Update app.py

app.py

@@ -21,7 +21,7 @@ templates = Jinja2Templates(directory="templates")
 
 def predict_yolo(image_path):
     # Load a model
-    model = YOLO('yolov8n.pt')  # pretrained YOLOv8n model
+    model = YOLO('/usr/src/ultralytics/yolov8n.pt')  # pretrained YOLOv8n model
 
     # Run batched inference on a list of images
     results = model(image_path)  # return a list of Results objects
@@ -73,4 +73,24 @@ async def read_root():
 
 @app.get("/")
 async def read_root():
+    # Download image content from URL
+    image_url = "https://s1.eestatic.com/2018/04/10/actualidad/actualidad_298735505_130172549_1000x500.jpg"
+    response = requests.get(image_url)
+    response.raise_for_status()
+
+    # Open the image using PIL
+    image = Image.open(io.BytesIO(response.content))
+    # Perform YOLO prediction
+    predictions = predict_yolo(image_url)
+    
+    # Draw bounding boxes on the image
+    image_np = np.array(image)
+    image_with_boxes = draw_boxes(image_np, predictions)
+    
+    # Save the image with bounding boxes
+    image_with_boxes_path = f"/tmp/{file.filename.split('.')[0]}_with_boxes.jpg"
+    cv2.imwrite(image_with_boxes_path, cv2.cvtColor(image_with_boxes, cv2.COLOR_RGB2BGR))
+    
+    # Render the HTML with the image and bounding boxes
+    return templates.TemplateResponse("prediction.html", {"request": file, "image_path": image_with_boxes_path})
     return {"message": "Hello, this is a YOLO prediction API using FastAPI!"}