├── 09_voice-bot-flask
├── 67b4722c-6e09-43e9-91a9-55bda94fb288.wav
├── app.py
└── templates
│ └── index.html
├── 06_diabetese_prediction
├── requirements.txt
├── svm_model.pkl
├── template
│ ├── show.html
│ └── home.html
├── main.py
├── diabetes.csv
└── Pima_Nulls.ipynb
├── 04_House-Price-Predictor
├── requirements.txt
├── .DS_Store
├── model.pkl
├── static
│ └── background.jpg
├── Data
│ └── House_Price.json
├── model.py
├── app.py
├── templates
│ └── index.html
└── model.ipynb
├── 08_wordcloud-flask
├── .DS_Store
├── uploads
│ ├── wordcloud.png
│ └── environmental_sustainability.pdf
├── __pycache__
│ ├── app.cpython-311.pyc
│ └── app1.cpython-311.pyc
├── environmental sustainability.pdf
├── templates
│ ├── result.html
│ └── index.html
└── app.py
├── 05_tips
├── tip_predictor_model.pkl
├── app.py
├── model.py
└── templates
│ ├── results.html
│ └── index.html
├── 07_test2speech-flask
├── .DS_Store
├── app.py
└── templates
│ └── index.html
├── 01_basic_app
├── __pycache__
│ └── app.cpython-311.pyc
└── app.py
├── 03_image_generation_app
├── generated image.png
├── templates
│ └── index.html
└── app.py
├── 02_eda_app
├── templates
│ └── index.html
└── app.py
└── ReadMe.md
/09_voice-bot-flask/67b4722c-6e09-43e9-91a9-55bda94fb288.wav:
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1 |
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/06_diabetese_prediction/requirements.txt:
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1 | Flask==2.2.5
2 | numpy==1.26.4
3 | pandas==2.2.1
4 | scikit_learn==1.4.1.post1
5 |
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/04_House-Price-Predictor/requirements.txt:
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1 | Flask==2.2.5
2 | numpy==1.26.4
3 | pandas==2.2.1
4 | scikit_learn==1.4.1.post1
5 |
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/08_wordcloud-flask/.DS_Store:
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https://raw.githubusercontent.com/AammarTufail/flask_webapp_development_series/main/08_wordcloud-flask/.DS_Store
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/05_tips/tip_predictor_model.pkl:
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https://raw.githubusercontent.com/AammarTufail/flask_webapp_development_series/main/05_tips/tip_predictor_model.pkl
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/07_test2speech-flask/.DS_Store:
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/04_House-Price-Predictor/.DS_Store:
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https://raw.githubusercontent.com/AammarTufail/flask_webapp_development_series/main/04_House-Price-Predictor/.DS_Store
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/04_House-Price-Predictor/model.pkl:
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/06_diabetese_prediction/svm_model.pkl:
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https://raw.githubusercontent.com/AammarTufail/flask_webapp_development_series/main/06_diabetese_prediction/svm_model.pkl
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/08_wordcloud-flask/uploads/wordcloud.png:
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/03_image_generation_app/generated image.png:
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https://raw.githubusercontent.com/AammarTufail/flask_webapp_development_series/main/03_image_generation_app/generated image.png
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/04_House-Price-Predictor/static/background.jpg:
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https://raw.githubusercontent.com/AammarTufail/flask_webapp_development_series/main/04_House-Price-Predictor/static/background.jpg
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/08_wordcloud-flask/__pycache__/app.cpython-311.pyc:
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/08_wordcloud-flask/__pycache__/app1.cpython-311.pyc:
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https://raw.githubusercontent.com/AammarTufail/flask_webapp_development_series/main/08_wordcloud-flask/__pycache__/app1.cpython-311.pyc
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/08_wordcloud-flask/environmental sustainability.pdf:
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https://raw.githubusercontent.com/AammarTufail/flask_webapp_development_series/main/08_wordcloud-flask/environmental sustainability.pdf
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/08_wordcloud-flask/uploads/environmental_sustainability.pdf:
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https://raw.githubusercontent.com/AammarTufail/flask_webapp_development_series/main/08_wordcloud-flask/uploads/environmental_sustainability.pdf
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/02_eda_app/templates/index.html:
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1 |
2 |
3 |
4 | Iris Dataset EDA
5 |
6 |
7 | Exploratory Data Analysis of the Iris Dataset
8 | Word Cloud Result
5 |
6 |
7 | Word Cloud Result
8 | Download Word Cloud
10 |
11 |
12 |
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/04_House-Price-Predictor/model.py:
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1 | import pandas as pd
2 | from sklearn.linear_model import LinearRegression
3 | import pickle
4 | df=pd.read_json('Data/House_Price.json')
5 | x=df['Area(in sq. ft)'].values.reshape(-1,1)
6 | y=df['Price(in Rs.)'].values.reshape(-1,1)
7 | lin=LinearRegression()
8 | lin.fit(x,y)
9 | pickle.dump(lin,open('model.pkl','wb'))
10 |
11 |
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/01_basic_app/app.py:
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1 | from flask import Flask
2 |
3 | app = Flask(__name__) # create an instance of the Flask class
4 |
5 | @app.route('/') # route() decorator to tell Flask what URL should trigger our function
6 |
7 | def hello_world():
8 | return 'Hello, World! How are you?'
