Update app.py

app.py

@@ -74,64 +74,4 @@ interface_image = gr.Interface(
     theme='huggingface'
 )
 
-##################################################Video Inference################################################################
-def show_preds_video(
-    video_path: str = None,
-    model_path: str = None,
-    image_size: int = 640,
-    conf_threshold: float = 0.25,
-    iou_threshold: float = 0.45,
-):
-    cap = cv2.VideoCapture(video_path)
-
-    while cap.isOpened():
-        success, frame = cap.read()
-
-        if success:
-            model = YOLO(model_path)
-            model.overrides['conf'] = conf_threshold
-            model.overrides['iou'] = iou_threshold
-            model.overrides['agnostic_nms'] = False
-            model.overrides['max_det'] = 1000 
-            results = model.predict(frame)
-            annotated_frame = results[0].plot()
-
-            # Do not display the frame using cv2.imshow
-            # cv2.imshow("YOLOv8 Inference", annotated_frame)
-
-            # Break the loop if 'q' is pressed
-            if cv2.waitKey(1) & 0xFF == ord("q"):
-                break
-        else:
-            break
-
-    cap.release()
-    cv2.destroyAllWindows()
-
-
-inputs_video = [
-    gr.components.Video(type="filepath", label="Input Video"),
-    gr.inputs.Dropdown(["foduucom/stockmarket-future-prediction"], 
-                       default="foduucom/stockmarket-future-prediction", label="Model"),
-    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
-
-]
-outputs_video = gr.outputs.Image(type="filepath", label="Output Video")
-video_path=[['test/testvideo.mp4','foduucom/stockmarket-future-prediction', 640, 0.25, 0.45]]
-interface_video = gr.Interface(
-    fn=show_preds_video,
-    inputs=inputs_video,
-    outputs=outputs_video,
-    title=model_heading,
-    description=description,
-    examples=video_path,
-    cache_examples=False,
-    theme='huggingface'
-)
-
-gr.TabbedInterface(
-    [interface_image, interface_video],
-    tab_names=['Image inference', 'Video inference']
-).queue().launch()
+interface_image.launch(debug=True, enable_queue=True)