topic", "text")}),
27 | ("Options", {"fields": ("option_a", "option_b", "option_c", "option_d")}),
28 | ("Correct Answer", {"fields": ("correct_answer",)}),
29 | )
30 | radio_fields = {
31 | "correct_answer": admin.HORIZONTAL
32 | } # Use radio buttons for correct_answer
33 |
34 |
35 | # Customize admin interface for UserAnswer
36 | class UserAnswerAdmin(admin.ModelAdmin):
37 | list_display = ("id", "user", "question", "selected_answer", "is_correct")
38 | list_filter = ("is_correct",)
39 | search_fields = ("user__username", "question__text")
40 | readonly_fields = ("is_correct",) # Make is_correct read-only
41 |
42 |
43 | # Customize admin interface for QuizProgress
44 | class QuizProgressAdmin(admin.ModelAdmin):
45 | list_display = ("id", "user", "topic", "current_question", "completed")
46 | list_filter = ("completed",)
47 | search_fields = ("user__username", "topic__name")
48 | actions = ["mark_as_completed"]
49 |
50 | def mark_as_completed(self, request, queryset):
51 | queryset.update(completed=True)
52 |
53 | mark_as_completed.short_description = "Mark selected progress records as completed"
54 |
55 |
56 | # Register models with their respective ModelAdmin
57 | admin.site.register(Topic, TopicAdmin)
58 | admin.site.register(Question, QuestionAdmin)
59 | admin.site.register(UserAnswer, UserAnswerAdmin)
60 | admin.site.register(QuizProgress, QuizProgressAdmin)
61 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # Algolyzer - Learn AI/ML with hands on practicals.
2 |
3 | A platform for learning about AI/ML models and algorithms from theoretical to executing pre-trained models all at one place.
4 | 
5 |
6 |
7 | ## Features(Planned)
8 | - Interactive Quizes
9 | - Levels and Experience points
10 | - Playground for running pre trained AIML models
11 | - Community Forums for deep discussions
12 | - Study Material offering explanations of AIML model working
13 | - Statistical Dashboard
14 |
15 | ## Status
16 | - 60-70% complete
17 | - Initial deployment at [link](http://algolyzer.servehttp.com)
18 | - Focusing on finishing the core modules of Algolyzer
19 |
20 | ## Progress
21 | - [x] Homepage
22 | - [x] Playground
23 | - [x] Community Forum
24 | - [x] Quiz
25 | - [x] Documentation
26 | - [x] Dashboard
27 | - [ ] Study Material
28 |
29 | ## How to setup Project
30 | - Development - [Setup manually](./docs/setup_development.md).
31 | - Production - [Setup with Docker](./docs/setup_production.md).
32 | ## Contribution
33 | For guidelines on how to contribute to this project, check out [Contribution Guidelines](./docs/CONTRIBUTING.md).
34 |
35 | ## Brief Description
36 | Algolyzer has the following apps:
37 |
38 | ### Quiz
39 | Learn about AI/ML models and test your knowledge by taking interesting quizzes.
40 | - Quizzes are of multiple level ranging from Easy to Veteran.
41 | - Completing a quiz gains you xp(experience).
42 | - The cumulative experience helps you unlock new models in the playground.
43 |
44 | ### Playground
45 | Run Machine Learning models and learn from experience.
46 | - Models are run on our server, reducing the need of expensive hardware.
47 | - You supply your own prompt to the model.
48 | - Output is received in the browser after processing.
49 |
50 | ### Study
51 | Learn about Machine Learning models and how to implement them yourself.
52 | - Study material is divided into modules for each type of model.
53 | - Each module contains comprehensive information about the working of the model.
54 | - The code needed to run the model yourself is included in-line.
55 |
56 | ### Community
57 | Discuss topics of interest with people all around the world using Algolyzer Community.
58 | - Get help and connect with awesome people using the platform.
59 | - Your submissions are visible to everyone, but your profile is kept private.
60 | - Forums are divided into multiple categories so you can find your niche in a few clicks.
61 | - Posts are editable, so if you need to change anything you can.
62 | - Remember to follow proper netiquette while using the community app.
--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
1 | absl-py==2.1.0
2 | amqp==5.3.1
3 | asgiref==3.8.1
4 | astunparse==1.6.3
5 | billiard==4.2.1
6 | black==24.10.0
7 | celery==5.4.0
8 | certifi==2024.12.14
9 | cffi==1.17.1
10 | cfgv==3.4.0
11 | charset-normalizer==3.4.1
12 | click==8.1.8
13 | click-didyoumean==0.3.1
14 | click-plugins==1.1.1
15 | click-repl==0.3.0
16 | colorama==0.4.6
17 | cryptography==44.0.0
18 | distlib==0.3.9
19 | Django==5.1.4
20 | django-allauth==65.3.1
21 | django-ckeditor==6.7.2
22 | django-js-asset==3.0.1
23 | filelock==3.16.1
24 | flake8==7.1.1
25 | flatbuffers==25.1.24
26 | fsspec==2025.2.0
27 | gast==0.6.0
28 | google-pasta==0.2.0
29 | grpcio==1.70.0
30 | h5py==3.12.1
31 | huggingface-hub==0.28.1
32 | identify==2.6.4
33 | idna==3.10
34 | isort==5.13.2
35 | Jinja2==3.1.5
36 | joblib==1.4.2
37 | keras==3.8.0
38 | kombu==5.4.2
39 | libclang==18.1.1
40 | Markdown==3.7
41 | markdown-it-py==3.0.0
42 | MarkupSafe==3.0.2
43 | mccabe==0.7.0
44 | mdurl==0.1.2
45 | ml-dtypes==0.4.1
46 | mpmath==1.3.0
47 | mypy-extensions==1.0.0
48 | namex==0.0.8
49 | networkx==3.4.2
50 | nodeenv==1.9.1
51 | numpy==2.0.2
52 | nvidia-cublas-cu12==12.4.5.8
53 | nvidia-cuda-cupti-cu12==12.4.127
54 | nvidia-cuda-nvrtc-cu12==12.4.127
55 | nvidia-cuda-runtime-cu12==12.4.127
56 | nvidia-cudnn-cu12==9.1.0.70
57 | nvidia-cufft-cu12==11.2.1.3
58 | nvidia-curand-cu12==10.3.5.147
59 | nvidia-cusolver-cu12==11.6.1.9
60 | nvidia-cusparse-cu12==12.3.1.170
61 | nvidia-cusparselt-cu12==0.6.2
62 | nvidia-nvjitlink-cu12==12.4.127
63 | nvidia-nvtx-cu12==12.4.127
64 | opt_einsum==3.4.0
65 | optree==0.14.0
66 | packaging==24.2
67 | pathspec==0.12.1
68 | pillow==11.1.0
69 | platformdirs==4.3.6
70 | pre_commit==4.0.1
71 | prompt_toolkit==3.0.50
72 | protobuf==5.29.3
73 | pycodestyle==2.12.1
74 | pycparser==2.22
75 | pyflakes==3.2.0
76 | Pygments==2.19.1
77 | PyJWT==2.10.1
78 | python-dateutil==2.9.0.post0
79 | python-dotenv==1.0.1
80 | PyYAML==6.0.2
81 | redis==5.2.1
82 | regex==2024.11.6
83 | requests==2.32.3
84 | rich==13.9.4
85 | safetensors==0.5.2
86 | scikit-learn==1.6.1
87 | scipy==1.15.2
88 | setuptools==75.8.0
89 | six==1.17.0
90 | sqlparse==0.5.3
91 | sympy==1.13.1
92 | tensorboard==2.18.0
93 | tensorboard-data-server==0.7.2
94 | tensorflow==2.18.0
95 | termcolor==2.5.0
96 | tf_keras==2.18.0
97 | threadpoolctl==3.6.0
98 | tokenizers==0.21.0
99 | torch==2.6.0
100 | torchaudio==2.6.0
101 | torchvision==0.21.0
102 | tqdm==4.67.1
103 | transformers==4.48.3
104 | typing_extensions==4.12.2
105 | tzdata==2025.1
106 | urllib3==2.3.0
107 | vine==5.1.0
108 | virtualenv==20.28.0
109 | wcwidth==0.2.13
110 | Werkzeug==3.1.3
111 | wheel==0.45.1
112 | whitenoise==6.8.2
113 | wrapt==1.17.2
114 |
--------------------------------------------------------------------------------
/docs/setup_development.md:
--------------------------------------------------------------------------------
1 | ## Pre Requisites
2 | The project is built using following versions of software:
3 | 1. Python 3.12.3
4 | 2. pip 24.3.1
5 | 3. Node 22.13.0
6 | 4. Npm 10.9.2
7 | 5. Docker
8 |
9 | ## Clone the Forked Repository
10 | ```bash
11 | git clone https://github.com/priyanshu-batham/Algolyzer.git
12 | cd Algolyzer
13 | ```
14 |
15 | ## Setting up Google OAuth service (Optional)
16 | Goto [Google Developer Console](https://console.developers.google.com). Make a new OAuth Client using the following URL as the Authorized Redirect URI:
17 | ```http://localhost:8000/accounts/google/login/callback/```
18 |
19 | ## Run the following commands (Windows users just use git bash):
20 | ```bash
21 | # Create a virtual environment named 'env'
22 | python3 -m venv env
23 |
24 | # Activate the virtual environment
25 | source env/bin/activate
26 | # (For Windows)
27 | source env/Scripts/activate
28 |
29 | # Create .env file from .env.sample
30 | cp .env.sample .env
31 |
32 | # Edit the values in .env accordingly and check it
33 | cat .env
34 |
35 | # Install python dependencies
36 | pip install -r requirements.txt
37 |
38 | # Install node dependencies
39 | npm install
40 |
41 | # Install pre-commit hooks
42 | pre-commit install
43 |
44 | # Navigate into Algolyzer project
45 | cd Algolyzer
46 |
47 | # Apply migrations
48 | python manage.py migrate
49 |
50 | # Create a local administrator (make sure you specified values in .env)
51 | python manage.py create_superuser
52 |
53 | # Load default values in database
54 | python manage.py loaddata data/*
55 |
56 | # download aiml models
57 | python manage.py download_models
58 |
59 | # start redis as a docker container
60 | docker run --name redis -p 6379:6379 -d redis
61 |
62 | # start celery
63 | celery -A Algolyzer worker --loglevel=info
64 | # (For Windows)
65 | celery -A Algolyzer worker --loglevel=info --pool=threads
66 |
67 | # Now open a NEW TERMINAL and start tailwind in Algolyzer dir
68 | npm run tw_watch
69 |
70 | # Finally Open a Third terminal and Run Django server.