9 |
10 | if __name__ == '__main__':
11 | app.run(debug=True, ) # run the application
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/08_wordcloud-flask/templates/index.html:
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1 |
2 |
3 |
4 | Word Cloud Generator
5 |
6 |
7 | Upload a file to generate a word cloud
8 |
17 |
18 |
19 |
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/04_House-Price-Predictor/app.py:
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1 | from flask import Flask,render_template,request
2 | import pickle
3 | import numpy as np
4 | app = Flask('__name__')
5 | model=pickle.load(open('model.pkl','rb'))
6 |
7 | @app.route('/')
8 | def home():
9 | return render_template('index.html')
10 |
11 | @app.route('/predict',methods=["POST"])
12 | def predict():
13 | feature=[int(x) for x in request.form.values()]
14 | feature_final=np.array(feature).reshape(-1,1)
15 | prediction=model.predict(feature_final)
16 | return render_template('index.html',prediction_text='Price of House will be Rs. {}'.format(int(prediction)))
17 |
18 | if(__name__=='__main__'):
19 | app.run(debug=True)
20 |
21 |
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/03_image_generation_app/templates/index.html:
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1 |
2 |
3 |
4 | DALL·E 3 Image Generator app by Codanics
6 |
7 |
8 | DALL·E 3 Image Generator app by Codanics
9 |
13 | {% if image_url %}
14 |
18 | {% endif %}
19 |
20 |
21 |
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/05_tips/app.py:
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1 | # app.py
2 | from flask import Flask, request, render_template
3 | import numpy as np
4 | import joblib
5 |
6 | app = Flask(__name__)
7 | model = joblib.load('tip_predictor_model.pkl')
8 |
9 | @app.route('/', methods=['GET', 'POST'])
10 | def predict():
11 | if request.method == 'POST':
12 | total_bill = float(request.form['total_bill'])
13 | size = int(request.form['size'])
14 |
15 | # Create the feature array
16 | features = np.array([[total_bill, size]])
17 | prediction = model.predict(features)
18 |
19 | return render_template('results.html', prediction=round(prediction[0], 2))
20 | return render_template('index.html')
21 |
22 | if __name__ == '__main__':
23 | app.run(debug=True)
24 |
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/05_tips/model.py:
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1 | # train_model.py
2 | import seaborn as sns
3 | import pandas as pd
4 | from sklearn.model_selection import train_test_split
5 | from sklearn.linear_model import LinearRegression
6 | import joblib
7 |
8 | # Load the dataset
9 | tips = sns.load_dataset('tips')
10 |
11 | # For simplicity, we'll use 'total_bill' and 'size' as features
12 | # Convert categorical variables to dummy variables if you use them
13 | X = tips[['total_bill', 'size']]
14 | y = tips['tip']
15 |
16 | # Split the data
17 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
18 |
19 | # Train the model
20 | model = LinearRegression()
21 | model.fit(X_train, y_train)
22 |
23 | # Save the model
24 | joblib.dump(model, 'tip_predictor_model.pkl')
25 |
26 | print("Model trained and saved.")
