update app

app.py

@@ -20,12 +20,11 @@ from huggingface_hub import from_pretrained_keras
 IMG_SIZE = 224
 NUM_FEATURES = 2048
 
-model = from_pretrained_keras("ChainYo/video-classification-cnn-rnn")
+model = from_pretrained_keras("keras-io/video-classification-cnn-rnn")
 samples = []
 for file in os.listdir("samples"):
-    print(file)
     tag = file.split("_")[1]
-    samples.append([f"samples/{file}", 25])
+    samples.append([f"samples/{file}", 20])
 
 
 def crop_center_square(frame):
@@ -96,9 +95,10 @@ def sequence_prediction(path):
     frame_features, frame_mask = prepare_video(frames)
     probabilities = model.predict([frame_features, frame_mask])[0]
 
+    preds = {}
     for i in np.argsort(probabilities)[::-1]:
-        print(f"  {class_vocab[i]}: {probabilities[i] * 100:5.2f}%")
-    return frames
+        preds[class_vocab[i]] = float(probabilities[i])
+    return preds
 
 
 def to_gif(images):
@@ -109,10 +109,10 @@ def to_gif(images):
 article = article = "<div style='text-align: center;'><a href='https://github.com/ChainYo' target='_blank'>Space by Thomas Chaigneau</a><br><a href='https://keras.io/examples/vision/video_classification/' target='_blank'>Keras example by Sayak Paul</a></div>"
 app = gr.Interface(
     sequence_prediction,
-    inputs=[gr.inputs.Video(label="Video", type="mp4")],
-    outputs=[],
+    inputs=[gr.inputs.Video(label="Video", type="avi")],
+    outputs=[gr.outputs.Label(label="Prediction", type="confidences")],
     title="Keras Video Classification CNN-RNN model",
     description="Keras Working Group",
     article=article,
     examples=samples
-).launch(enable_queue=True, cache_examples=True)
+).launch(enable_queue=True)