71 | source myenv/bin/activate
72 | # (For Windows)
73 | source env/Scripts/activate
74 |
75 | cd Algolyzer
76 | python manage.py runserver 8000
77 | ```
78 | ## Open a web browser and goto
79 | ```bash
80 | http://localhost:8000
81 |
82 | ```
83 | **Note - Always use `localhost:8000` instead of `127.0.0.1:8000` if you don't want access blocked from Google during development**
--------------------------------------------------------------------------------
/Algolyzer/home/forms.py:
--------------------------------------------------------------------------------
1 | import re
2 | from datetime import date
3 |
4 | from django import forms
5 | from django.core.exceptions import ValidationError
6 |
7 | from .models import UserProfile
8 |
9 |
10 | class OnboardingForm(forms.ModelForm):
11 | dob = forms.DateField(
12 | widget=forms.DateInput(attrs={"type": "date", "class": "form-control"}),
13 | label="Date of Birth",
14 | required=True,
15 | )
16 |
17 | full_name = forms.CharField(
18 | widget=forms.TextInput(attrs={"class": "form-control"}),
19 | label="Full Name",
20 | max_length=100,
21 | required=True,
22 | )
23 |
24 | phone_number = forms.CharField(
25 | widget=forms.TextInput(
26 | attrs={"class": "form-control", "placeholder": "+1234567890"}
27 | ),
28 | label="Phone Number",
29 | max_length=15, # Increased to accommodate country codes
30 | required=True,
31 | )
32 |
33 | gender = forms.ChoiceField(
34 | choices=[("M", "Male"), ("F", "Female"), ("O", "Other")],
35 | widget=forms.Select(attrs={"class": "form-control"}),
36 | label="Gender",
37 | required=True,
38 | )
39 |
40 | address = forms.CharField(
41 | widget=forms.Textarea(attrs={"class": "form-control", "rows": 3}),
42 | label="Address",
43 | required=True,
44 | )
45 |
46 | class Meta:
47 | model = UserProfile
48 | fields = [
49 | "full_name",
50 | "dob",
51 | "phone_number",
52 | "gender",
53 | "address",
54 | ]
55 |
56 | def clean_phone_number(self):
57 | phone_number = self.cleaned_data.get("phone_number")
58 |
59 | # Remove all non-digit characters
60 | # cleaned_number = re.sub(r'[^\d+]', '', phone_number)
61 |
62 | # Check if the number contains only digits and optional + at start
63 | if not re.match(r"^\+?\d+$", phone_number):
64 | raise ValidationError(
65 | "Phone number must contain only numbers and an optional + at the start."
66 | )
67 |
68 | # Check minimum length (adjust according to your requirements)
69 | if len(phone_number) < 10:
70 | raise ValidationError("Phone number must be at least 10 digits long.")
71 |
72 | return phone_number
73 |
74 | def clean_dob(self):
75 | dob = self.cleaned_data.get("dob")
76 | today = date.today()
77 |
78 | # Calculate age
79 | age = today.year - dob.year - ((today.month, today.day) < (dob.month, dob.day))
80 |
81 | if age < 12:
82 | raise ValidationError("You must be at least 12 years old to register.")
83 |
84 | return dob
85 |
--------------------------------------------------------------------------------
/Algolyzer/playground/templates/playground/sentiment_analysis.html:
--------------------------------------------------------------------------------
1 | {% extends "base.html" %}
2 |
3 | {% block content %}
4 |
7 |
15 |
16 |
34 |
35 |
36 | {% comment %} -
8 | {% for i in total_array %}
9 |
12 | {% endfor %}
13 |
Question {{ progress.current_question|add:1 }} of {{ progress.topic.question_set.count }}
{% endcomment %} 37 |
5 |
59 | {% endblock %}
--------------------------------------------------------------------------------
/Algolyzer/Algolyzer/settings/base.py:
--------------------------------------------------------------------------------
1 | import os
2 | from pathlib import Path
3 |
4 | from dotenv import load_dotenv
5 |
6 | # Load environment variables
7 | load_dotenv()
8 |
9 | BASE_DIR = Path(__file__).resolve().parent.parent.parent
10 |
11 | # Security settings (overridden in development and production)
12 | SECRET_KEY = os.getenv("SECRET_KEY", "your-default-secret-key")
13 | DEBUG = False # Will be overridden in respective settings files
14 |
15 | ALLOWED_HOSTS = []
16 |
17 | INSTALLED_APPS = [
18 | # default django apps
19 | "django.contrib.admin",
20 | "django.contrib.auth",
21 | "django.contrib.contenttypes",
22 | "django.contrib.sessions",
23 | "django.contrib.messages",
24 | "django.contrib.staticfiles",
25 | # allauth apps
26 | "allauth",
27 | "allauth.account",
28 | "allauth.socialaccount",
29 | "allauth.socialaccount.providers.google",
30 | # algolyzer apps
31 | "home",
32 | "quiz",
33 | "study",
34 | "community",
35 | "playground",
36 | ]
37 |
38 | MIDDLEWARE = [
39 | "django.middleware.security.SecurityMiddleware",
40 | "django.contrib.sessions.middleware.SessionMiddleware",
41 | "django.middleware.common.CommonMiddleware",
42 | "django.middleware.csrf.CsrfViewMiddleware",
43 | "django.contrib.auth.middleware.AuthenticationMiddleware",
44 | "django.contrib.messages.middleware.MessageMiddleware",
45 | "django.middleware.clickjacking.XFrameOptionsMiddleware",
46 | "allauth.account.middleware.AccountMiddleware",
47 | ]
48 |
49 | ROOT_URLCONF = "Algolyzer.urls"
50 |
51 | TEMPLATES = [
52 | {
53 | "BACKEND": "django.template.backends.django.DjangoTemplates",
54 | "DIRS": [BASE_DIR / "templates"],
55 | "APP_DIRS": True,
56 | "OPTIONS": {
57 | "context_processors": [
58 | "django.template.context_processors.debug",
59 | "django.template.context_processors.request",
60 | "django.contrib.auth.context_processors.auth",
61 | "django.contrib.messages.context_processors.messages",
62 | ],
63 | },
64 | },
65 | ]
66 |
67 | WSGI_APPLICATION = "Algolyzer.wsgi.application"
68 |
69 | # Authentication
70 | AUTHENTICATION_BACKENDS = [
71 | "django.contrib.auth.backends.ModelBackend",
72 | "allauth.account.auth_backends.AuthenticationBackend",
73 | ]
74 |
75 | SOCIALACCOUNT_PROVIDERS = {
76 | "google": {
77 | "APP": {
78 | "client_id": os.getenv("GOOGLE_CLIENT_ID"),
79 | "secret": os.getenv("GOOGLE_SECRET_KEY"),
80 | }
81 | }
82 | }
83 |
84 | ACCOUNT_AUTHENTICATION_METHOD = "email"
85 | ACCOUNT_EMAIL_REQUIRED = True
86 | LOGIN_REDIRECT_URL = "/dashboard/"
87 | ACCOUNT_LOGOUT_ON_GET = False
88 | ACCOUNT_SIGNUP_REDIRECT_URL = "/onboarding/"
89 |
90 | LANGUAGE_CODE = "en-us"
91 | TIME_ZONE = "UTC"
92 | USE_I18N = True
93 | USE_TZ = True
94 | DEFAULT_AUTO_FIELD = "django.db.models.BigAutoField"
95 |
96 | STATIC_URL = "/static/"
97 | STATICFILES_DIRS = [BASE_DIR / "static"]
98 | STATIC_ROOT = BASE_DIR / "staticfiles"
99 |
100 | # AIML downloaded models directory
101 | MODEL_DIR = "/aiml_models"
102 |
--------------------------------------------------------------------------------
/Algolyzer/quiz/models.py:
--------------------------------------------------------------------------------
1 | from django.contrib.auth import get_user_model
2 | from django.db import models
3 |
4 |
5 | class Topic(models.Model):
6 | name = models.CharField(max_length=100)
7 | description = models.CharField(max_length=1000, default="No description provided.")
8 | difficulty = models.CharField(
9 | max_length=7,
10 | choices=[
11 | ("Easy", "Easy"),
12 | ("Medium", "Medium"),
13 | ("Hard", "Hard"),
14 | ("Veteran", "Veteran"),
15 | ],
16 | default="Easy",
17 | help_text="Difficulty levels",
18 | )
19 |
20 | def __str__(self):
21 | return self.name
22 |
23 |
24 | class Question(models.Model):
25 | topic = models.ForeignKey(Topic, on_delete=models.CASCADE)
26 | text = models.TextField() # The question text
27 | option_a = models.CharField(max_length=200) # Option A
28 | option_b = models.CharField(max_length=200) # Option B
29 | option_c = models.CharField(max_length=200) # Option C
30 | option_d = models.CharField(max_length=200) # Option D
31 | correct_answer = models.CharField(
32 | max_length=1,
33 | choices=[
34 | ("a", "Option A"),
35 | ("b", "Option B"),
36 | ("c", "Option C"),
37 | ("d", "Option D"),
38 | ],
39 | help_text="Correct answer should be 'a', 'b', 'c', or 'd'",
40 | )
41 |
42 | def get_option_text(self, option_key):
43 | """Returns the full text of the option based on the key (a, b, c, d)."""
44 | option_mapping = {
45 | "a": self.option_a,
46 | "b": self.option_b,
47 | "c": self.option_c,
48 | "d": self.option_d,
49 | }
50 | return option_mapping.get(option_key, "Invalid Option")
51 |
52 | def __str__(self):
53 | return self.text
54 |
55 |
56 | class UserAnswer(models.Model):
57 | topic = models.ForeignKey(Topic, on_delete=models.CASCADE)
58 | user = models.ForeignKey(get_user_model(), on_delete=models.CASCADE)
59 | question = models.ForeignKey(Question, on_delete=models.CASCADE)
60 | selected_answer = models.CharField(
61 | max_length=1,
62 | choices=[
63 | ("a", "Option A"),
64 | ("b", "Option B"),
65 | ("c", "Option C"),
66 | ("d", "Option D"),
67 | ],
68 | help_text="Selected answer should be 'a', 'b', 'c', or 'd'",
69 | )
70 | is_correct = models.BooleanField(default=False)
71 |
72 | class Meta:
73 | unique_together = (
74 | "user",
75 | "topic",
76 | "question",
77 | ) # Ensure a user can only answer a question once in a topic
78 |
79 | def save(self, *args, **kwargs):
80 | # Automatically determine if the selected answer is correct
81 | self.is_correct = self.selected_answer == self.question.correct_answer
82 | super().save(*args, **kwargs)
83 |
84 |
85 | class QuizProgress(models.Model):
86 | user = models.ForeignKey(get_user_model(), on_delete=models.CASCADE)
87 | topic = models.ForeignKey(Topic, on_delete=models.CASCADE)
88 | current_question = models.IntegerField(default=0)
89 | completed = models.BooleanField(default=False)
90 |
--------------------------------------------------------------------------------
/Algolyzer/playground/tasks.py:
--------------------------------------------------------------------------------
1 | import os
2 |
3 | from celery import shared_task
4 | from django.conf import settings
5 | from django.utils.timezone import now
6 | from PIL import Image
7 | from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
8 |
9 | from .models import PlaygroundTask
10 |
11 |
12 | @shared_task(bind=True)
13 | def sentiment_analysis_task(self, task_db_id, input_data):
14 | """Celery task to process ML model input."""