27 |
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/09_voice-bot-flask/app.py:
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1 | from flask import Flask, render_template, request, jsonify
2 | import openai
3 | import os
4 |
5 | app = Flask(__name__)
6 | openai.api_key = os.environ.get("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")
7 |
8 | @app.route('/')
9 | def index():
10 | return render_template('index.html')
11 |
12 | @app.route('/generate_response', methods=['POST'])
13 | def generate_response():
14 | data = request.get_json()
15 | text = data.get('text')
16 |
17 | client = openai.OpenAI(api_key=openai.api_key)
18 | response = client.chat.completions.create(
19 | model="gpt-3.5-turbo",
20 | messages=[
21 | {"role": "user", "content": text}
22 | ]
23 | )
24 |
25 | ai_text = response.choices[0].message.content
26 | return jsonify({'ai_text': ai_text})
27 |
28 | if __name__ == '__main__':
29 | app.run(debug=True)
30 |
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/05_tips/templates/results.html:
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1 |
2 |
3 |
4 | Tip Prediction Result
7 |
8 |
12 |
Predicted Tip Amount
13 |
Your predicted tip is: ${{ prediction }}
14 |
Calculate again
15 |
36 |
37 |
38 | Predict House Price
39 |
40 |
47 |
50 |
51 |
52 |
53 |
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/03_image_generation_app/app.py:
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1 | from flask import Flask, render_template, request, redirect, url_for
2 | import openai
3 |
4 | # Create a Flask app
5 | app = Flask(__name__)
6 |
7 | # Load your OpenAI API key (hardcoded for testing purposes)
8 | openai_api_key = "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"
9 |
10 | def generate_image(prompt):
11 | client = openai.OpenAI(api_key=openai_api_key)
12 |
13 | response = client.images.generate(
14 | model="dall-e-3",
15 | prompt=prompt,
16 | size="1024x1024",
17 | quality="standard",
18 | n=1,
19 | )
20 |
21 | return response.data[0].url
22 |
23 | @app.route('/', methods=['GET', 'POST'])
24 | def index():
25 | if request.method == 'POST':
26 | prompt = request.form.get('prompt')
27 |
28 | if prompt:
29 | image_url = generate_image(prompt)
30 | if image_url:
31 | return render_template('index.html', image_url=image_url)
32 | else:
33 | return render_template('index.html', error='Failed to generate image. Please try again.')
34 | else:
35 | return render_template('index.html', error='Please enter a description.')
36 |
37 | return render_template('index.html')
38 |
39 | if __name__ == '__main__':
40 | app.run(debug=True)
41 |
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/05_tips/templates/index.html:
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1 |
2 |
3 |
4 | Tip Calculator
7 |
8 |
12 |
Enter Your Bill Details
13 |
24 |
display results
6 |
42 |
43 |
44 | DIABETES PREDICTION
45 | Pregnancy Value : {{ preg }}
46 | Blood Pressure Value : {{ bp }}
47 | Glucose Levels : {{ gluc }}
48 | Skin Thickness : {{ st }}
49 | Insulin Levels : {{ ins }}
50 | BMI : {{ bmi }}
51 | DiabetesPedigreeFunction : {{ dbf }}
52 | Age : {{ age }}
53 |
54 |
55 |
Diabetes Prediction : {{ res }}
56 |
57 |
58 |
59 |
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/02_eda_app/app.py:
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1 | # import libraries
2 | from flask import Flask, render_template
3 | import pandas as pd
4 | import numpy as np
5 | import matplotlib.pyplot as plt
6 | import seaborn as sns
7 | import io
8 | import base64
9 |
10 | # Create an instance of the Flask class
11 | app = Flask(__name__)
12 |
13 | # define a function to load the dataset
14 | def load_data():
15 | df = sns.load_dataset('iris')
16 | return df
17 |
18 | # define the function to plot the data
19 | def plot(df):
20 | # Create a figure for the plots
21 | fig, axs = plt.subplots(2, 2, figsize=(12, 12))
22 |
23 | # Plot distribution of each feature
