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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() | |