15 | try:
16 | # Fetch the task from the database
17 | task = PlaygroundTask.objects.get(id=task_db_id)
18 | task.status = "PROCESSING"
19 | task.save()
20 |
21 | MODEL_DIR = os.path.join(
22 | settings.BASE_DIR,
23 | "playground",
24 | "aiml_models",
25 | "finiteautomata_bertweet-base-sentiment-analysis",
26 | )
27 |
28 | # Load model from saved directory
29 | tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
30 | model = AutoModelForSequenceClassification.from_pretrained(MODEL_DIR)
31 | sentiment_pipeline = pipeline(
32 | "sentiment-analysis", model=model, tokenizer=tokenizer
33 | )
34 |
35 | if not model:
36 | raise ValueError(
37 | "Model 'finiteautomata_bertweet-base-sentiment-analysis' not found."
38 | )
39 |
40 | # Process the input with the model
41 | result = sentiment_pipeline(input_data)[0]
42 |
43 | # Update the task in the database
44 | task.status = "COMPLETED"
45 | task.result = str(result) # Convert to JSON-friendly format if needed
46 | task.completed_at = now()
47 | task.save()
48 |
49 | except Exception as e:
50 | task.status = "FAILED"
51 | task.result = str(e)
52 | task.save()
53 | # raise e
54 |
55 | return task.result # Celery stores the result
56 |
57 |
58 | # Load the pre-trained MNIST model from Hugging Face
59 | mnist_classifier = pipeline(
60 | "image-classification", model="farleyknight/mnist-digit-classification-2022-09-04"
61 | )
62 |
63 |
64 | @shared_task
65 | def doodle_classifier_task(task_id):
66 | try:
67 | # Get the task
68 | task = PlaygroundTask.objects.get(id=task_id)
69 |
70 | # Open the saved PNG image from the input_image field
71 | image = Image.open(task.input_image.path)
72 | print("image being loaded: ", image.filename)
73 |
74 | # Run inference using the pre-trained model
75 | result = mnist_classifier(image)
76 | print(str(result))
77 | best_prediction = max(result, key=lambda x: x["score"])
78 | best_label = best_prediction["label"]
79 | # print("Label: ", best_label, "Score: ", best_score)
80 |
81 | # Update the task with the result
82 | task.result = best_label # Convert the result to a string for storage
83 | task.status = "COMPLETED"
84 | task.save()
85 |
86 | except Exception as e:
87 | # Handle any errors that occur during processing
88 | task = PlaygroundTask.objects.get(id=task_id)
89 | task.result = str(e) # Store the error message
90 | task.status = "FAILED"
91 | task.save()
92 |
93 | return task.result
94 |
--------------------------------------------------------------------------------
/Algolyzer/community/templates/community/post_list.html:
--------------------------------------------------------------------------------
1 | {% extends 'base.html' %}
2 |
3 | {% block content %}
4 |
6 |
58 | Sentiment Analysis
7 | 8 | 9 | 19 | 20 | 21 |
22 |
57 | Previous Results
23 |
24 | {% for result in previous_results %}
25 |
56 |
26 |
27 |
52 | {% empty %}
53 |
28 | Prompt: {{ result.input_data|truncatechars:50 }}
29 |
30 |
31 |
51 |
32 |
50 |
33 |
37 |
38 | Status
34 | {{ result.status }}
35 | Failed | Completed
36 |
39 |
43 |
44 | Result
40 | {{ result.result.label }}
41 | Positive | Negative
42 |
45 |
49 | Score
46 | {{ result.result.score|truncatechars:7 }}
47 | Between 0 to 1
48 | No previous results available.
54 | {% endfor %} 55 |
5 |
63 | {% endblock %}
64 |
--------------------------------------------------------------------------------
/Algolyzer/home/templates/home/onboarding.html:
--------------------------------------------------------------------------------
1 | {% extends "base.html" %}
2 |
3 | {% block content %}
4 | Posts in {{ category.name }}
6 | ← Back to Categories 7 | 8 | {% if user.is_authenticated %} 9 | 10 | 11 | 33 | {% endif %} 34 | 35 | {% for post in page_obj %} 36 | 37 |
38 |
43 |
44 | {% empty %}
45 |
39 |
41 | {{ post.title }}
40 |Votes: {{ post.total_votes|default:0 }}
42 |No posts available in this category.
46 | {% endfor %} 47 | 48 | 49 | 62 |
5 |
6 |
72 | {% endblock %}
73 |
--------------------------------------------------------------------------------
/Algolyzer/home/views.py:
--------------------------------------------------------------------------------
1 | from community.models import Comment, Post
2 | from django.contrib.auth.decorators import login_required
3 | from django.shortcuts import redirect, render
4 | from playground.models import PlaygroundTask
5 | from quiz.models import QuizProgress, Topic
6 |
7 | from .forms import OnboardingForm
8 | from .models import UserProfile
9 |
10 |
11 | def home(request):
12 | """Main Home Page"""
13 | return render(request, "home/home.html")
14 |
15 |
16 | @login_required
17 | def dashboard(request):
18 | """Dashboard page for logged-in users only"""
19 | # Fetch the user's profile
20 | user_profile = UserProfile.objects.filter(user=request.user).first()
21 |
22 | # Redirect to onboarding if no profile exists
23 | if not user_profile:
24 | return redirect("onboarding")
25 |
26 | # Calculate XP progress percentage
27 | next_level_xp = user_profile.get_next_level_xp()
28 | progress_percentage = (
29 | (user_profile.xp / next_level_xp) * 100 if next_level_xp else 0
30 | )
31 |
32 | # Fetch quiz progress data
33 | completed_quizzes = QuizProgress.objects.filter(
34 | user=request.user, completed=True
35 | ).count()
36 | total_quizzes = Topic.objects.count()
37 | recent_quizzes = QuizProgress.objects.filter(
38 | user=request.user, completed=True
39 | ).order_by("-id")[:5]
40 |
41 | # Fetch task data
42 | completed_tasks = PlaygroundTask.objects.filter(
43 | user=request.user, status="COMPLETED"
44 | ).count()
45 | pending_tasks = PlaygroundTask.objects.filter(
46 | user=request.user, status="PENDING"
47 | ).count()
48 | recent_tasks = PlaygroundTask.objects.filter(user=request.user).order_by(
49 | "-created_at"
50 | )[:5]
51 |
52 | # Fetch forum activity data
53 | total_posts = Post.objects.filter(author=request.user).count()
54 | total_comments = Comment.objects.filter(author=request.user).count()
55 | recent_posts = Post.objects.filter(author=request.user).order_by("-created_at")[:5]
56 | recent_comments = Comment.objects.filter(author=request.user).order_by(
57 | "-created_at"
58 | )[:5]
59 |
60 | # Prepare context
61 | context = {
62 | "user_profile": user_profile,
63 | "progress_percentage": progress_percentage, # XP progress percentage
64 | "completed_quizzes": completed_quizzes, # Number of completed quizzes
65 | "total_quizzes": total_quizzes, # Total number of quizzes
66 | "recent_quizzes": recent_quizzes, # Recent quizzes
67 | "completed_tasks": completed_tasks, # Number of completed tasks
68 | "pending_tasks": pending_tasks, # Number of pending tasks
69 | "recent_tasks": recent_tasks, # Recent tasks
70 | "total_posts": total_posts, # Total number of posts
71 | "total_comments": total_comments, # Total number of comments
72 | "recent_posts": recent_posts, # Recent posts
73 | "recent_comments": recent_comments, # Recent comments
74 | }
75 |
76 | return render(request, "home/dashboard.html", context=context)
77 |
78 |
79 | @login_required
80 | def onboarding(request):
81 | # Check if the UserProfile exists
82 | if UserProfile.objects.filter(user=request.user).exists():
83 | return redirect("dashboard") # Redirect if profile exists
84 |
85 | if request.method == "POST":
86 | # If the profile doesn't exist, no instance is passed to the form
87 | form = OnboardingForm(request.POST)
88 |
89 | if form.is_valid():
90 | # Create the UserProfile only after form validation
91 | UserProfile.objects.create(user=request.user, dob=form.cleaned_data["dob"])
92 | return redirect("dashboard")
93 | else:
94 | # On GET request, just create an empty form (no instance)
95 | form = OnboardingForm()
96 |
97 | return render(request, "home/onboarding.html", {"form": form})
98 |
--------------------------------------------------------------------------------
/docs/CONTRIBUTING.md:
--------------------------------------------------------------------------------
1 | # Contributing Guidelines
2 |
3 | Thank you for considering contributing to Algolyzer! We appreciate your help in improving our project. Please follow the steps below to ensure a smooth and efficient contribution process.
4 |
5 | 1. Fork the Repository
6 | - Click the "Fork" button at the top of the repository page to create a copy in your GitHub account.
7 |
8 | 2. Clone the Forked Repository
9 | - Use the following command to clone your fork locally (replace `your-username` with your actual github username):
10 |
11 | ```bash
12 | git clone https://github.com/Complete Your Profile
7 | 8 | 70 | 71 |
5 |
82 | {% endblock %}
83 |
--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | share/python-wheels/
24 | *.egg-info/
25 | .installed.cfg
26 | *.egg
27 | MANIFEST
28 |
29 | # PyInstaller
30 | # Usually these files are written by a python script from a template
31 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
32 | *.manifest
33 | *.spec
34 |
35 | # Installer logs
36 | pip-log.txt
37 | pip-delete-this-directory.txt
38 |
39 | # Unit test / coverage reports
40 | htmlcov/
41 | .tox/
42 | .nox/
43 | .coverage
44 | .coverage.*
45 | .cache
46 | nosetests.xml
47 | coverage.xml
48 | *.cover
49 | *.py,cover
50 | .hypothesis/
51 | .pytest_cache/
52 | cover/
53 |
54 | # Translations
55 | *.mo
56 | *.pot
57 |
58 | # Django stuff:
59 | *.log
60 | local_settings.py
61 | db.sqlite3
62 | db.sqlite3-journal
63 |
64 | # Flask stuff:
65 | instance/
66 | .webassets-cache
67 |
68 | # Scrapy stuff:
69 | .scrapy
70 |
71 | # Sphinx documentation
72 | docs/_build/
73 |
74 | # PyBuilder
75 | .pybuilder/
76 | target/
77 |
78 | # Jupyter Notebook
79 | .ipynb_checkpoints
80 |
81 | # IPython
82 | profile_default/
83 | ipython_config.py
84 |
85 | # pyenv
86 | # For a library or package, you might want to ignore these files since the code is
87 | # intended to run in multiple environments; otherwise, check them in:
88 | # .python-version
89 |
90 | # pipenv
91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies
93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not
94 | # install all needed dependencies.