24 | sns.histplot(data=df, x='sepal_length', kde=True, ax=axs[0, 0], color='skyblue')
25 | sns.histplot(data=df, x='sepal_width', kde=True, ax=axs[0, 1], color='olive')
26 | sns.histplot(data=df, x='petal_length', kde=True, ax=axs[1, 0], color='gold')
27 | sns.histplot(data=df, x='petal_width', kde=True, ax=axs[1, 1], color='teal')
28 |
29 | # Adjust layout
30 | plt.tight_layout()
31 |
32 | # Save plot to a bytes buffer
33 | buf = io.BytesIO()
34 | plt.savefig(buf, format='png')
35 | buf.seek(0)
36 |
37 | # Encode the plot to base64 string for HTML embedding
38 | b64_image = base64.b64encode(buf.read()).decode('utf-8')
39 | return b64_image
40 |
41 | # define the route
42 | @app.route('/')
43 | def index():
44 | # load the dataset
45 | df = load_data()
46 | # plot the data
47 | b64_image = plot(df)
48 | return render_template('index.html', b64_image=b64_image)
49 |
50 | # run the app
51 | if __name__ == '__main__':
52 | app.run(debug=True)
53 |
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/07_test2speech-flask/app.py:
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1 | from flask import Flask, render_template, request, send_file, url_for
2 | import os
3 | import tempfile
4 | import pydub
5 | from openai import OpenAI
6 |
7 | app = Flask(__name__)
8 |
9 | # Replace YOUR_API_KEY with your actual OpenAI API key
10 | openai = OpenAI(api_key="")
11 |
12 | @app.route('/')
13 | def index():
14 | return render_template('index.html')
15 |
16 | @app.route('/convert', methods=['POST'])
17 | def convert():
18 | text = request.form['text']
19 | model = request.form['model']
20 | voice = request.form['voice']
21 | format = request.form['format']
22 |
23 | mp3_speech_path = text_to_speech(text, model, voice)
24 |
25 | if format != "mp3":
26 | with tempfile.NamedTemporaryFile(delete=False, suffix=f".{format}") as tmpfile:
27 | convert_audio_format(mp3_speech_path, tmpfile.name, format)
28 | speech_path = tmpfile.name
29 | os.remove(mp3_speech_path)
30 | else:
31 | speech_path = mp3_speech_path
32 |
33 | return send_file(speech_path, as_attachment=True)
34 |
35 | def text_to_speech(text, model, voice):
36 | with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmpfile:
37 | speech_file_path = tmpfile.name
38 | response = openai.audio.speech.create(
39 | model=model,
40 | voice=voice,
41 | input=text
42 | )
43 | response.stream_to_file(speech_file_path)
44 | return speech_file_path
45 |
46 | def convert_audio_format(input_path, output_path, format):
47 | audio = pydub.AudioSegment.from_mp3(input_path)
48 | audio.export(output_path, format=format)
49 |
50 | if __name__ == '__main__':
51 | app.run(debug=True)
52 |
53 |
54 | # add the name option to save the fil
55 | # make this app a multimodel app
56 | # use responsive images
57 |
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/07_test2speech-flask/templates/index.html:
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1 |
2 |
3 |
4 | Text to Speech Converter
5 |
6 |
7 | 🔊 Text to Speech Converter 📝
8 |
10 | This app converts text to speech using OpenAI's tts-1 or tts-1-hd model.
11 | Please enter your OpenAI API key. Do not share your API key with others.
12 |
13 |
40 |
41 |
42 |
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/09_voice-bot-flask/templates/index.html:
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1 |
2 |
3 |
4 | Voice-Assisted Chatbot 🎙️
7 |
8 |
9 | Voice-Assisted Chatbot 🎙️
10 | Ask Question, I am Listening...
11 | Start Recording
12 |
13 |
14 |
51 |
52 |
53 |
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/06_diabetese_prediction/main.py:
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1 | from flask import Flask, render_template, request
2 | import pickle
3 | import pandas as pd
4 | import numpy as np
5 | from sklearn.preprocessing import StandardScaler
6 | from sklearn.pipeline import Pipeline
7 |
8 | app = Flask(__name__, template_folder='template')
