app.py

from fastapi import FastAPI, File, UploadFile, HTTPException, Header, Form, BackgroundTasks
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from typing import Optional
import os
import asyncio
from models.color import extract_color_palette
from models.classifier import get_classifier
from models.material import detect_material_clip
from models.embeddings import get_image_embedding
from models.attribute_parser import generate_caption
from llm_utils import generate_description  # same as before, calls Seller Assistant

app = FastAPI(title="Advanced Product Intelligence Service")
app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])

@app.post("/api/v1/product-intelligence")
async def product_intelligence(
    file: UploadFile = File(...),
    generate_seo: Optional[bool] = Form(False),
    generate_caption: Optional[bool] = Form(False),   # BLIP caption
    api_key: Optional[str] = Header(None)
):
    contents = await file.read()
    
    # Run all models in parallel (some can be run concurrently)
    tasks = [
        extract_color_palette(contents),
        asyncio.to_thread(lambda: get_classifier().predict(contents)),
        asyncio.to_thread(lambda: detect_material_clip(contents)),
        asyncio.to_thread(get_image_embedding, contents)
    ]
    if generate_caption:
        tasks.append(asyncio.to_thread(generate_caption, contents))
    
    results = await asyncio.gather(*tasks)
    palette_info, classifier_output, material_info, embedding, caption = results[0], results[1], results[2], results[3], results[4] if generate_caption else None
    
    response = {
        "colors": palette_info,
        "category": classifier_output["category"],
        "category_confidence": classifier_output["category_confidence"],
        "attributes": classifier_output["attributes"],
        "material": material_info["primary_material"],
        "material_confidence": material_info["confidence"],
        "material_all_scores": material_info["all_scores"],
        "embedding": embedding,   # 512-dim vector
        "suggested_tags": [classifier_output["category"], material_info["primary_material"]] + palette_info["all_hex"][:2]
    }
    
    if generate_caption and caption:
        response["caption"] = caption
    
    if generate_seo:
        if not api_key:
            response["seo_title"] = f"Premium {material_info['primary_material']} {classifier_output['category']}"
            response["seo_description"] = f"High-quality {material_info['primary_material']} {classifier_output['category']} with {', '.join(palette_info['all_names'][:2])} tones."
        else:
            seo = await generate_description(
                classifier_output["category"], 
                material_info["primary_material"], 
                palette_info["all_hex"], 
                api_key
            )
            response["seo_title"] = seo.get("title", "")
            response["seo_description"] = seo.get("description", "")
    
    return JSONResponse(content=response)

@app.get("/health")
async def health():
    return {"status": "advanced intelligence ready"}