Update handler.py

handler.py

@@ -9,53 +9,6 @@ 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}")
-def run_ngrok():
-    subprocess.run(["ngrok", "http", "6000"])
-ngrok_thread = threading.Thread(target=run_ngrok)
-ngrok_thread.daemon = True 
-ngrok_thread.start()
-logger.info("ngrok is running in the background")
-
 class EndpointHandler:
     def __init__(self, path=""):
         self.pipe = FluxPipeline.from_pretrained(
@@ -69,13 +22,7 @@ class EndpointHandler:
         self.pipe.vae = torch.compile(
             self.pipe.vae, mode="max-autotune-no-cudagraphs",
         )
-        # ngrok
-        self.public_url = os.getcwd()
-        # self.public_url = ngrok.connect(6000).public_url
-        # subprocess.Popen(['ngrok', 'http', '5000'], 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=}")
@@ -89,7 +36,8 @@ class EndpointHandler:
                 "Provided input body must contain either the key `inputs` or `prompt` with the"
                 " prompt to use for the image generation, and it needs to be a non-empty string."
             )
-
+        if prompt=="get_quene":
+            return self.record
         parameters = data.pop("parameters", {})
 
         num_inference_steps = parameters.get("num_inference_steps", 28)
@@ -100,7 +48,9 @@ class EndpointHandler:
         # seed generator (seed cannot be provided as is but via a generator)
         seed = parameters.get("seed", 0)
         generator = torch.manual_seed(seed)
+        self.record+=1
         start_time = time.time()
+        sleep(10)
         result = self.pipe(  # type: ignore
             prompt,
             height=height,
@@ -112,10 +62,6 @@ class EndpointHandler:
         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