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app.py ADDED
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+ import streamlit as st
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+ import pandas as pd
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+ import joblib
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+ from sklearn.linear_model import LinearRegression
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+ from sklearn.tree import DecisionTreeRegressor
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+ from sklearn.ensemble import RandomForestRegressor
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+
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+ # Load the trained models (make sure these pickle files are uploaded in the same directory as your app.py in Hugging Face Space)
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+ lr = joblib.load('linear_regression_model.pkl')
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+ dt = joblib.load('decision_tree_model.pkl')
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+ rf = joblib.load('random_forest_model.pkl')
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+
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+ # Streamlit UI
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+ st.title("Indian Food Cook Time Prediction")
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+ st.write("Enter the features to predict the cook time of Indian food.")
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+
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+ # Input fields
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+ diet = st.selectbox("Diet Type", options=["vegetarian", "non vegetarian"])
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+ prep_time = st.number_input("Preparation Time (minutes)", min_value=0, step=1)
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+ ingredients = st.number_input("Number of Ingredients", min_value=1, step=1)
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+
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+ # Convert diet to numeric (0 for vegetarian, 1 for non-vegetarian)
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+ diet = 0 if diet == "vegetarian" else 1
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+
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+ # Combine inputs into a DataFrame for prediction
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+ input_data = pd.DataFrame({
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+ 'diet': [diet],
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+ 'prep_time': [prep_time],
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+ 'num_ingredients': [ingredients]
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+ })
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+
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+ # Model selection
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+ model_choice = st.selectbox("Select a Model", ["Linear Regression", "Decision Tree", "Random Forest"])
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+
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+ if st.button("Predict Cook Time"):
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+ # Make prediction based on model choice
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+ if model_choice == "Linear Regression":
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+ prediction = lr.predict(input_data)[0]
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+ elif model_choice == "Decision Tree":
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+ prediction = dt.predict(input_data)[0]
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+ elif model_choice == "Random Forest":
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+ prediction = rf.predict(input_data)[0]
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+
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+ # Display the predicted cook time
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+ st.write(f"Predicted Cook Time: {round(prediction, 2)} minutes")
decision_tree_model.pkl ADDED
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+ size 8929
linear_regression_model.pkl ADDED
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random_forest_model.pkl ADDED
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