95 | #Pipfile.lock
96 |
97 | # UV
98 | # Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
99 | # This is especially recommended for binary packages to ensure reproducibility, and is more
100 | # commonly ignored for libraries.
101 | #uv.lock
102 |
103 | # poetry
104 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
105 | # This is especially recommended for binary packages to ensure reproducibility, and is more
106 | # commonly ignored for libraries.
107 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
108 | #poetry.lock
109 |
110 | # pdm
111 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
112 | #pdm.lock
113 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
114 | # in version control.
115 | # https://pdm.fming.dev/latest/usage/project/#working-with-version-control
116 | .pdm.toml
117 | .pdm-python
118 | .pdm-build/
119 |
120 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
121 | __pypackages__/
122 |
123 | # Celery stuff
124 | celerybeat-schedule
125 | celerybeat.pid
126 |
127 | # SageMath parsed files
128 | *.sage.py
129 |
130 | # Environments
131 | .env
132 | .venv
133 | env/
134 | venv/
135 | ENV/
136 | env.bak/
137 | venv.bak/
138 |
139 | # Spyder project settings
140 | .spyderproject
141 | .spyproject
142 |
143 | # Rope project settings
144 | .ropeproject
145 |
146 | # mkdocs documentation
147 | /site
148 |
149 | # mypy
150 | .mypy_cache/
151 | .dmypy.json
152 | dmypy.json
153 |
154 | # Pyre type checker
155 | .pyre/
156 |
157 | # pytype static type analyzer
158 | .pytype/
159 |
160 | # Cython debug symbols
161 | cython_debug/
162 |
163 | # PyCharm
164 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can
165 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
166 | # and can be added to the global gitignore or merged into this file. For a more nuclear
167 | # option (not recommended) you can uncomment the following to ignore the entire idea folder.
168 | .idea/
169 |
170 | # For vscode users
171 | .vscode/
172 |
173 | # PyPI configuration file
174 | .pypirc
175 |
176 | # Node.js
177 | node_modules
178 |
179 | # TailwindCSS
180 | *output.css
181 |
182 | # productions static files
183 | staticfiles
184 |
185 | # algolyzer playground aiml models
186 | aiml_models
187 |
188 | doodles
--------------------------------------------------------------------------------
/Algolyzer/quiz/templates/quiz/home.html:
--------------------------------------------------------------------------------
1 | {% extends 'base.html' %}
2 |
3 | {% block content %}
4 | 6 | A Beginner's Guide to Doodle Classification 7 |
8 | 9 |Introduction
10 |11 | Doodle classification is a type of image classification that predicts which number (0-9) was drawn by a user. 12 | This is done using a deep learning model trained on the MNIST dataset—a famous dataset of handwritten digits. 13 |
14 |
15 | In this project, we're using a pre-trained model from Hugging Face called farleyknight/mnist-digit-classification-2022-09-04,
16 | which is specifically trained to recognize digits from rough sketches or "doodles."
17 |
How Does It Work?
20 |-
21 |
- The user draws a digit on a canvas in the browser. 22 |
- The drawing is converted to a base64 PNG image. 23 |
- This image is sent to the server and saved temporarily. 24 |
- The image is passed through a deep learning model. 25 |
- The model outputs the most likely digit with a confidence score. 26 |
Step-by-Step Breakdown
29 | 30 |Step 1: Capture the Drawing
31 |32 | We use an HTML canvas where users draw their digit. The drawing is captured as a base64-encoded image string. 33 |
34 | 35 |
36 |
38 |
39 | image_data = request.POST.get("image_data")Step 2: Save the Drawing as an Image
40 |41 | The base64 string is decoded and saved as a PNG image, so that it can be read like a normal image file. 42 |
43 | 44 |
45 |
49 |
50 | format, imgstr = image_data.split(";base64,")
46 | ext = format.split("/")[-1]
47 | image_file = ContentFile(base64.b64decode(imgstr), name=f"doodle.{ext}")Step 3: Run the Image through the Classifier
51 |52 | The image is passed into a pre-trained model that analyzes the pixel patterns and predicts which digit was drawn. 53 |
54 | 55 |
56 |
59 |
60 | image = Image.open(task.input_image.path)
57 | result = mnist_classifier(image)Step 4: Get the Best Prediction
61 |62 | The model returns a list of possible digits with scores. We choose the one with the highest score. 63 |
64 | 65 |
66 |
69 |
70 | best_prediction = max(result, key=lambda x: x["score"])
67 | best_label = best_prediction["label"]Why This Model?
71 |-
72 |
- ✅ Trained on real-world digit images from the MNIST dataset. 73 |
- ✅ Fast and lightweight — ideal for web apps and educational tools. 74 |
- ✅ Easy to integrate using Hugging Face’s
pipelineinterface.
75 |
Try Drawing!
78 |79 | Head over to the classifier page and draw a number to see the magic happen in real-time! 🎨 80 |
81 |
5 |
100 |
101 | {% endblock content %}
--------------------------------------------------------------------------------
/Algolyzer/study/templates/study/k_means_clustering.html:
--------------------------------------------------------------------------------
1 | {% extends "base.html" %}
2 |
3 | {% block content %}
4 | Available Quiz Topics
6 | 7 |-
8 |
9 | {% for item in data %}
10 |
11 | {% comment %}
12 | {{ topic.name }}
13 | {% endcomment %}
14 |
-
15 |
16 | {% if not forloop.first %}
17 |
18 | {% endif %} 19 | 20 |21 |76 |22 | 23 |75 |25 | 26 | {{ item.topic.name }} 27 | 28 |29 |31 | 32 | {% comment %} Detailed Card {% endcomment %} 33 |74 |34 |73 |35 |59 | 60 | 61 | 72 |36 |42 | 43 |Your Score37 | 38 |{{item.score}}39 | 40 |Excellent41 |44 |50 | 51 |Total Questions45 | 46 |{{item.total}}47 | 48 |↗︎ 400 (22%)49 |52 |58 |Difficulty53 | 54 |{{item.topic.difficulty}}55 | 56 |↘︎ 90 (14%)57 |77 | 87 |88 | 89 | {% if not forloop.last %} 90 |
91 | {% endif %} 92 | 93 |
94 |
95 | {% empty %}
96 |
No topics available at the moment. Please check back later!
97 | {% endfor %} 98 |
5 |
91 | {% endblock %}
92 |
--------------------------------------------------------------------------------
/Algolyzer/community/migrations/0001_initial.py:
--------------------------------------------------------------------------------
1 | # Generated by Django 5.1.4 on 2025-02-02 09:59
2 |
3 | import django.db.models.deletion
4 | from django.conf import settings
5 | from django.db import migrations, models
6 |
7 |
8 | class Migration(migrations.Migration):
9 | initial = True
10 |
11 | dependencies = [
12 | migrations.swappable_dependency(settings.AUTH_USER_MODEL),
13 | ]
14 |
15 | operations = [
16 | migrations.CreateModel(
17 | name="Category",
18 | fields=[
19 | (
20 | "id",
21 | models.BigAutoField(
22 | auto_created=True,
23 | primary_key=True,
24 | serialize=False,
25 | verbose_name="ID",
26 | ),
27 | ),
28 | ("name", models.CharField(max_length=255, unique=True)),
29 | ("description", models.TextField(blank=True)),
30 | ],
31 | ),
32 | migrations.CreateModel(
33 | name="Post",
34 | fields=[
35 | (
36 | "id",
37 | models.BigAutoField(
38 | auto_created=True,
39 | primary_key=True,
40 | serialize=False,
41 | verbose_name="ID",
42 | ),
43 | ),
44 | ("title", models.CharField(max_length=255)),
45 | ("content", models.TextField()),
46 | ("created_at", models.DateTimeField(auto_now_add=True)),
47 | ("updated_at", models.DateTimeField(auto_now=True)),
48 | (
49 | "author",
50 | models.ForeignKey(
51 | on_delete=django.db.models.deletion.CASCADE,
52 | to=settings.AUTH_USER_MODEL,
53 | ),
54 | ),
55 | (
56 | "category",
57 | models.ForeignKey(
58 | on_delete=django.db.models.deletion.CASCADE,
59 | to="community.category",
60 | ),
61 | ),
62 | ],
63 | ),
64 | migrations.CreateModel(
65 | name="Comment",
66 | fields=[
67 | (
68 | "id",
69 | models.BigAutoField(
70 | auto_created=True,
71 | primary_key=True,
72 | serialize=False,
73 | verbose_name="ID",
74 | ),
75 | ),
76 | ("content", models.TextField()),
77 | ("created_at", models.DateTimeField(auto_now_add=True)),
78 | (
79 | "author",
80 | models.ForeignKey(
81 | on_delete=django.db.models.deletion.CASCADE,
82 | to=settings.AUTH_USER_MODEL,
83 | ),
84 | ),
85 | (
86 | "post",
87 | models.ForeignKey(
88 | on_delete=django.db.models.deletion.CASCADE,
89 | related_name="comments",
90 | to="community.post",
91 | ),
92 | ),
93 | ],
94 | ),
95 | migrations.CreateModel(
96 | name="Vote",
97 | fields=[
98 | (
99 | "id",
100 | models.BigAutoField(
101 | auto_created=True,
102 | primary_key=True,
103 | serialize=False,
104 | verbose_name="ID",
105 | ),
106 | ),
107 | (
108 | "vote_type",
109 | models.IntegerField(choices=[(1, "Upvote"), (-1, "Downvote")]),
110 | ),
111 | (
112 | "post",
113 | models.ForeignKey(
114 | on_delete=django.db.models.deletion.CASCADE,
115 | related_name="votes",
116 | to="community.post",
117 | ),
118 | ),
119 | (
120 | "user",
121 | models.ForeignKey(
122 | on_delete=django.db.models.deletion.CASCADE,
123 | to=settings.AUTH_USER_MODEL,
124 | ),
125 | ),
126 | ],
127 | options={
128 | "unique_together": {("post", "user")},
129 | },
130 | ),
131 | ]
132 |
--------------------------------------------------------------------------------
/Algolyzer/study/templates/study/linear_regression.html:
--------------------------------------------------------------------------------
1 |
2 | {% extends "base.html" %}
3 |
4 | {% block content %}
5 | 6 | A Beginner's Guide to K-Means Clustering 7 |
8 | 9 |Introduction
10 |11 | K-Means clustering is an unsupervised learning algorithm that groups data into clusters based on similarity. 12 | It finds a set of "centroids" that best represent the data points in each cluster. 13 |
14 |15 | In this tool, you enter a list of numbers, and the algorithm will group them into up to three clusters. 16 |
17 | 18 |How Does It Work?
