Update app.py

app.py

@@ -15,21 +15,26 @@ model_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/Rea
 model_path = 'RealESRGAN_x4plus.pth'
 
 if not os.path.exists(model_path):
-    print("Downloading AI model...")
+    print("Downloading AI model... please wait.")
     response = requests.get(model_url)
     with open(model_path, 'wb') as f:
         f.write(response.content)
 
-model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
-upsampler = RealESRGANer(
-    scale=4,
-    model_path=model_path,
-    model=model,
-    tile=400, # Prevents "Out of Memory" errors on free CPUs
-    tile_pad=10,
-    pre_pad=0,
-    half=False
-)
+# Initialize the AI Engine
+try:
+    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+    upsampler = RealESRGANer(
+        scale=4,
+        model_path=model_path,
+        model=model,
+        tile=400, 
+        tile_pad=10,
+        pre_pad=0,
+        half=False
+    )
+    print("✅ AI Engine Loaded Successfully")
+except Exception as e:
+    print(f"❌ Initialization Error: {e}")
 
 @app.get("/")
 def home():
@@ -38,22 +43,19 @@ def home():
 @app.post("/upscale")
 async def upscale(file: UploadFile = File(...)):
     try:
-        # Read image
         data = await file.read()
         nparr = np.frombuffer(data, np.uint8)
         img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
         
-        # Actual AI Processing
-        # outscale=4 makes it 4x bigger and sharper
+        # Process image (4x Upscale)
         output, _ = upsampler.enhance(img, outscale=4)
         
-        # Convert back to JPG
-        _, encoded_img = cv2.imencode('.jpg', output, [int(cv2.IMWRITE_JPEG_QUALITY), 90])
+        # Convert to high-quality JPG
+        _, encoded_img = cv2.imencode('.jpg', output, [int(cv2.IMWRITE_JPEG_QUALITY), 95])
         return Response(content=encoded_img.tobytes(), media_type="image/jpeg")
     
     except Exception as e:
-        print(f"Error: {str(e)}")
-        return {"error": "AI Engine busy or image too large"}
+        return {"error": str(e)}
 
 if __name__ == "__main__":
     import uvicorn