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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 | |
interface = gr.Interface( | |
fn=predict_coffee_type, | |
inputs=[ | |
gr.inputs.Dropdown(['morning', 'afternoon', 'evening'], label="Time of Day"), | |
gr.inputs.Dropdown(['mild', 'regular', 'strong'], label="Coffee Strength"), | |
gr.inputs.Dropdown(['unsweetened', 'lightly sweetened', 'sweet'], label="Sweetness Level"), | |
gr.inputs.Dropdown(['none', 'regular', 'skim', 'almond'], label="Milk Type"), | |
gr.inputs.Dropdown(['hot', 'iced', 'cold brew'], label="Coffee Temperature"), | |
gr.inputs.Dropdown(['yes', 'no'], label="Flavored Coffee"), | |
gr.inputs.Dropdown(['low', 'medium', 'high'], label="Caffeine Tolerance"), | |
gr.inputs.Dropdown(['Arabica', 'Robusta', 'blend'], label="Coffee Bean"), | |
gr.inputs.Dropdown(['small', 'medium', 'large'], label="Coffee Size"), | |
gr.inputs.Dropdown(['none', 'vegan', 'lactose-intolerant'], label="Dietary Preferences") | |
], | |
outputs=gr.outputs.Textbox(label="Recommended Coffee Type"), | |
title="Coffee Type Recommendation" | |
) | |
if __name__ == "__main__": | |
interface.launch() | |