Create app.py

app.py

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+import subprocess
+import sys
+
+# Force install scikit-learn if not found
+try:
+    import sklearn
+except ModuleNotFoundError:
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "scikit-learn"])
+    import sklearn  # Import again after installation
+
+import gradio as gr
+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)
+
+def predict_coffee_type(time_of_day, coffee_strength, sweetness_level, milk_type, coffee_temperature, flavored_coffee, caffeine_tolerance, coffee_bean, coffee_size, dietary_preferences):
+    # Creating input DataFrame for the model
+    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 = model.feature_names_in_  # Get the feature columns from the model
+    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]
+
+    return coffee_type
+
+# Gradio Interface using components
+interface = gr.Interface(
+    fn=predict_coffee_type,
+    inputs=[
+        gr.Dropdown(['morning', 'afternoon', 'evening'], label="Time of Day"),
+        gr.Dropdown(['mild', 'regular', 'strong'], label="Coffee Strength"),
+        gr.Dropdown(['unsweetened', 'lightly sweetened', 'sweet'], label="Sweetness Level"),
+        gr.Dropdown(['none', 'regular', 'skim', 'almond'], label="Milk Type"),
+        gr.Dropdown(['hot', 'iced', 'cold brew'], label="Coffee Temperature"),
+        gr.Dropdown(['yes', 'no'], label="Flavored Coffee"),
+        gr.Dropdown(['low', 'medium', 'high'], label="Caffeine Tolerance"),
+        gr.Dropdown(['Arabica', 'Robusta', 'blend'], label="Coffee Bean"),
+        gr.Dropdown(['small', 'medium', 'large'], label="Coffee Size"),
+        gr.Dropdown(['none', 'vegan', 'lactose-intolerant'], label="Dietary Preferences")
+    ],
+    outputs=gr.Textbox(label="Recommended Coffee Type"),
+    title="Coffee Type Recommendation"
+)
+
+if __name__ == "__main__":
+    interface.launch()