import gradio as gr
import cv2
import numpy as np
import joblib
from utils import extract_features  # Your feature extraction logic

# Load model and encoders
model = joblib.load("model/random_forest.pkl")
label_encoders = joblib.load("model/label_encoders.pkl")

def recommend_mask(image):
    # Extract face shape, skin tone, face size from image
    face_shape, skin_tone, face_size = extract_features(image)
    
    # Label encode features
    face_encoded = label_encoders["face_shape"].transform([face_shape])[0]
    skin_encoded = label_encoders["skin_tone"].transform([skin_tone])[0]
    size_encoded = label_encoders["face_size"].transform([face_size])[0]
    
    # Predict mask style
    prediction = model.predict([[face_encoded, skin_encoded, size_encoded]])[0]
    return prediction

# Gradio Interface
demo = gr.Interface(
    fn=recommend_mask,
    inputs=gr.Image(label="Upload Your Face", type="filepath"),
    outputs=gr.Textbox(label="Recommended Mask Style"),
    title="🎭 AI Party Mask Recommender",
    description="Upload a photo to get a personalized mask recommendation!",
)

demo.launch()