uitest.py

import gradio as gr
import requests
from PIL import Image
import base64
from io import BytesIO
import logging

# Set up logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)

# Define the API endpoints
skin_metrics_url = 'http://127.0.1:5000/upload'
recommendation_url = 'http://127.0.1:5000/recommend'

def capture_face_image(file):
    try:
        if file is None:
            return "Please upload an image", None

        # Open the uploaded image file (directly using the file object passed from Gradio)
        image = Image.open(file)
        
        # Convert image to base64
        buffered = BytesIO()
        image.save(buffered, format="PNG")
        img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
        
        # Send to backend for skin analysis
        try:
            response = requests.put(
                skin_metrics_url, 
                json={'file': img_str},
                timeout=10  # Add timeout
            )
            response.raise_for_status()  # Raise exception for bad status codes
            
            skin_data = response.json()  # Skin analysis data from backend
            return skin_data, image  # Return both the analysis data and image
            
        except requests.exceptions.RequestException as e:
            logger.error(f"API request failed: {str(e)}")
            return f"Error in skin analysis: {str(e)}", image
            
    except Exception as e:
        logger.error(f"Image processing error: {str(e)}")
        return f"Error processing image: {str(e)}", None

def get_recommendations(tone, skin_type, concerns):
    try:
        # Define the full list of features that the model expects (18 in total)
        all_features = [
            'normal', 'dry', 'oily', 'combination', 'acne', 'sensitive', 'fine lines', 'wrinkles', 'redness',
            'dull', 'pore', 'pigmentation', 'blackheads', 'whiteheads', 'blemishes', 'dark circles', 'eye bags', 'dark spots'
        ]
        
        # Prepare the features dict with all the values set to 0 initially
        features_dict = {feature: 0 for feature in all_features}
        
        # Update the dictionary with features selected by the user
        for concern in concerns:
            if concern in features_dict:
                features_dict[concern] = 1
        
        # Prepare the data to send to the backend
        data = {
            'tone': tone,
            'type': skin_type,
            'features': features_dict  # Send the full features dictionary
        }

        # Log the data being sent to the backend
        logger.info(f"Sending recommendation request with data: {data}")
        
        # Make a request to the backend for recommendations
        try:
            response = requests.put(
                recommendation_url, 
                json=data,
                timeout=10  # Add timeout
            )
            response.raise_for_status()
            
            # Log the response status code and content
            logger.info(f"Recommendation API response status: {response.status_code}")
            recommendations = response.json()  # Receive recommendations from backend
            
            # Log the recommendations received from the backend
            logger.info(f"Received recommendations: {recommendations}")
            
            # Generate HTML content for cards
            product_cards = ""
            if recommendations.get("general"):
                for category, products in recommendations["general"].items():
                    for product in products:
                        card_html = f"""
                        <div style="border: 1px solid #ddd; padding: 10px; width: calc(33.33% - 20px); text-align: center; margin: 10px; border-radius: 10px; box-sizing: border-box; display: inline-block;">
                            <img src="{product['img']}" alt="{product['name']}" style="width: 100%; height: auto; border-radius: 10px;">
                            <h3>{product['name']}</h3>
                            <p>{product['brand']}</p>
                            <p><strong>{product['price']}</strong></p>
                            <a href="{product['url']}" target="_blank" style="display: inline-block; background-color: #1E90FF; color: white; padding: 10px 15px; border-radius: 5px; text-decoration: none;">Buy Now</a>
                        </div>
                        """
                        product_cards += card_html
            
            # Wrap the product cards in a responsive grid
            grid_html = f"""
            <div style="display: flex; flex-wrap: wrap; justify-content: space-around;">
                {product_cards}
            </div>
            """
            
            return grid_html  # Return the HTML formatted product cards in a responsive grid
        
        except requests.exceptions.RequestException as e:
            logger.error(f"Recommendation API request failed: {str(e)}")
            return {"error": f"Error fetching recommendations: {str(e)}"}

    except Exception as e:
        logger.error(f"Recommendation processing error: {str(e)}")
        return {"error": str(e)}

# Build the Gradio interface
with gr.Blocks() as demo:
    # Image upload section
    with gr.Row():
        with gr.Column():
            image_input = gr.Image(
                label="Upload Face Image",
                type="file",  # Type 'file' for image file upload
                interactive=True
            )
            submit_button = gr.Button("Analyze Face")
    
    # Form section
    with gr.Row():
        with gr.Column():
            tone_input = gr.Slider(
                minimum=1,
                maximum=6,
                label="Skin Tone",
                value=5,
                step=1
            )
            skin_type_input = gr.Radio(
                choices=["All", "Oily", "Normal", "Dry"],
                label="Skin Type",
                value="All"
            )
            acne_input = gr.Radio(
                choices=['Low', 'Moderate', 'Severe'],
                label="Acne Severity",
                value="Moderate",  # Default selected value
                interactive=True   # Make sure interactive is properly set
            )
            concerns_input = gr.CheckboxGroup(
                choices=[
                    'normal', 'dry', 'oily', 'combination', 'acne', 'sensitive', 'fine lines', 'wrinkles', 'redness',
                    'dull', 'pore', 'pigmentation', 'blackheads', 'whiteheads', 'blemishes', 'dark circles', 'eye bags', 'dark spots'
                ],
                label="Skin Concerns"
            )
            form_submit_button = gr.Button("Get Recommendations")


    # Output section
    with gr.Row():
        recommendation_output = gr.HTML(label="Recommended Products")

    # Event handlers
    submit_button.click(
        fn=capture_face_image,
        inputs=image_input,
        outputs=[recommendation_output, image_input]
    )
    
    form_submit_button.click(
        fn=get_recommendations,
        inputs=[tone_input, skin_type_input, concerns_input],
        outputs=recommendation_output
    )

# Launch the interface
demo.launch(
    server_name="127.0.1",
    server_port=7863,
    debug=True
)
gr.close_all()