19 |-
20 |
- You input several numbers (e.g., 2, 8, 3, 15, 6). 21 |
- The server reads and parses these numbers into a list. 22 |
- K-Means places cluster centers and assigns each number to the nearest center. 23 |
- The centers move to the average of their assigned points until stable. 24 |
- The final clusters and their labels are shown. 25 |
Step-by-Step Breakdown
28 | 29 |Step 1: Read and Parse Input
30 |31 | We get the raw JSON string of numbers from the form and convert it into a Python list. 32 |
33 |
34 |
39 |
40 | import json
35 |
36 | values_json = request.POST.get("values", "[]")
37 | input_values = json.loads(values_json) # e.g., [2, 8, 3, 15, 6]Step 2: Prepare Data for K-Means
41 |42 | We convert the list of numbers into a NumPy array and reshape it so each number is its own data point. 43 |
44 |
45 |
49 |
50 | import numpy as np
46 |
47 | X = np.array(input_values).reshape(-1, 1) # e.g., [[2], [8], [3], [15], [6]]Step 3: Choose Number of Clusters
51 |52 | We allow up to 3 clusters but never more than the number of points. 53 |
54 |
55 |
60 |
61 | from sklearn.cluster import KMeans
56 |
57 | n_clusters = min(len(input_values), 3)
58 | kmeans = KMeans(n_clusters=n_clusters, random_state=42)Step 4: Run K-Means
62 |63 | We fit the model to our data and get a label for each point. 64 |
65 |
66 |
69 |
70 | kmeans.fit(X)
67 | labels = kmeans.labels_.tolist() # e.g., [0, 1, 0, 2, 0]Step 5: Pair Values with Cluster Labels
71 |72 | We zip each original number with its cluster label for easy display. 73 |
74 |
75 |
78 |
79 | classified_values = list(zip(input_values, labels))
76 | # e.g., [(2,0), (8,1), (3,0), (15,2), (6,0)]Why K-Means?
80 |-
81 |
- ✅ Easy to understand and implement. 82 |
- ✅ Fast and scales well with data size. 83 |
- ✅ Good for discovering hidden groupings in your data. 84 |
Try Clustering!
87 |88 | Visit the K-Means Clustering page, input a few numbers separated by commas, and see how they group together! 🧩 89 |
90 |
6 |
96 | {% endblock %}
97 |
--------------------------------------------------------------------------------
/Algolyzer/community/views.py:
--------------------------------------------------------------------------------
1 | from django.contrib.auth.decorators import login_required
2 | from django.core.paginator import Paginator
3 | from django.db.models import Sum
4 | from django.http import HttpResponseForbidden
5 | from django.shortcuts import get_object_or_404, redirect, render
6 | from home.decorators import profile_required
7 |
8 | from .models import Category, Comment, Post, Vote
9 |
10 |
11 | def category_list(request):
12 | """Show all categories."""
13 | categories = Category.objects.all()
14 | return render(request, "community/category_list.html", {"categories": categories})
15 |
16 |
17 | def post_list_by_category(request, category_id):
18 | """Show posts in a category and allow creating new posts."""
19 | category = get_object_or_404(Category, id=category_id)
20 |
21 | if request.method == "POST" and request.user.is_authenticated:
22 | title = request.POST.get("title")
23 | content = request.POST.get("content")
24 | if title and content:
25 | Post.objects.create(
26 | title=title, content=content, author=request.user, category=category
27 | )
28 | return redirect("post_list_by_category", category_id=category.id)
29 |
30 | # For normal get request
31 | category = get_object_or_404(Category, id=category_id)
32 | posts = (
33 | Post.objects.filter(category=category)
34 | .annotate(total_votes=Sum("votes__vote_type"))
35 | .order_by("-total_votes", "-created_at")
36 | )
37 |
38 | paginator = Paginator(posts, 5) # Show 5 posts per page
39 | page_number = request.GET.get("page")
40 | page_obj = paginator.get_page(page_number)
41 |
42 | return render(
43 | request,
44 | "community/post_list.html",
45 | {"category": category, "page_obj": page_obj},
46 | )
47 |
48 |
49 | def post_detail(request, post_id):
50 | post = get_object_or_404(Post, id=post_id)
51 |
52 | # Delete Post
53 | if (
54 | request.method == "POST"
55 | and request.POST.get("_method") == "DELETE"
56 | and request.user.is_authenticated
57 | ):
58 | if request.user == post.author:
59 | post.delete()
60 | return redirect("post_list_by_category", category_id=post.category.id)
61 | else:
62 | return HttpResponseForbidden("You can only delete your own posts.")
63 |
64 | # Update Post
65 | if request.method == "POST" and request.user.is_authenticated:
66 | if request.user == post.author:
67 | title = request.POST.get("title")
68 | content = request.POST.get("content")
69 | if title and content:
70 | post.title = title
71 | post.content = content
72 | post.save()
73 | return redirect("post_detail", post_id=post.id)
74 | else:
75 | return HttpResponseForbidden("You can only edit your own posts.")
76 |
77 | # Read posts
78 | """Show post details along with comments and voting functionality."""
79 | comments = post.comments.all()
80 | total_votes = post.votes.aggregate(Sum("vote_type"))["vote_type__sum"] or 0
81 |
82 | return render(
83 | request,
84 | "community/post_detail.html",
85 | {"post": post, "comments": comments, "total_votes": total_votes},
86 | )
87 |
88 |
89 | @login_required
90 | @profile_required
91 | def add_comment(request, post_id):
92 | """Allow users to add comments to a post."""
93 | post = get_object_or_404(Post, id=post_id)
94 |
95 | if request.method == "POST":
96 | content = request.POST.get("content")
97 | if content:
98 | Comment.objects.create(post=post, author=request.user, content=content)
99 |
100 | return redirect("post_detail", post_id=post.id)
101 |
102 |
103 | @login_required
104 | @profile_required
105 | def vote_post(request, post_id, vote_type):
106 | post = get_object_or_404(Post, id=post_id)
107 | vote_type = int(vote_type)
108 |
109 | # Ensure vote_type is either 1 (upvote) or -1 (downvote)
110 | if vote_type not in [1, -1]:
111 | return HttpResponseForbidden("Invalid vote type.")
112 |
113 | # Check if the user has already voted on this post
114 | existing_vote = Vote.objects.filter(post=post, user=request.user).first()
115 |
116 | if existing_vote:
117 | if existing_vote.vote_type == vote_type:
118 | # If the user clicks the same vote again, remove the vote (unvote)
119 | existing_vote.delete()
120 | else:
121 | # Otherwise, update the vote
122 | existing_vote.vote_type = vote_type
123 | existing_vote.save()
124 | else:
125 | # Create a new vote if none exists
126 | Vote.objects.create(post=post, user=request.user, vote_type=vote_type)
127 |
128 | return redirect("post_detail", post_id=post.id)
129 |
--------------------------------------------------------------------------------
/Algolyzer/quiz/templates/quiz/topic.html:
--------------------------------------------------------------------------------
1 | {% extends "base.html" %}
2 |
3 | {% block content %}
4 |
5 | 7 | A Beginner's Guide to Linear Regression 8 |
9 | 10 |Introduction
11 |12 | Linear regression is a basic machine learning method that finds a straight line (relationship) between input data and output values. 13 | In simple terms, it learns how one variable changes when another changes and uses that to make predictions. 14 |
15 |16 | Here, we collect five numeric inputs from sliders (values 1–5) and predict a sixth value based on the trends in those inputs. 17 |
18 | 19 |How Does It Work?
20 |-
21 |
- User moves five sliders to choose numbers. 22 |
- Values are sent to the server when the form is submitted. 23 |
- A straight-line model is trained on the five numbers. 24 |
- The model predicts the value at position 6. 25 |
- We display the predicted value along with the line’s equation. 26 |
Step-by-Step Breakdown
29 | 30 |Step 1: Collect Input Data
31 |32 | We read the slider values from the form submission using Django’s request object. 33 |
34 |
35 |
43 |
44 | values = [
36 | float(request.POST.get("value_1", 0)),
37 | float(request.POST.get("value_2", 0)),
38 | float(request.POST.get("value_3", 0)),
39 | float(request.POST.get("value_4", 0)),
40 | float(request.POST.get("value_5", 0)),
41 | ]Step 2: Prepare Data for Training
45 |46 | We create an array of positions (1–5) and another array of the slider values. This shapes the data for the model. 47 |
48 |
49 |
54 |
55 | import numpy as np
50 |
51 | X = np.array(range(1, 6)).reshape(-1, 1) # Positions 1 to 5
52 | y = np.array(values) # The values from slidersStep 3: Train the Model
56 |57 | We use scikit-learn’s LinearRegression to learn the best-fit line through our data points. 58 |
59 |
60 |
65 |
66 | from sklearn.linear_model import LinearRegression
61 |
62 | model = LinearRegression()
63 | model.fit(X, y)Step 4: Predict the Next Value
67 |68 | With the trained model, we predict what the value would be at position 6. 69 |
70 |
71 |
74 |
75 | next_index = np.array([[6]])
72 | predicted_value = model.predict(next_index)[0]Step 5: Get the Line Equation
76 |77 | We extract the slope (change rate) and intercept (starting point) to show the line’s formula. 78 |
79 |
80 |
83 |
84 | slope = model.coef_[0]
81 | intercept = model.intercept_Why Linear Regression?
85 |-
86 |
- ✅ Simple and interpretable — you see the actual equation. 87 |
- ✅ Fast to train and requires little data. 88 |
- ✅ Great starting point for understanding how prediction works. 89 |
Try It Yourself!
