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import subprocess
import sys
# Check if scikit-learn is installed, if not install it
try:
import sklearn
except ModuleNotFoundError:
subprocess.check_call([sys.executable, "-m", "pip", "install", "scikit-learn"])
import sklearn
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',
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