Update app.py

app.py

@@ -26,6 +26,23 @@ import PIL.Image
 from PIL import Image
 from io import BytesIO
 import torchvision.transforms as T
+import requests
+from pathlib import Path
+
+MODEL_URL = "https://www.dropbox.com/s/04suaimdpru76h3/ArtLine_920.pkl?dl=1"
+MODEL_PATH = "ArtLine_920.pkl"
+
+# Download the model file
+response = requests.get(MODEL_URL)
+if response.status_code == 200:
+    with open(MODEL_PATH, 'wb') as f:
+        f.write(response.content)
+else:
+    print("Failed to download the model")
+
+# Load the model using FastAI
+path = Path(".")
+learn = load_learner(path, MODEL_PATH)
 
 app = FastAPI()
 
@@ -64,32 +81,6 @@ def add_margin(pil_img, top, right, bottom, left, color):
     result.paste(pil_img, (left, top))
     return result
 
-from fastapi import HTTPException
-from httpx import AsyncClient
-
-MODEL_URL = "https://www.dropbox.com/s/04suaimdpru76h3/ArtLine_920.pkl?dl=1"
-
-async def download_model(url: str, filename: str):
-    async with AsyncClient() as client:
-        response = await client.get(url)
-        if response.status_code == 200:
-            with open(filename, "wb") as f:
-                f.write(response.content)
-        else:
-            raise HTTPException(status_code=response.status_code, detail="Failed to download model")
-
-# Define the function to download the model asynchronously
-async def setup_model():
-    await download_model(MODEL_URL, "ArtLine_920.pkl")
-
-# Run the setup function to download the model before the FastAPI app starts
-import asyncio
-loop = asyncio.get_event_loop()
-loop.run_until_complete(setup_model())
-
-# Now load the learner once the model is downloaded
-path = Path(".")
-learn = load_learner(path, 'ArtLine_920.pkl')