9 | svm_model=pickle.load(open('svm_model.pkl','rb'))
10 |
11 | @app.route('/')
12 | def home():
13 | return render_template("home.html")
14 |
15 | def std_scalar(df):
16 | std_X = StandardScaler()
17 | x = pd.DataFrame(std_X.fit_transform(df))
18 | return x
19 |
20 | def pipeline(features):
21 | steps = [('scaler', StandardScaler()), ('SVM', svm_model)]
22 | pipe = Pipeline(steps)
23 | return pipe.fit_transform(features)
24 |
25 |
26 | @app.route('/send', methods=['POST'])
27 | def getdata():
28 |
29 | features = [float() for x in request.form.values()]
30 | final_features = [np.array(features)]
31 |
32 | #Feature tranform and prediction using pipeline
33 | # We can now use predictions from this feature_tranformed variable
34 | #feature_tranformed= pipeline(final_features)
35 |
36 |
37 | feature_transform=std_scalar(final_features)
38 | # Using standard scalar method
39 | prediction = svm_model.predict(feature_transform)
40 | if prediction==0:
41 | result="You Are Non-Diabetic"
42 | else:
43 | result="You Are Diabetic"
44 |
45 | Pregnancies=request.form['Pregnancies']
46 | Glucose = request.form['Glucose']
47 | BloodPressure = request.form['BloodPressure']
48 | SkinThickness = request.form['SkinThickness']
49 | Insulin = request.form['Insulin']
50 | BMI = request.form['BMI']
51 | DiabetesPedigreeFunction = request.form['DiabetesPedigreeFunction']
52 | Age = request.form['Age']
53 | return render_template('show.html', preg=Pregnancies, bp=BloodPressure,
54 | gluc=Glucose, st=SkinThickness, ins=Insulin, bmi=BMI,
55 | dbf=DiabetesPedigreeFunction, age=Age, res=result)
56 |
57 |
58 | if __name__=="__main__":
59 | app.run(debug=True)
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/06_diabetese_prediction/template/home.html:
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1 |
2 |
3 |
4 | Diabetes Prediction
5 | ')
56 | def result(filename):
57 | return send_from_directory(app.config['UPLOAD_FOLDER'], filename)
58 |
59 | @app.route('/uploads/')
60 | def uploaded_file(filename):
61 | return send_from_directory(app.config['UPLOAD_FOLDER'], filename)
62 |
63 | if __name__ == '__main__':
64 | # Print the absolute path of UPLOAD_FOLDER for debugging
65 | print(f'Upload folder is set to {os.path.abspath(app.config["UPLOAD_FOLDER"])}')
66 | app.run(debug=True)
67 |
68 | # you have to make the front end better than this
69 | # add download button
70 | # provide the table to user after making wordcloud
71 | # interface should be interactive
72 | #.............................................
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/04_House-Price-Predictor/model.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": 2,
6 | "metadata": {},
7 | "outputs": [],
8 | "source": [
9 | "import pandas as pd \n",
10 | "from sklearn.linear_model import LinearRegression\n",
11 | "import pickle\n",
12 | "df=pd.read_json('Data/House_Price.json')\n",
13 | "X=df['Area(in sq. ft)'].values.reshape(-1,1)\n",
14 | "y=df['Price(in Rs.)'].values.reshape(-1,1)\n",
15 | "model =LinearRegression()\n",
16 | "model.fit(X,y)\n",
17 | "pickle.dump(model,open('model.pkl','wb'))"
18 | ]
19 | },
20 | {
21 | "cell_type": "code",
22 | "execution_count": 3,
23 | "metadata": {},
24 | "outputs": [
25 | {
26 | "data": {
27 | "text/html": [
28 | "\n",
29 | "\n",
42 | "
\n",
43 | " \n",
44 | " \n",
45 | " Area(in sq. ft) \n",
47 | " Price(in Rs.) \n",
48 | " \n",
49 | " \n",
50 | " \n",
51 | " \n",
52 | " 0 \n",
53 | " 1000 \n",
54 | " 1500000 \n",
55 | " \n",
56 | " \n",
57 | " 1 \n",
58 | " 1200 \n",
59 | " 1800000 \n",
60 | " \n",
61 | " \n",
62 | " 2 \n",
63 | " 1500 \n",
64 | " 2250000 \n",
65 | " \n",
66 | " \n",
67 | " 3 \n",
68 | " 1800 \n",
69 | " 2700000 \n",
70 | " \n",
71 | " \n",
72 | " 4 \n",
73 | " 2000 \n",
74 | " 3000000 \n",
75 | " \n",
76 | " \n",
77 | "
\n",
78 | "
\n",
44 | "\n",
57 | "
\n",
58 | " \n",
59 | " \n",
60 | " Pregnancies \n",
