Update handler.py

handler.py

@@ -6,49 +6,6 @@ from diffusers import FluxPipeline
 from huggingface_inference_toolkit.logging import logger
 from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe
 import time
-import uuid
-from huggingface_hub import HfApi
-
-from pyngrok import ngrok
-import subprocess
-from fastapi import FastAPI
-from fastapi.responses import FileResponse
-import uvicorn
-import threading
-
-image_directory='./images'
-if not os.path.exists(image_directory):
-    os.makedirs(image_directory)
-
-app = FastAPI()
-
-@app.get("/images/{image_name}")
-async def get_image(image_name: str):
-    image_path = os.path.join(image_directory, image_name)
-    
-    if os.path.exists(image_path):
-        return FileResponse(image_path)
-    else:
-        return {"error": "Image not found"}
-
-def run_uvicorn():
-    uvicorn.run(app, host="127.0.0.1", port=6000)
-
-uvicorn_thread = threading.Thread(target=run_uvicorn)
-uvicorn_thread.daemon = True  
-uvicorn_thread.start()
-
-authtoken = "2cvqFKWc1eb9b0aN7pRLDUBfEtC_2FUehxFL8CAKXRkW3Hfjo"
-commands = [
-    # "snap install ngrok",
-    f"ngrok config add-authtoken {authtoken}"
-]
-for command in commands:
-    try:
-        subprocess.run(command, shell=True, check=True)
-        logger.info(f"SUCCESS CMD: {command}")
-    except subprocess.CalledProcessError as e:
-        logger.info(f"Failed CMD: {e}")
 
 
 class EndpointHandler:
@@ -64,13 +21,8 @@ class EndpointHandler:
         self.pipe.vae = torch.compile(
             self.pipe.vae, mode="max-autotune-no-cudagraphs",
         )
-        # ngrok
-        self.public_url = ngrok.connect(6000).public_url
-        subprocess.Popen(['ngrok', 'http', '6000'], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-        
-        logger.info("Ngrok is running in the background.")
+        self.record=0
 
-        
     def __call__(self, data: Dict[str, Any]) -> str:
         logger.info(f"Received incoming request with {data=}")
 
@@ -95,21 +47,17 @@ class EndpointHandler:
         seed = parameters.get("seed", 0)
         generator = torch.manual_seed(seed)
         start_time = time.time()
-        result = self.pipe(  # type: ignore
-            prompt,
-            height=height,
-            width=width,
-            guidance_scale=guidance_scale,
-            num_inference_steps=num_inference_steps,
-            generator=generator,
-        ).images[0]
+        # result = self.pipe(  # type: ignore
+        #     prompt,
+        #     height=height,
+        #     width=width,
+        #     guidance_scale=guidance_scale,
+        #     num_inference_steps=num_inference_steps,
+        #     generator=generator,
+        # ).images[0]
         end_time = time.time()
         time_taken = end_time - start_time
         print(f"Time taken: {time_taken:.2f} seconds")
-        filename = f"{uuid.uuid4()}.png"
-        image_path = f"/images/{filename}"
-
-        result.save(image_path)
-        image_url = f"{self.public_url+image_path}"
+        self.record+=1
 
-        return image_url
+        return self.record