92 |93 | Go to the Linear Regression page, move the sliders, and watch your predicted point update! 📈 94 |
95 |
6 | {% comment %} Stats Bar
7 |
90 |
91 |
92 | {% endblock content %}
--------------------------------------------------------------------------------
/Algolyzer/templates/account/signup.html:
--------------------------------------------------------------------------------
1 | {% extends "base.html" %}
2 | {% load allauth i18n %}
3 | {% block head_title %}
4 | {% trans "Signup" %}
5 | {% endblock head_title %}
6 |
7 | {% block content %}
8 |
8 |
{% endcomment %}
61 |
62 |
9 |
60 |
10 |
27 |
28 |
11 |
22 |
23 | Total Likes
24 | 25.6K
25 | 21% more than last month
26 |
29 |
46 |
47 |
30 |
41 |
42 | Page Views
43 | 2.6M
44 | 21% more than last month
45 |
48 |
59 |
49 |
55 |
50 |
54 |
51 | 
52 |
53 |
52 | 86%
56 | Reach
57 | Better than Before
58 |
63 |
87 |
88 |
89 |
64 | {% comment %} Rules {% endcomment %}
65 |
85 | Start The Challenge
86 |
66 |
76 |
77 | {% comment %} Topic Overview {% endcomment %}
78 |
67 |
75 | Rules
68 |-
69 |
- There are {{total}} No of questions in this Quiz. 70 |
- Each right answer gives 2 XP points. 71 |
- There is NO time limit so answer without hasting 72 |
- Your Score will be evaluated at the end of the Quiz 73 |
79 |
84 |
80 |
83 | Overview
81 |Artificial Intelligence (AI) and Machine Learning (ML) are transforming the way we interact with technology, enabling systems to learn from data and make intelligent decisions. This AI-ML quiz is designed to test your understanding of fundamental concepts such as supervised and unsupervised learning, neural networks, natural language processing, and deep learning. Whether you're a beginner exploring the basics or an advanced learner diving into complex algorithms, this quiz will challenge your knowledge and help you assess your grasp of key AI-ML principles. Get ready to sharpen your skills and see how well you understand the world of intelligent machines! 🚀
82 |
9 |
62 | {% endblock content %}
63 |
64 | {% block scripts %}
65 |
88 | {% endblock %}
89 |
--------------------------------------------------------------------------------
/Algolyzer/playground/templates/playground/doodle_classifier.html:
--------------------------------------------------------------------------------
1 | {% extends "base.html" %}
2 |
3 | {% block content %}
4 |
10 |
61 |
11 |
60 | 12 | {% trans "Sign Up" %} 13 |
14 |15 | {% blocktranslate %} 16 | Already have an account? Then please 17 | sign in. 18 | {% endblocktranslate %} 19 |
20 | 21 | {% if not SOCIALACCOUNT_ONLY %} 22 | 58 | {% endif %} 59 |
5 |
65 |
66 |
111 | {% endblock %}
--------------------------------------------------------------------------------
/Algolyzer/study/templates/study/svm_regression.html:
--------------------------------------------------------------------------------
1 | {% extends "base.html" %}
2 |
3 | {% block content %}
4 |
6 |
64 | Doodle Classifier
7 | 8 | 9 |
10 |
11 |
12 |
13 |
14 |
15 |
16 |
17 |
18 |
19 |
20 |
24 |
25 |
26 |
27 |
63 | Previous Results
28 |
29 | {% for result in previous_results %}
30 |
62 |
31 |
32 |
58 | {% empty %}
59 |
33 | Submitted Doodle
34 |
35 |
36 |
37 |
57 |
38 |
56 |
39 |
43 |
44 | Status
40 | {{ result.status }}
41 | Failed | Completed
42 |
45 |
49 |
50 |
55 | Prediction
46 | {{ result.result }}
47 | Number 0 to 9
48 | No previous results available.
60 | {% endfor %} 61 |
5 |
103 | {% endblock %}
104 |
--------------------------------------------------------------------------------
/Algolyzer/quiz/migrations/0001_initial.py:
--------------------------------------------------------------------------------
1 | # Generated by Django 5.1.4 on 2025-01-12 13:17
2 |
3 | import django.db.models.deletion
4 | from django.conf import settings
5 | from django.db import migrations, models
6 |
7 |
8 | class Migration(migrations.Migration):
9 | initial = True
10 |
11 | dependencies = [
12 | migrations.swappable_dependency(settings.AUTH_USER_MODEL),
13 | ]
14 |
15 | operations = [
16 | migrations.CreateModel(
17 | name="Topic",
18 | fields=[
19 | (
20 | "id",
21 | models.BigAutoField(
22 | auto_created=True,
23 | primary_key=True,
24 | serialize=False,
25 | verbose_name="ID",
26 | ),
27 | ),
28 | ("name", models.CharField(max_length=100)),
29 | ],
30 | ),
31 | migrations.CreateModel(
32 | name="QuizProgress",
33 | fields=[
34 | (
35 | "id",
36 | models.BigAutoField(
37 | auto_created=True,
38 | primary_key=True,
39 | serialize=False,
40 | verbose_name="ID",
41 | ),
42 | ),
43 | ("current_question", models.IntegerField(default=0)),
44 | ("completed", models.BooleanField(default=False)),
45 | (
46 | "user",
47 | models.ForeignKey(
48 | on_delete=django.db.models.deletion.CASCADE,
49 | to=settings.AUTH_USER_MODEL,
50 | ),
51 | ),
52 | (
53 | "topic",
54 | models.ForeignKey(
55 | on_delete=django.db.models.deletion.CASCADE, to="quiz.topic"
56 | ),
57 | ),
58 | ],
59 | ),
60 | migrations.CreateModel(
61 | name="Question",
62 | fields=[
63 | (
64 | "id",
65 | models.BigAutoField(
66 | auto_created=True,
67 | primary_key=True,
68 | serialize=False,
69 | verbose_name="ID",
70 | ),
71 | ),
72 | ("text", models.TextField()),
73 | ("option_a", models.CharField(max_length=200)),
74 | ("option_b", models.CharField(max_length=200)),
75 | ("option_c", models.CharField(max_length=200)),
76 | ("option_d", models.CharField(max_length=200)),
77 | (
78 | "correct_answer",
79 | models.CharField(
80 | choices=[
81 | ("a", "Option A"),
82 | ("b", "Option B"),
83 | ("c", "Option C"),
84 | ("d", "Option D"),
85 | ],
86 | help_text="Correct answer should be 'a', 'b', 'c', or 'd'",
87 | max_length=1,
88 | ),
89 | ),
90 | (
91 | "topic",
92 | models.ForeignKey(
93 | on_delete=django.db.models.deletion.CASCADE, to="quiz.topic"
94 | ),
95 | ),
96 | ],
97 | ),
98 | migrations.CreateModel(
99 | name="UserAnswer",
100 | fields=[
101 | (
102 | "id",
103 | models.BigAutoField(
104 | auto_created=True,
105 | primary_key=True,
106 | serialize=False,
107 | verbose_name="ID",
108 | ),
109 | ),
110 | (
111 | "selected_answer",
112 | models.CharField(
113 | choices=[
114 | ("a", "Option A"),
115 | ("b", "Option B"),
116 | ("c", "Option C"),
117 | ("d", "Option D"),
118 | ],
119 | help_text="Selected answer should be 'a', 'b', 'c', or 'd'",
120 | max_length=1,
121 | ),
122 | ),
123 | ("is_correct", models.BooleanField(default=False)),
124 | (
125 | "question",
126 | models.ForeignKey(
127 | on_delete=django.db.models.deletion.CASCADE, to="quiz.question"
128 | ),
129 | ),
130 | (
131 | "topic",
132 | models.OneToOneField(
133 | on_delete=django.db.models.deletion.CASCADE, to="quiz.topic"
134 | ),
135 | ),
136 | (
137 | "user",
138 | models.ForeignKey(
139 | on_delete=django.db.models.deletion.CASCADE,
140 | to=settings.AUTH_USER_MODEL,
141 | ),
142 | ),
143 | ],
144 | ),
145 | ]
146 |
--------------------------------------------------------------------------------
/Algolyzer/playground/templates/playground/linear_regression.html:
--------------------------------------------------------------------------------
1 | {% extends "base.html" %}
2 |
3 | {% block extra_head %}
4 |
5 | {% endblock %}
6 |
7 | {% block content %}
8 | 6 | A Beginner's Guide to SVM Regression 7 |
8 | 9 |Introduction
10 |11 | Support Vector Machine (SVM) Regression is a method that finds a function 12 | to predict continuous values (like temperature or humidity) based on past data. 13 | It works well even when the relationship between inputs and outputs is complex. 14 |
15 |16 | In this example, we use previous temperature and humidity readings 17 | to predict the next temperature and humidity values. 18 |
19 | 20 |How Does It Work?
21 |-
22 |
- You input a list of temperature-humidity pairs (e.g., [[22,55], [23,60], [24,65]]). 23 |
- The server converts this list into a format the model understands. 24 |
- We train two SVM models: one to predict temperature, another for humidity. 25 |
- The models use all but the last pair to learn patterns. 26 |
- We predict the next temperature and humidity for the final pair. 27 |
Step-by-Step Breakdown
30 | 31 |Step 1: Read the Input Values
32 |33 | We get the JSON string of pairs from the form and convert it into a Python list. 34 |
35 |
36 |
41 |
42 | import json
37 |
38 | values_json = request.POST.get("values", "[]")
39 | input_values = json.loads(values_json) # e.g., [[22,55], [23,60], [24,65]]Step 2: Prepare Data for SVM
43 |44 | We convert the list to a NumPy array and split it into features and targets. 45 | Features (X) are all pairs except the last. Targets (y) are temperature and humidity separately. 46 |
47 |
48 |
55 |
56 | import numpy as np
49 |
50 | data = np.array(input_values) # shape: (n, 2)
51 | X = data[:-1] # all but last for training
52 | y_temp = data[1:, 0] # target: next temperatures
53 | y_hum = data[1:, 1] # target: next humiditiesStep 3: Train SVM Models
57 |58 | We create two SVR (Support Vector Regression) models and fit them on our training data. 59 |
60 |
61 |
68 |
69 | from sklearn.svm import SVR
62 |
63 | svr_temp = SVR(kernel="rbf", C=100, gamma="scale")
64 | svr_hum = SVR(kernel="rbf", C=100, gamma="scale")
65 | svr_temp.fit(X, y_temp)
66 | svr_hum.fit(X, y_hum)Step 4: Predict the Next Values
70 |71 | We take the last input pair and ask both models to predict the next temperature and humidity. 72 |
73 |
74 |
78 |
79 | last_pair = data[-1].reshape(1, -1)
75 | predicted_temp = svr_temp.predict(last_pair)[0]
76 | predicted_hum = svr_hum.predict(last_pair)[0]Step 5: Display the Predictions
80 |81 | We round the predictions and send them back to the template for display. 82 |
83 |
84 |
90 |
91 | pred_temp = round(predicted_temp, 2)
85 | pred_hum = round(predicted_hum, 2)87 | Then render these in the Django template 88 |
89 |Why SVM Regression?
92 |-
93 |
- ✅ Works well with small to medium-sized datasets. 94 |
- ✅ Can model complex, non-linear relationships. 95 |
- ✅ More robust to outliers than simple linear regression. 96 |
Give It a Try!
