Update app.py

app.py

@@ -49,13 +49,7 @@ def count_people_in_frame(frame):
     # Apply Non-Maximum Suppression (NMS)
     indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4) if boxes else []
 
-    # Draw bounding boxes on the image
-    for i in indexes:
-        x, y, w, h = boxes[i]
-        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
-
-    # Return processed frame and number of people detected
-    return frame, len(indexes)
+    return len(indexes)
 
 def count_people_video(video_path):
     """
@@ -77,7 +71,7 @@ def count_people_video(video_path):
             break
 
         # Count people in the frame
-        _, people_count = count_people_in_frame(frame)
+        people_count = count_people_in_frame(frame)
         people_per_frame.append(people_count)
 
         frame_count += 1
@@ -85,18 +79,23 @@ def count_people_video(video_path):
     cap.release()
 
     # Generate analytics
-    return {
-        "People in Video": int(np.max(people_per_frame)) if people_per_frame else 0,
-    }
+    return f"Max People Detected in Video: {max(people_per_frame) if people_per_frame else 0}"
 
 def analyze_video(video_file):
-    result = count_people_video(video_file)
-    return "\n".join([f"{key}: {value}" for key, value in result.items()])
+    # Extract video path from uploaded file
+    video_path = video_file if isinstance(video_file, str) else video_file.name
+
+    # Ensure path exists
+    if not os.path.exists(video_path):
+        return "Error: Video file could not be loaded."
+
+    result = count_people_video(video_path)
+    return result
 
 def analyze_image(image):
     image_cv = np.array(image)  # Convert PIL image to NumPy array
-    processed_image, people_count = count_people_in_frame(image_cv)
-    return processed_image, f"People in Image: {people_count}"
+    people_count = count_people_in_frame(image_cv)
+    return image, f"People in Image: {people_count}"
 
 # Gradio Interface for Image Processing
 image_interface = gr.Interface(
@@ -110,7 +109,7 @@ image_interface = gr.Interface(
 # Gradio Interface for Video Processing
 video_interface = gr.Interface(
     fn=analyze_video,
-    inputs=gr.Video(label="Upload Video"),
+    inputs=gr.Video(type="file", label="Upload Video"),  # Ensure video is treated as a file
     outputs=gr.Textbox(label="People Counting Results"),
     title="YOLO People Counter (Video)",
     description="Upload a video to detect and count people using YOLOv3."
@@ -119,7 +118,7 @@ video_interface = gr.Interface(
 # Combine both interfaces into tabs
 app = gr.TabbedInterface(
     [image_interface, video_interface],
-    tab_names=["Image Mode", "Video Mode"]  # Explicitly define tab names
+    tab_names=["Image Mode", "Video Mode"]
 )
 
 # Launch app