Fix app.py

app.py

@@ -46,24 +46,10 @@ def load_pretrained_weights():
                 )
             )
     for file_url in tqdm(file_urls):
-        if not os.path.exists("gradio_demo/weights/" + os.path.basename(file_url)):
-            command = "wget -P gradio_demo/weights/ {}".format(file_url)
+        if not os.path.exists("weights/" + os.path.basename(file_url)):
+            command = "wget -P weights/ {}".format(file_url)
             subprocess.run(command, shell=True)
 
-    # Slowfast weights
-    if not os.path.exists("SLOWFAST_8x8_R50.pkl"):
-        subprocess.run(
-            "wget https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/kinetics400/SLOWFAST_8x8_R50.pkl",
-            shell=True,
-        )
-
-    # PANNs weights
-    if not os.path.exists("Cnn14_mAP=0.431.pth"):
-        subprocess.run(
-            "wget https://zenodo.org/record/3987831/files/Cnn14_mAP%3D0.431.pth",
-            shell=True,
-        )
-
     return file_urls
 
 
@@ -79,7 +65,7 @@ Model initialization
 """
 load_pretrained_weights()
 model = CGDETRPredictor(
-    "gradio_demo/weights/clip_cg_detr_qvhighlight.ckpt",
+    "weights/clip_cg_detr_qvhighlight.ckpt",
     device=device,
     feature_name="clip",
     slowfast_path=None,
@@ -147,11 +133,9 @@ def model_load(radio, video):
             raise gr.Error("Select from the models")
 
         model = model_class(
-            "gradio_demo/weights/{}_{}_qvhighlight.ckpt".format(feature, model_name),
+            "weights/{}_{}_qvhighlight.ckpt".format(feature, model_name),
             device=device,
             feature_name="{}".format(feature),
-            slowfast_path="SLOWFAST_8x8_R50.pkl",
-            pann_path="Cnn14_mAP=0.431.pth",
         )
 
         load_finished_msg = "Model loaded: {}".format(radio)
@@ -222,7 +206,7 @@ def predict(textbox, line, gallery):
         for i, (second, score) in enumerate(
             zip(highlighted_seconds, highlighted_scores)
         ):
-            output_path = "gradio_demo/highlight_frames/highlight_{}.png".format(i)
+            output_path = "highlight_frames/highlight_{}.png".format(i)
             (
                 ffmpeg.input(loaded_video_path, ss=second)
                 .output(output_path, vframes=1, qscale=2)
@@ -341,7 +325,7 @@ def main():
                     ],
                 )
 
-    demo.launch()
+    demo.launch(share=True, server_name="0.0.0.0")
 
 
 if __name__ == "__main__":