99 |100 | Visit the SVM Regression page, enter a JSON list of [temp, hum] pairs, and see your next prediction! 🌡️💧 101 |
102 |
9 |
128 | {% endblock %}
--------------------------------------------------------------------------------
/Algolyzer/quiz/views.py:
--------------------------------------------------------------------------------
1 | from django.contrib.auth.decorators import login_required
2 | from django.shortcuts import get_object_or_404, redirect, render
3 | from home.decorators import profile_required
4 | from home.models import UserProfile
5 |
6 | from .forms import QuizForm
7 | from .models import Question, QuizProgress, Topic, UserAnswer
8 |
9 |
10 | @login_required
11 | @profile_required
12 | def quiz_home(request):
13 | """Main QUIZ Home Page"""
14 | data = []
15 | topics = Topic.objects.all()
16 | for topic in topics:
17 | score = 0
18 | total = Question.objects.filter(topic=topic).count()
19 | progress = QuizProgress.objects.filter(user=request.user, topic=topic).first()
20 | if progress is not None and progress.completed:
21 | answers = UserAnswer.objects.filter(user=request.user, topic=topic)
22 | score = answers.filter(is_correct=True).count()
23 | data.append(
24 | {
25 | "topic": topic,
26 | "score": score,
27 | "total": total,
28 | }
29 | )
30 |
31 | context = {"data": data}
32 | return render(request, "quiz/home.html", context=context)
33 |
34 |
35 | @login_required
36 | @profile_required
37 | def quiz_topic(request, topic_id):
38 | topic = get_object_or_404(Topic, id=topic_id)
39 | score = 0
40 | total = 0
41 | progress = QuizProgress.objects.filter(user=request.user, topic=topic).first()
42 | if progress is not None and progress.completed:
43 | answers = UserAnswer.objects.filter(user=request.user, topic=topic)
44 | score = answers.filter(is_correct=True).count()
45 | total = Question.objects.filter(topic=topic).count()
46 |
47 | context = {
48 | "topic": topic,
49 | "score": score,
50 | "total": total,
51 | }
52 | return render(request, "quiz/topic.html", context=context)
53 |
54 |
55 | @login_required
56 | @profile_required
57 | def quiz_start(request, topic_id):
58 | """
59 | Resets user progress to start a fresh quiz
60 | """
61 | topic = get_object_or_404(Topic, id=topic_id)
62 | questions = Question.objects.filter(topic=topic)
63 | if not questions.exists():
64 | return render(request, "quiz/no_questions.html")
65 |
66 | # Initialize or reset progress
67 | QuizProgress.objects.filter(user=request.user, topic=topic).delete()
68 | QuizProgress.objects.create(user=request.user, topic=topic, current_question=0)
69 | UserAnswer.objects.filter(user=request.user, topic=topic).delete()
70 | return redirect("quiz_question", topic_id=topic.id)
71 |
72 |
73 | @login_required
74 | @profile_required
75 | def quiz_question(request, topic_id):
76 | topic = get_object_or_404(Topic, id=topic_id)
77 | progress = get_object_or_404(QuizProgress, user=request.user, topic=topic)
78 |
79 | # Fetch the current question
80 | questions = Question.objects.filter(topic=topic)
81 | if progress.current_question >= len(questions):
82 | progress.completed = True
83 | progress.save()
84 | return redirect("quiz_results", topic_id=topic.id)
85 |
86 | question = questions[progress.current_question]
87 |
88 | if request.method == "POST":
89 | form = QuizForm(question, request.POST)
90 | if form.is_valid():
91 | selected_answer = form.cleaned_data["answer"]
92 |
93 | # Save user's answer, is_correct field is managed internally in model.
94 | UserAnswer.objects.create(
95 | topic=topic,
96 | user=request.user,
97 | question=question,
98 | selected_answer=selected_answer,
99 | )
100 |
101 | # Move to the next question
102 | progress.current_question += 1
103 | progress.save()
104 | return redirect("quiz_question", topic_id=topic.id)
105 | else:
106 | form = QuizForm(question)
107 |
108 | return render(
109 | request,
110 | "quiz/question.html",
111 | {"form": form, "progress": progress, "total_array": range(len(questions))},
112 | )
113 |
114 |
115 | def quiz_results(request, topic_id):
116 | topic = get_object_or_404(Topic, id=topic_id)
117 | quiz_progress = get_object_or_404(QuizProgress, topic=topic, user=request.user)
118 | if not quiz_progress.completed:
119 | return redirect("quiz_question", topic_id=topic.id)
120 |
121 | answers = UserAnswer.objects.filter(user=request.user, topic=topic)
122 | for answer in answers:
123 | # Add full option text to each answer
124 | answer.selected_option_text = answer.question.get_option_text(
125 | answer.selected_answer
126 | )
127 | answer.correct_option_text = answer.question.get_option_text(
128 | answer.question.correct_answer
129 | )
130 |
131 | score = answers.filter(is_correct=True).count()
132 | total = Question.objects.filter(topic=topic).count()
133 | XP_REWARDS = {
134 | "Easy": 2,
135 | "Medium": 5,
136 | "Hard": 10,
137 | "Veteran": 20,
138 | }
139 | # Add XP to the user's profile based on the topic difficulty
140 | user_profile = UserProfile.objects.get(user=request.user)
141 | xp_to_add = (
142 | XP_REWARDS.get(topic.difficulty, 0) * score
143 | ) # Default to 0 if difficulty is not found
144 | user_profile.add_xp(xp_to_add)
145 |
146 | context = {
147 | "topic": topic,
148 | "score": score,
149 | "total": total,
150 | "answers": answers,
151 | "xp": xp_to_add,
152 | }
153 |
154 | return render(request, "quiz/results.html", context=context)
155 |
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/Algolyzer/quiz/templates/quiz/results.html:
--------------------------------------------------------------------------------
1 | {% extends 'base.html' %}
2 |
3 | {% block content %}
4 |
10 |
127 | Linear Regression
11 | 12 | 13 | 37 | 38 | {% if predicted_value %} 39 |
40 |
45 | {% endif %}
46 |
47 | {% if show_chart %}
48 |
41 |
44 | Input Values: {{ input_values|join:", " }}
42 |Prediction: The next value is {{ predicted_value }}.
43 |
49 |
56 |
57 | {% block scripts %}
58 |
123 | {% endblock %}
124 | {% endif %}
125 |
126 |
50 |
55 |
51 |
54 | Linear Regression Chart
52 | 53 |
5 |
63 | {% endblock %}
64 |
65 | {% block scripts %}
66 |
111 | {% endblock %}
--------------------------------------------------------------------------------
/Algolyzer/playground/templates/playground/kmeans_clustering.html:
--------------------------------------------------------------------------------
1 | {% extends "base.html" %}
2 |
3 | {% block extra_head %}
4 |
5 | {% endblock %}
6 |
7 | {% block content %}
8 |
6 |
62 | 🎊 Congratulations! 🎊
7 |
8 |
61 |
9 |
21 |
22 |
23 |
10 |
20 |
11 |
19 | Your Score
12 |
13 |
14 |
15 |
16 |
17 | Experience earned: +{{ xp }}
18 |
24 | {% for answer in answers %}
25 |
60 |
26 |
27 |
35 |
58 | {% endfor %}
59 |
36 |
57 |
37 |
56 |
38 |
55 | 39 | Your Answer: 40 | 41 | {{ answer.get_selected_answer_display }} - 42 | {{ answer.selected_option_text }} 43 | 44 |
45 | {% if not answer.is_correct %} 46 |47 | Correct Answer: 48 | 49 | {{ answer.question.get_correct_answer_display }} - 50 | {{ answer.correct_option_text }} 51 | 52 |
53 | {% endif %} 54 |
9 |
64 | {% endblock %}
65 |
66 | {% block scripts %}
67 |
173 | {% endblock %}
--------------------------------------------------------------------------------
/Algolyzer/data/quiz_data.json:
--------------------------------------------------------------------------------
1 | [
2 | {
3 | "model": "quiz.question",
4 | "pk": 1,
5 | "fields": {
6 | "topic": 1,
7 | "text": "What is Artificial Intelligence (AI)?",
8 | "option_a": "A branch of science focused on studying human psychology",
9 | "option_b": "The process of designing computer games",
10 | "option_c": "The simulation of human intelligence in machines",
11 | "option_d": "The development of physical robots only",
12 | "correct_answer": "c"
13 | }
14 | },
15 | {
16 | "model": "quiz.question",
17 | "pk": 2,
18 | "fields": {
19 | "topic": 1,
20 | "text": "Which of the following is NOT a type of machine learning?",
21 | "option_a": "Supervised learning",
22 | "option_b": "Unsupervised learning",
23 | "option_c": "Reinforcement learning",
24 | "option_d": "Data modeling",
25 | "correct_answer": "d"
26 | }
27 | },
28 | {
29 | "model": "quiz.question",
30 | "pk": 3,
31 | "fields": {
32 | "topic": 1,
33 | "text": "What distinguishes Machine Learning from traditional programming?",
34 | "option_a": "Machine Learning models rely on fixed rules defined by humans",
35 | "option_b": "Machine Learning models learn patterns from data",
36 | "option_c": "Machine Learning does not require any data",
37 | "option_d": "Traditional programming can handle image recognition better than Machine Learning",
38 | "correct_answer": "b"
39 | }
40 | },
41 | {
42 | "model": "quiz.question",
43 | "pk": 4,
44 | "fields": {
45 | "topic": 1,
46 | "text": "Which of the following is an example of supervised learning?",
47 | "option_a": "Grouping customers based on purchasing behavior",
48 | "option_b": "Predicting house prices based on features like area and number of rooms",
49 | "option_c": "Detecting unusual transactions without labeled data",
50 | "option_d": "Training a robot to learn from rewards in a game environment",
51 | "correct_answer": "b"
52 | }
53 | },
54 | {
55 | "model": "quiz.question",
56 | "pk": 5,
57 | "fields": {
58 | "topic": 1,
59 | "text": "What is the role of a dataset in Machine Learning?",
60 | "option_a": "To provide training and testing data for the model",
61 | "option_b": "To create a database of rules",
62 | "option_c": "To store the output of the model",
63 | "option_d": "To develop software applications",
64 | "correct_answer": "a"
65 | }
66 | },
67 | {
