Update app.py

app.py

@@ -1,53 +1,35 @@
 import cv2
 import torch
-import gradio as gr
 from ultralytics import YOLO
-import numpy as np
-
-# Load YOLOv5 model (assuming weights are already downloaded)
-model = YOLO("yolov5s.pt")  # You can change to 'yolov5m.pt' or 'yolov5l.pt' for better accuracy
-
-def detect_objects_image(image):
-    results = model(image)
-    result_img = results[0].plot()  # Render image with bounding boxes
-    return result_img
-
-# Video detection function
-def detect_objects_video():
-    cap = cv2.VideoCapture(0)  # Capture from default webcam
-    cap.set(cv2.CAP_PROP_FPS, 30)  # Set FPS
-    
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-        
-        results = model(frame)
-        result_img = results[0].plot()
-        
-        _, buffer = cv2.imencode(".jpg", result_img)
-        yield buffer.tobytes()
-    
-    cap.release()
-
-def start_video():
-    return gr.Video(update=detect_objects_video, streaming=True)
-
-# Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("## Live Object Detection with YOLOv5")
-    
-    with gr.Row():
-        img_input = gr.Image(type="numpy")
-        img_output = gr.Image()
-        img_button = gr.Button("Detect Objects in Image")
-    
-    img_button.click(detect_objects_image, inputs=img_input, outputs=img_output)
-    
-    with gr.Row():
-        video_button = gr.Button("Start Live Video Detection")
-        video_output = gr.Video()
-    
-    video_button.click(start_video, outputs=video_output)
-
-demo.launch()
+
+# Load YOLOv5 model with GPU support if available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = YOLO("yolov5s.pt").to(device)  # You can use yolov5m.pt for better accuracy
+
+# Initialize video capture (Webcam)
+cap = cv2.VideoCapture(0)
+cap.set(cv2.CAP_PROP_FPS, 30)  # Ensure 30+ FPS
+cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)  # Set width
+cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)  # Set height
+
+while cap.isOpened():
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    # Perform object detection
+    results = model(frame)
+
+    # Plot the results (draw bounding boxes)
+    result_img = results[0].plot()
+
+    # Display the output in a window
+    cv2.imshow("YOLOv5 Live Object Detection", result_img)
+
+    # Break loop with 'q' key
+    if cv2.waitKey(1) & 0xFF == ord("q"):
+        break
+
+# Release resources
+cap.release()
+cv2.destroyAllWindows()