Upload app.py

app.py

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