Update app.py

app.py

@@ -6,7 +6,7 @@ import os
 # Load YOLO model
 net = cv2.dnn.readNet('yolov3.weights', 'yolov3.cfg')
 
-# Set backend (CPU or GPU)
+# Set backend (CPU)
 net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
 net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
 
@@ -98,15 +98,27 @@ def analyze_image(image):
     processed_image, people_count = count_people_in_frame(image_cv)
     return processed_image, f"People in Image: {people_count}"
 
-# Gradio Interface
-interface = gr.Interface(
-    fn=[analyze_image, analyze_video],  # Supports both image & video
-    inputs=[gr.Image(type="pil", label="Upload Image"), gr.Video(label="Upload Video")],
+# Gradio Interface for Image Processing
+image_interface = gr.Interface(
+    fn=analyze_image,
+    inputs=gr.Image(type="pil", label="Upload Image"),
     outputs=[gr.Image(label="Processed Image"), gr.Textbox(label="People Counting Results")],
-    title="YOLO-based People Counter",
-    description="Upload an image or video to detect and count people using YOLOv3."
+    title="YOLO People Counter (Image)",
+    description="Upload an image to detect and count people using YOLOv3."
 )
 
+# Gradio Interface for Video Processing
+video_interface = gr.Interface(
+    fn=analyze_video,
+    inputs=gr.Video(label="Upload Video"),
+    outputs=gr.Textbox(label="People Counting Results"),
+    title="YOLO People Counter (Video)",
+    description="Upload a video to detect and count people using YOLOv3."
+)
+
+# Combine both interfaces into tabs
+app = gr.TabbedInterface([image_interface, video_interface], ["Image Mode", "Video Mode"])
+
 # Launch app
 if __name__ == "__main__":
-    interface.launch()
+    app.launch()