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Upload app.py

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  1. app.py +61 -0
app.py ADDED
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+ import streamlit as st
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+ import pandas as pd
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+ import pickle
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+
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+ # Load the pre-trained model
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+ with open('best_model.pkl', 'rb') as model_file:
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+ model = pickle.load(model_file)
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+
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+ # Load the label encoder
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+ with open('label_encoder.pkl', 'rb') as label_encoder_file:
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+ label_encoder = pickle.load(label_encoder_file)
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+
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+ # Title of the app
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+ st.title("Coffee Type Prediction")
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+
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+ # Sidebar inputs for user preferences
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+ st.sidebar.header("User Preferences")
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+
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+ time_of_day = st.sidebar.selectbox("Time of Day", ['morning', 'afternoon', 'evening'])
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+ coffee_strength = st.sidebar.selectbox("Coffee Strength", ['mild', 'regular', 'strong'])
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+ sweetness_level = st.sidebar.selectbox("Sweetness Level", ['unsweetened', 'lightly sweetened', 'sweet'])
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+ milk_type = st.sidebar.selectbox("Milk Type", ['none', 'regular', 'skim', 'almond'])
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+ coffee_temperature = st.sidebar.selectbox("Coffee Temperature", ['hot', 'iced', 'cold brew'])
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+ flavored_coffee = st.sidebar.selectbox("Flavored Coffee", ['yes', 'no'])
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+ caffeine_tolerance = st.sidebar.selectbox("Caffeine Tolerance", ['low', 'medium', 'high'])
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+ coffee_bean = st.sidebar.selectbox("Coffee Bean", ['Arabica', 'Robusta', 'blend'])
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+ coffee_size = st.sidebar.selectbox("Coffee Size", ['small', 'medium', 'large'])
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+ dietary_preferences = st.sidebar.selectbox("Dietary Preferences", ['none', 'vegan', 'lactose-intolerant'])
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+
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+ # Encoding the inputs manually (same encoding as in your training data)
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+ input_data = pd.DataFrame({
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+ 'Token_0': [time_of_day],
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+ 'Token_1': [coffee_strength],
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+ 'Token_2': [sweetness_level],
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+ 'Token_3': [milk_type],
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+ 'Token_4': [coffee_temperature],
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+ 'Token_5': [flavored_coffee],
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+ 'Token_6': [caffeine_tolerance],
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+ 'Token_7': [coffee_bean],
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+ 'Token_8': [coffee_size],
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+ 'Token_9': [dietary_preferences]
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+ })
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+
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+ # One-hot encode the input data (ensure it matches the training data)
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+ input_encoded = pd.get_dummies(input_data)
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+
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+ # Align columns with the training data (required columns)
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+ required_columns = [...] # Include all columns from the original model training data
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+ for col in required_columns:
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+ if col not in input_encoded.columns:
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+ input_encoded[col] = 0
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+ input_encoded = input_encoded[required_columns]
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+
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+ # Make the prediction
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+ prediction = model.predict(input_encoded)[0]
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+
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+ # Reverse the label encoding (map the prediction back to the coffee type)
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+ coffee_type = label_encoder.inverse_transform([prediction])[0]
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+
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+ # Display the prediction
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+ st.subheader(f"Recommended Coffee: {coffee_type}")