Update handler.py

handler.py

@@ -8,7 +8,54 @@ from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe
 import time
 import uuid
 from huggingface_hub import HfApi
-record=0
+
+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", "5000"])
+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(
@@ -22,9 +69,14 @@ 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.")
+
         
     def __call__(self, data: Dict[str, Any]) -> str:
-        global record
         logger.info(f"Received incoming request with {data=}")
 
         if "inputs" in data and isinstance(data["inputs"], str):
@@ -36,8 +88,7 @@ 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 record
+
         parameters = data.pop("parameters", {})
 
         num_inference_steps = parameters.get("num_inference_steps", 28)
@@ -48,20 +99,22 @@ class EndpointHandler:
         # seed generator (seed cannot be provided as is but via a generator)
         seed = parameters.get("seed", 0)
         generator = torch.manual_seed(seed)
-        record+=1
         start_time = time.time()
-        time.sleep(6)
-        # 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")
-        record-=1
+        filename = f"{uuid.uuid4()}.png"
+        image_path = f"/images/{filename}"
+
+        result.save(image_path)
+        image_url = f"{self.public_url+image_path}"
 
-        return record
+        return image_url