Update app.py

app.py

@@ -195,6 +195,29 @@ def process_clip_from_file(prompt, frames, chatbot, fps, video_path, id):
     
     return chatbot
 
+# New synchronous function to process video clips and return events
+def process_clip_from_file_sync(prompt, frames, fps, video_path, id):
+    global stop_capture
+    if not stop_capture:
+        israel_tz = pytz.timezone('Asia/Jerusalem')
+        start_time = datetime.now(israel_tz).strftime('%H:%M:%S')
+        print("[Start]:", start_time, len(frames))
+        
+        frames_to_skip = int(fps)
+        base64Frames = process_frames(frames, frames_to_skip)
+        frames_count, processing_time, api_response = check_condition(prompt, base64Frames)
+        
+        if api_response and api_response.get("condition_met", False):
+            video_clip_path = clip_video_segment_2(video_path, id*LENGTH, LENGTH)
+            event = {
+                'event_id': id + 1,
+                'video_clip_path': video_clip_path,
+                'start_time': start_time,
+                'details': api_response.get('details', '')
+            }
+            return event
+    return None
+
 # Function to capture video frames
 def analyze_stream(prompt, stream, chatbot):
     global stop_capture
@@ -263,6 +286,49 @@ def analyze_video_file(prompt, video_path, chatbot):
             yield result
     return chatbot
 
+# New function to analyze video file synchronously and return events
+def analyze_video_file_sync(prompt, video_path):
+    global stop_capture
+    stop_capture = False  # Reset the stop flag when analysis starts
+
+    cap = cv2.VideoCapture(video_path)
+    
+    # Get video properties
+    fps = int(cap.get(cv2.CAP_PROP_FPS))  # Frames per second
+    frames_per_chunk = fps * LENGTH  # Number of frames per LENGTH-second chunk
+    
+    frames = []
+    chunk = 0
+    events = []
+    
+    # Create a thread pool for concurrent processing
+    with ThreadPoolExecutor(max_workers=4) as executor:
+        futures = []
+
+        while not stop_capture:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            frames.append(frame)
+            
+            # Split the video into chunks of frames corresponding to LENGTH seconds
+            if len(frames) >= frames_per_chunk:
+                futures.append(executor.submit(process_clip_from_file_sync, prompt, frames.copy(), fps, video_path, chunk))
+                frames = []
+                chunk+=1
+        
+        # If any remaining frames that are less than LENGTH seconds, process them as a final chunk
+        if len(frames) > 0:
+            futures.append(executor.submit(process_clip_from_file_sync, prompt, frames.copy(), fps, video_path, chunk))
+            chunk+=1
+        
+        cap.release()
+        # Collect results as threads complete
+        for future in as_completed(futures):
+            result = future.result()
+            if result is not None:
+                events.append(result)
+    return events
 
 # Function to stop video capture
 def stop_capture_func():
@@ -291,5 +357,13 @@ with gr.Blocks(title="Conntour", fill_height=True) as demo:
                 stop_btn = gr.Button("Stop")
             start_btn.click(analyze_stream, inputs=[prompt, stream, chatbot], outputs=[chatbot], queue=True)
             stop_btn.click(stop_capture_func)
+    # Add new API endpoint (without UI components)
+    api_interface = gr.Interface(
+        fn=analyze_video_file_sync,
+        inputs=[gr.Textbox(label="Prompt"), gr.Video(label="Video File")],
+        outputs=gr.JSON(label="Captured Events"),
+        live=False,
+    )
+    demo.add_component(api_interface)
 
-demo.launch(favicon_path='favicon.ico', auth=(user_name, password))
+demo.launch(favicon_path='favicon.ico', auth=(user_name, password))