68 | "model": "quiz.question",
69 | "pk": 6,
70 | "fields": {
71 | "topic": 1,
72 | "text": "What is a common application of AI in daily life?",
73 | "option_a": "Weather forecasting using random guesses",
74 | "option_b": "Spam email filtering in mail services",
75 | "option_c": "Writing code for computer programs manually",
76 | "option_d": "Developing spreadsheets for data storage",
77 | "correct_answer": "b"
78 | }
79 | },
80 | {
81 | "model": "quiz.question",
82 | "pk": 7,
83 | "fields": {
84 | "topic": 1,
85 | "text": "What does \"unsupervised learning\" aim to do?",
86 | "option_a": "Predict a target variable using labeled data",
87 | "option_b": "Find hidden patterns in unlabeled data",
88 | "option_c": "Simulate human intelligence for logical tasks",
89 | "option_d": "Train robots to follow human instructions",
90 | "correct_answer": "b"
91 | }
92 | },
93 | {
94 | "model": "quiz.question",
95 | "pk": 8,
96 | "fields": {
97 | "topic": 1,
98 | "text": "Which of the following is an example of reinforcement learning?",
99 | "option_a": "Clustering news articles based on their content",
100 | "option_b": "Teaching a robot to walk by rewarding it for each successful step",
101 | "option_c": "Predicting stock prices using historical data",
102 | "option_d": "Identifying spam emails based on labeled examples",
103 | "correct_answer": "b"
104 | }
105 | },
106 | {
107 | "model": "quiz.question",
108 | "pk": 9,
109 | "fields": {
110 | "topic": 1,
111 | "text": "Which of these statements is true about deep learning?",
112 | "option_a": "It is a subset of reinforcement learning",
113 | "option_b": "It does not use neural networks",
114 | "option_c": "It requires large amounts of data to perform well",
115 | "option_d": "It is primarily used for clustering",
116 | "correct_answer": "c"
117 | }
118 | },
119 | {
120 | "model": "quiz.question",
121 | "pk": 10,
122 | "fields": {
123 | "topic": 1,
124 | "text": "What is the primary objective of Machine Learning?",
125 | "option_a": "To make machines conscious like humans",
126 | "option_b": "To build hardware that mimics the human brain",
127 | "option_c": "To write manual code for all decision-making processes",
128 | "option_d": "To enable machines to learn from data and make decisions",
129 | "correct_answer": "d"
130 | }
131 | },
132 | {
133 | "model": "quiz.topic",
134 | "pk": 1,
135 | "fields": {
136 | "name": "Introduction to Artificial Intelligence and Machine Learning"
137 | }
138 | },
139 | {
140 | "model": "quiz.topic",
141 | "pk": 2,
142 | "fields": {
143 | "name": "Mathematics and Statistics for AI/ML"
144 | }
145 | },
146 | {
147 | "model": "quiz.topic",
148 | "pk": 3,
149 | "fields": {
150 | "name": "Data Preprocessing and Feature Engineering"
151 | }
152 | },
153 | {
154 | "model": "quiz.topic",
155 | "pk": 4,
156 | "fields": {
157 | "name": "Supervised Learning"
158 | }
159 | },
160 | {
161 | "model": "quiz.topic",
162 | "pk": 5,
163 | "fields": {
164 | "name": "Unsupervised Learning"
165 | }
166 | },
167 | {
168 | "model": "quiz.topic",
169 | "pk": 6,
170 | "fields": {
171 | "name": "Neural Networks and Deep Learning"
172 | }
173 | },
174 | {
175 | "model": "quiz.topic",
176 | "pk": 7,
177 | "fields": {
178 | "name": "Reinforcement Learning"
179 | }
180 | },
181 | {
182 | "model": "quiz.topic",
183 | "pk": 8,
184 | "fields": {
185 | "name": "Natural Language Processing (NLP)"
186 | }
187 | },
188 | {
189 | "model": "quiz.topic",
190 | "pk": 9,
191 | "fields": {
192 | "name": "Model Deployment and Scalability"
193 | }
194 | },
195 | {
196 | "model": "quiz.topic",
197 | "pk": 10,
198 | "fields": {
199 | "name": "Ethical AI and Advanced Topics"
200 | }
201 | }
202 | ]
203 |
--------------------------------------------------------------------------------
/Algolyzer/home/templates/home/dashboard.html:
--------------------------------------------------------------------------------
1 | {% extends "base.html" %}
2 |
3 | {% block content %}
4 |
10 |
63 | K-Means Clustering
11 | 12 | 13 | 29 | 30 | {% if error %} 31 |
32 |
36 | {% endif %}
37 |
38 | {% if classified_values %}
39 |
33 |
35 | {{ error }}
34 |
40 |
50 | {% endif %}
51 |
52 | {% if show_chart %}
53 |
41 |
49 | Input Values: {{ input_values|join:", " }}
42 |Clusters:
43 |-
44 | {% for value, cluster in classified_values %}
45 |
- Value: {{ value }} → Cluster: {{ cluster }} 46 | {% endfor %} 47 |
54 |
61 | {% endif %}
62 |
55 |
60 |
56 |
59 | K-Means Clustering Chart
57 | 58 |
5 |
142 | {% endblock %}
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/Algolyzer/community/templates/community/post_detail.html:
--------------------------------------------------------------------------------
1 | {% extends 'base.html' %}
2 |
3 | {% block content %}
4 | 6 | Welcome, {{ user_profile }} 7 |
11 | 12 | 13 |
14 |
15 |
64 |
65 |
66 |
16 |
27 |
28 |
29 |
17 |
20 |
21 | Your XP
22 | {{ user_profile.xp }} XP
23 | Level {{ user_profile.level }}
24 |
25 | {{ user_profile.get_next_level_xp }} XP to next level
26 |
30 |
39 |
40 |
41 |
31 |
34 |
35 | Quizzes Completed
36 | {{ completed_quizzes }}
37 | Out of {{ total_quizzes }} topics
38 |
42 |
51 |
52 |
53 |
43 |
46 |
47 | Tasks
48 | {{ completed_tasks }} Done
49 | {{ pending_tasks }} Pending
50 |
54 |
63 |
55 |
58 |
59 | Forum Activity
60 | {{ total_posts }} Posts
61 | {{ total_comments }} Comments
62 |
67 |
103 |
104 |
105 | Recent Activity
68 |
69 |
70 |
102 |
71 |
83 |
84 |
85 |
72 |
82 | Recent Quizzes
73 |-
74 | {% for progress in recent_quizzes %}
75 |
-
76 |
{{ progress.topic.name }}
77 |Completed on {{ progress.updated_at|date:"M d, Y" }}
78 |
79 | {% endfor %}
80 |
86 |
101 |
87 |
100 | Recent Tasks
88 |-
89 | {% for task in recent_tasks %}
90 |
-
91 |
{{ task.model_name }}
92 |93 | Status: {{ task.status }} 94 |
95 |Created on {{ task.created_at|date:"M d, Y" }}
96 |
97 | {% endfor %}
98 |
106 |
141 | Forum Activity
107 |
108 |
109 |
140 |
110 |
123 |
124 |
125 |
111 |
122 | Recent Posts
112 |-
113 | {% for post in recent_posts %}
114 |
-
115 |
{{ post.title }}
116 |In {{ post.category.name }}
117 |Posted on {{ post.created_at|date:"M d, Y" }}
118 |
119 | {% endfor %}
120 |
126 |
139 |
127 |
138 | Recent Comments
128 |-
129 | {% for comment in recent_comments %}
130 |
-
131 |
{{ comment.content|truncatechars:50 }}
132 |On {{ comment.post.title }}
133 |Posted on {{ comment.created_at|date:"M d, Y" }}
134 |
135 | {% endfor %}
136 |
5 |
125 | {% endblock %}
126 |
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/Algolyzer/templates/account/login.html:
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1 | {% extends "base.html" %}
2 | {% load allauth i18n %}
3 | {% block head_title %}
4 | {% trans "Sign In" %}
5 | {% endblock head_title %}
6 |
7 | {% block content %}
8 |
6 | ← Back to Posts
7 |
8 | {% if user == post.author %}
9 |
10 |
15 |
37 | {% endif %}
38 |
39 | {% if user == post.author %}
40 |
41 |
46 |
58 | {% endif %}
59 |
60 |
61 |
62 |
63 |
98 |
99 |
100 |
64 | 
69 | {% endif %}
70 |
71 |
72 |
97 | {{ post.title }}
65 |By {{ post.author.username }} | {{ post.created_at|date:"F j, Y, g:i a" }}
66 | 67 | {% if post.image %} 68 |
69 | {% endif %}
70 |
71 |
72 |
73 |
75 |
76 |
77 | {{ post.content|linebreaks }}
74 |
78 |
86 |
94 |
96 | {{ total_votes }}
95 |
101 |
124 | Comments
102 |
103 | {% for comment in comments %}
104 |
112 |
113 | {% if user.is_authenticated %}
114 |
115 |
120 | {% else %}
121 |
105 |
108 | {% empty %}
109 | {{ comment.author.username }}: {{ comment.content }}
106 |{{ comment.created_at|date:"F j, Y, g:i a" }}
107 |No comments yet. Be the first to comment!
110 | {% endfor %} 111 |You must be logged in to comment.
122 | {% endif %} 123 |
9 |
109 | {% endblock content %}
110 |
111 | {% block extra_body %}
112 | {{ block.super }}
113 | {% if PASSKEY_LOGIN_ENABLED %}
114 | {% include "mfa/webauthn/snippets/login_script.html" with button_id="passkey_login" %}
115 | {% endif %}
116 | {% endblock extra_body %}
117 |
118 | {% block scripts %}
119 |
120 |
121 |
144 | {% endblock %}
145 |
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/Algolyzer/home/templates/home/home.html:
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1 | {% extends 'base.html' %}
2 |
3 | {% block extra_head %}
4 | {% load static %}
5 |
13 | {% endblock %}
14 |
15 | {% block content %}
16 |
10 |
108 |
11 |
107 | 12 | {% trans "Sign In" %} 13 |
14 | {% if not SOCIALACCOUNT_ONLY %} 15 |16 | {% blocktranslate %} 17 | If you don't have an account, please 18 | sign up first. 19 | {% endblocktranslate %} 20 |
21 | {% endif %} 22 | 23 | 83 | 84 | {% if LOGIN_BY_CODE_ENABLED or PASSKEY_LOGIN_ENABLED %} 85 |OR
86 |
87 | {% if PASSKEY_LOGIN_ENABLED %}
88 |
89 | {% trans "Sign in with a passkey" %}
90 |
91 | {% endif %}
92 | {% if LOGIN_BY_CODE_ENABLED %}
93 |
94 | {% trans "Mail me a sign-in code" %}
95 |
96 | {% endif %}
97 |
98 | {% endif %}
99 |
100 | {% if SOCIALACCOUNT_ENABLED %}
101 | Or Use a Third Party App
102 |
103 | {% include "socialaccount/snippets/login.html" with page_layout="entrance" %}
104 |
105 | {% endif %}
106 |
17 |
30 | {% endblock %}
31 |
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/Algolyzer/templates/base.html:
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1 |
2 |
3 |
4 |
5 |
6 |
7 |
18 |
19 | {% comment %}
29 | Welcome to
{% endcomment %} 20 |Easiest way to
21 |Learn AI-ML
22 |Become the next Algo Expert with Algolyzer
23 | 24 |
25 | Contribute
26 | Get Started
27 |
28 |
28 |
92 |
93 |
94 |
95 |
29 | 
57 |
58 |
59 |
81 |
91 |
30 |
55 |
56 |
31 |
34 |
35 |
36 | {% comment %} mobile navbar links {% endcomment %}
37 |
54 |