62 | " Glucose \n",
63 | " BloodPressure \n",
64 | " SkinThickness \n",
65 | " Insulin \n",
66 | " BMI \n",
67 | " DiabetesPedigreeFunction \n",
68 | " Age \n",
69 | " Outcome \n",
70 | " \n",
71 | " \n",
72 | " \n",
73 | " \n",
74 | " 0 \n",
75 | " 6 \n",
76 | " 148 \n",
77 | " 72 \n",
78 | " 35 \n",
79 | " 0 \n",
80 | " 33.6 \n",
81 | " 0.627 \n",
82 | " 50 \n",
83 | " 1 \n",
84 | " \n",
85 | " \n",
86 | " 1 \n",
87 | " 1 \n",
88 | " 85 \n",
89 | " 66 \n",
90 | " 29 \n",
91 | " 0 \n",
92 | " 26.6 \n",
93 | " 0.351 \n",
94 | " 31 \n",
95 | " 0 \n",
96 | " \n",
97 | " \n",
98 | " 2 \n",
99 | " 8 \n",
100 | " 183 \n",
101 | " 64 \n",
102 | " 0 \n",
103 | " 0 \n",
104 | " 23.3 \n",
105 | " 0.672 \n",
106 | " 32 \n",
107 | " 1 \n",
108 | " \n",
109 | " \n",
110 | " 3 \n",
111 | " 1 \n",
112 | " 89 \n",
113 | " 66 \n",
114 | " 23 \n",
115 | " 94 \n",
116 | " 28.1 \n",
117 | " 0.167 \n",
118 | " 21 \n",
119 | " 0 \n",
120 | " \n",
121 | " \n",
122 | " 4 \n",
123 | " 0 \n",
124 | " 137 \n",
125 | " 40 \n",
126 | " 35 \n",
127 | " 168 \n",
128 | " 43.1 \n",
129 | " 2.288 \n",
130 | " 33 \n",
131 | " 1 \n",
132 | " \n",
133 | " \n",
134 | "
\n",
135 | "
\n",
267 | "\n",
280 | "
\n",
281 | " \n",
282 | " \n",
283 | " Outcome \n",
285 | " Glucose \n",
286 | " \n",
287 | " \n",
288 | " \n",
289 | " \n",
290 | " 0 \n",
291 | " 0 \n",
292 | " 107.0 \n",
293 | " \n",
294 | " \n",
295 | " 1 \n",
296 | " 1 \n",
297 | " 140.0 \n",
298 | " \n",
299 | " \n",
300 | "
\n",
301 | "
"
403 | ]
404 | },
405 | "metadata": {
406 | "needs_background": "light"
407 | },
408 | "output_type": "display_data"
409 | }
410 | ],
411 | "source": [
412 | "plt.figure(dpi = 125,figsize= (5,4))\n",
413 | "mask = np.triu(df.corr())\n",
414 | "#np.triu returns lower triangle for our heatmap as we do not need upper map\n",
415 | "sns.heatmap(df_nan.corr(),mask = mask, fmt = \".1f\",annot=True,lw=0.1,cmap = 'YlGnBu')\n",
416 | "plt.title('Correlation Map')\n",
417 | "plt.show()\n",
418 | "# New Correlation map has higher correlated values"
419 | ]
420 | },
421 | {
422 | "cell_type": "markdown",
423 | "metadata": {},
424 | "source": []
425 | },
426 | {
427 | "cell_type": "code",
428 | "execution_count": null,
429 | "metadata": {},
430 | "outputs": [],
431 | "source": []
432 | },
433 | {
434 | "cell_type": "code",
435 | "execution_count": null,
436 | "metadata": {},
437 | "outputs": [],
438 | "source": []
439 | },
440 | {
441 | "cell_type": "code",
442 | "execution_count": null,
443 | "metadata": {},
444 | "outputs": [],
445 | "source": []
446 | },
447 | {
448 | "cell_type": "code",
449 | "execution_count": null,
450 | "metadata": {},
451 | "outputs": [],
452 | "source": []
453 | }
454 | ],
455 | "metadata": {
456 | "kernelspec": {
457 | "display_name": "Python 3",
458 | "language": "python",
459 | "name": "python3"
460 | },
461 | "language_info": {
462 | "codemirror_mode": {
463 | "name": "ipython",
464 | "version": 3
465 | },
466 | "file_extension": ".py",
467 | "mimetype": "text/x-python",
468 | "name": "python",
469 | "nbconvert_exporter": "python",
470 | "pygments_lexer": "ipython3",
471 | "version": "3.8.3"
472 | }
473 | },
474 | "nbformat": 4,
475 | "nbformat_minor": 4
476 | }
477 |
--------------------------------------------------------------------------------
9 |
10 |
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/04_House-Price-Predictor/Data/House_Price.json:
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1 | {
2 | "Area(in sq. ft)":{"0":1000,"1":1200,"2":1500,"3":1800,"4":2000,"5":2300,"6":2500,"7":2700,"8":3000,"9":3200},
3 | "Price(in Rs.)" : {"0":1500000,"1":1800000,"2":2250000,"3":2700000,"4":3000000,"5":3450000,"6":3750000,"7":4050000,"8":4500000,"9":4800000}
4 | }
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/08_wordcloud-flask/templates/result.html:
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1 |
2 |
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4 | 
9 |