refactor: supabase image fetch

src/api/nto_api.py

@@ -619,7 +619,9 @@ async def necklace_try_on_with_points(necklace_try_on_id: NecklaceTryOnIDEntity
 @nto_cto_router.post("/clothingAndNecklaceTryOn")
 async def clothing_and_necklace_try_on(
         image: UploadFile = File(...),
-        necklace: UploadFile = File(...),
+        necklaceImageId: str = Form(...),
+        necklaceCategory: str = Form(...),
+        storename: str = Form(...),
         clothing_type: str = Form(...)
 ):
     logger.info("-" * 50)
@@ -632,70 +634,61 @@ async def clothing_and_necklace_try_on(
         return f"data:image/webp;base64,{base64.b64encode(buffer.getvalue()).decode('utf-8')}"
 
     try:
-        # Load both images concurrently
-        person_bytes, necklace_bytes = await asyncio.gather(
-            image.read(),
-            necklace.read()
-        )
-
-        # Convert bytes to PIL Images
+        person_bytes = await image.read()
         person_image = Image.open(BytesIO(person_bytes)).convert("RGB").resize((512, 512))
-        necklace_image = Image.open(BytesIO(necklace_bytes)).convert("RGBA")
+
+        jewellery_url = f"https://lvuhhlrkcuexzqtsbqyu.supabase.co/storage/v1/object/public/Stores/{storename}/{necklaceCategory}/image/{necklaceImageId}.png"
+        necklace_image = Image.open(returnBytesData(url=jewellery_url)).convert("RGBA")
+        
         logger.info(">>> IMAGES LOADED SUCCESSFULLY <<<")
 
-        # Generate mask and get shoulder points in one go
         mask, left_point, right_point = await pipeline.shoulderPointMaskGeneration_(image=person_image)
         logger.info(">>> MASK AND POINTS GENERATION COMPLETED <<<")
 
-        # Prepare base64 encodings concurrently
         mask_data_uri, image_data_uri = await asyncio.gather(
             asyncio.to_thread(image_to_base64, mask),
             asyncio.to_thread(image_to_base64, person_image)
         )
 
-        # Run CTO
         cto_output = replicate_run_cto({
             "mask": mask_data_uri,
             "image": image_data_uri,
             "prompt": f"Dull {clothing_type}, non-reflective clothing, properly worn, natural setting, elegant, natural look, neckline without jewellery, simple, perfect eyes, perfect face, perfect body, high quality, realistic, photorealistic, high resolution,traditional full sleeve blouse",
             "negative_prompt": "necklaces, jewellery, jewelry, necklace, neckpiece, garland, chain, neck wear, jewelled neck, jeweled neck, necklace on neck, jewellery on neck, accessories, watermark, text, changed background, wider body, narrower body, bad proportions, extra limbs, mutated hands, changed sizes, altered proportions, unnatural body proportions, blury, ugly",
-            "num_inference_steps": 20  # reduced from 25 for optimization
+            "num_inference_steps": 20
         })
 
         if not cto_output or not isinstance(cto_output, (list, tuple)) or not cto_output[0]:
             raise ValueError("Invalid output from clothing try-on")
 
-        # Download CTO result
         async with aiohttp.ClientSession() as session:
             async with session.get(str(cto_output[0])) as response:
                 if response.status != 200:
                     raise HTTPException(status_code=response.status, detail="Failed to fetch CTO output")
                 cto_result_bytes = await response.read()
 
-        # Process CTO result and perform NTO with points
         with BytesIO(cto_result_bytes) as buf:
             cto_result_image = Image.open(buf).convert("RGB")
 
-            # Use necklaceTryOnWithPoints directly with the points we already have
             result, headerText, _ = await pipeline.necklaceTryOnWithPoints_(
                 image=cto_result_image,
                 jewellery=necklace_image,
                 left_shoulder=left_point,
                 right_shoulder=right_point,
-                storename="default"
+                storename=storename
             )
-            result.show()
 
             if result is None:
                 raise ValueError("Failed to process necklace try-on")
 
-            final_base64 = await asyncio.to_thread(image_to_base64, result)
-
-        logger.info(f"Left Shoulder: {left_point}, Right Shoulder: {right_point}")
+            result_url = await supabase_upload_and_return_url(prefix="clothing_necklace_try_on", image=result)
+            
+            if not result_url:
+                raise ValueError("Failed to upload result image")
 
         response = {
             "code": 200,
-            "output": final_base64,
+            "output": result_url,
             "inference_time": round((time.time() - start_time), 2)
         }