Update app.py

app.py

@@ -77,64 +77,6 @@ async def upload_image(file: UploadFile = File(...)):
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"Error processing image: {str(e)}")
 
-
-@app.post("/upload-video/")
-async def upload_video(file: UploadFile = File(...)):
-    """Upload a video, process it frame by frame, and return the processed video."""
-    if model is None:
-        raise HTTPException(status_code=500, detail="Model not loaded. Please upload '12x.pt' to run detection.")
-
-    try:
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_video:
-            shutil.copyfileobj(file.file, temp_video)
-            temp_video_path = temp_video.name
-
-        cap = cv2.VideoCapture(temp_video_path)
-        if not cap.isOpened():
-            os.remove(temp_video_path)
-            raise HTTPException(status_code=400, detail="Could not open video file.")
-
-        # Get video properties
-        fps = int(cap.get(cv2.CAP_PROP_FPS))
-        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-
-        # Output video
-        output_video_path = temp_video_path.replace(".mp4", "_processed.mp4")
-        out = cv2.VideoWriter(output_video_path, fourcc, fps, (width, height))
-
-        frame_interval = 5  # Process every 5th frame for efficiency
-        frame_index = 0
-
-        while True:
-            ret, frame = cap.read()
-            if not ret:
-                break
-
-            if frame_index % frame_interval == 0:
-                predictions, _ = process_frame(frame)
-
-                # Draw bounding boxes on the frame
-                for pred in predictions:
-                    x1, y1, x2, y2 = map(int, pred["bbox"])
-                    label = f"{pred['class']} ({pred['confidence']:.2f})"
-                    cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
-                    cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
-
-            out.write(frame)
-            frame_index += 1
-
-        cap.release()
-        out.release()
-        os.remove(temp_video_path)  # Clean up temp file
-
-        return FileResponse(output_video_path, media_type="video/mp4", filename="processed_video.mp4")
-
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error processing video: {str(e)}")
-
-
 @app.get("/")
 def home():
     return {"message": "🎯 Object Detection API for Images and Videos using 12x.pt"}