Spaces:
Sleeping
Sleeping
File size: 2,939 Bytes
476804e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
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()
|