Update app.py

app.py

@@ -138,29 +138,11 @@ def detect_video(video):
     # Release resources
     cap.release()
 
-# def display_two_videos():
-#     # Replace these paths with the paths to your video files
-#     video_path_1 = "data/c_base_detected.mp4"
-#     video_path_2 = "data/c_tuned_detected.mp4"
-    
-#     return open(video_path_1, "rb"), open(video_path_2, "rb")
-
-def display_two_videos():
-    path1= os.path.join(os.path.dirname(__file__), "data/c_base_detected.mp4")
-    path2= os.path.join(os.path.dirname(__file__), "data/c_tuned_detected.mp4")
-    cap1 = cv2.VideoCapture(path1)
-    cap2 = cv2.VideoCapture(path2)
-
-    while True:
-        ret1, frame1 = cap1.read()
-        ret2, frame2 = cap2.read()
-        if not ret1 or not ret2:
-            break
-    yield frame1, frame2  # Yield frames from both videos as a tuple
-
-   # return open(path1, "rb"), open(path2, "rb")
+a = os.path.join(os.path.dirname(__file__), "data/c_base_detected.mp4")  # Video
+b = os.path.join(os.path.dirname(__file__), "data/c_tuned_detected.mp4")  # Video
 
-#os.path.join(os.path.dirname(__file__), "files/a.mp4")
+def video_demo(video1, video2):
+    return [video, video2]
 
 label_id_offset = 0    
 REPO_ID = "apailang/mytfodmodel"
@@ -231,12 +213,12 @@ tuned_image = gr.Interface(
 
 
 video = gr.Interface(
-    fn=display_two_videos,
-    inputs=[
-        # gr.Textbox(label="Path to detected base model Video",value="data/c_base_detected.mp4",info="video has been pre-processed"),
-        # gr.Textbox(label="Path to tuned base model  Video",value="data/c_tuned_detected.mp4",info="video has been pre-processed")
+    fn=video_demo,
+    inputs=[gr.Video(label="base model Video"),gr.Textbox(label="tuned model Video")],
+    outputs=[gr.Video(label="base model", gr.Video(label="Tuned model")],  # Specify video outputs
+    examples=[
+        [a, b]
     ],
-    outputs=[gr.Video(label="base model", interpretation="default"), gr.Video(label="Tuned model", interpretation="default")],  # Specify video outputs
     title="Comparing base vs tuned detected video",
     description="using SSD mobile net V2 320x320. Model has been customed trained to detect Character of Luffy and Chopper"
 )