app.py

import os
import json
import base64
from typing import List, Tuple, Dict
import gradio as gr
import httpx
from sqlalchemy import create_engine, text
from dotenv import load_dotenv
import google.generativeai as genai

def get_secret(secret_name, service="", username=""):
    try:
        from google.colab import userdata
        return userdata.get(secret_name)
    except:
        try:
            return os.environ[secret_name]
        except:
            import keyring
            return keyring.get_password(service, username)

# Load environment variables
load_dotenv()

# Database configuration
DB_NAME = "kroyscappingdb"
DB_USER = "read_only"
DB_PASSWORD = get_secret('FASHION_PG_PASS')
DB_HOST = "rc1d-vbh2dw5ha0gpsazk.mdb.yandexcloud.net"
DB_PORT = "6432"

DATABASE_URL = f"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}:{DB_PORT}/{DB_NAME}"

# Create the SQLAlchemy engine
db_conn = create_engine(DATABASE_URL)

# Configure Gemini API
genai.configure(api_key=get_secret("GEMINI_API_KEY"))

def get_marketplace_and_main_image(id_product_money: str) -> Tuple[str, str]:
    """Get marketplace and main image URL for a product."""
    query = text("""
        select mp, image as main_image_url
        from public.products
        where id_product_money = :id_product_money
    """)
    
    with db_conn.connect() as connection:
        result = connection.execute(query, {"id_product_money": id_product_money}).first()
        if result is None:
            raise ValueError(f"No product found with id_product_money: {id_product_money}")
        return result.mp, result.main_image_url

def get_additional_images(id_product_money: str, marketplace: str) -> List[str]:
    """Get additional images based on marketplace."""
    if marketplace == 'lamoda':
        query = text("""
            select info_chrc->'gallery' as more_images
            from public.lamoda_chrc_and_reviews
            where id_product_money = :id_product_money
            limit 1
        """)
        with db_conn.connect() as connection:
            result = connection.execute(query, {"id_product_money": id_product_money}).first()
            if result and result.more_images:
                paths = json.loads(result.more_images)
                return [f"https://a.lmcdn.ru/product{path}" for path in paths]
    
    elif marketplace == 'wildberries':
        query = text("""
            select features->'images' as more_images
            from public.wb_chrc
            where id_product_money = :id_product_money
            limit 1
        """)
        with db_conn.connect() as connection:
            result = connection.execute(query, {"id_product_money": id_product_money}).first()
            if result and result.more_images:
                return json.loads(result.more_images)
    
    return []

def download_and_encode_images(image_urls: List[str]) -> List[Dict]:
    """Download images and convert them to base64 format for Gemini."""
    encoded_images = []
    with httpx.Client() as client:
        for url in image_urls:
            try:
                response = client.get(url)
                response.raise_for_status()
                encoded_image = base64.b64encode(response.content).decode('utf-8')
                encoded_images.append({
                    'mime_type': 'image/jpeg',  # Assuming JPEG format
                    'data': encoded_image
                })
            except Exception as e:
                print(f"Error downloading image {url}: {str(e)}")
    return encoded_images

def get_gemini_response(model_name: str, encoded_images: List[Dict], prompt: str) -> str:
    """Get response from a Gemini model."""
    try:
        model = genai.GenerativeModel(model_name)
        response = model.generate_content(encoded_images + [prompt])
        return response.text
    except Exception as e:
        return f"Error with {model_name}: {str(e)}"

def process_input(id_product_money: str, prompt: str) -> Tuple[List[str], str, str]:
    """Main processing function."""
    try:
        # Get marketplace and main image
        marketplace, main_image = get_marketplace_and_main_image(id_product_money)
        
        # Get additional images
        additional_images = get_additional_images(id_product_money, marketplace)
        
        # Combine all images
        all_image_urls = [main_image] + additional_images
        
        # Download and encode images
        encoded_images = download_and_encode_images(all_image_urls)
        
        if not encoded_images:
            raise ValueError("No images could be downloaded")
        
        # Get responses from both models
        gemini_1_5_response = get_gemini_response("gemini-1.5-pro", encoded_images, prompt)
        gemini_2_0_response = get_gemini_response("gemini-pro-vision", encoded_images, prompt)
        
        return all_image_urls, gemini_1_5_response, gemini_2_0_response
    
    except Exception as e:
        return [], f"Error: {str(e)}", f"Error: {str(e)}"

# Create Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Product Image Analysis with Gemini Models")
    
    with gr.Row():
        id_input = gr.Textbox(label="Product ID (id_product_money)")
        prompt_input = gr.Textbox(label="Prompt for VLMs")
    
    submit_btn = gr.Button("Analyze")
    
    with gr.Row():
        image_gallery = gr.Gallery(label="Product Images")
    
    with gr.Row():
        with gr.Column():
            gr.Markdown("### Gemini 1.5 Pro Response")
            gemini_1_5_output = gr.Textbox(label="")
        with gr.Column():
            gr.Markdown("### Gemini Pro Vision Response")
            gemini_2_0_output = gr.Textbox(label="")
    
    submit_btn.click(
        fn=process_input,
        inputs=[id_input, prompt_input],
        outputs=[image_gallery, gemini_1_5_output, gemini_2_0_output]
    )

if __name__ == "__main__":
    demo.launch(share=True)