Update app.py

app.py

@@ -1,27 +1,28 @@
 import gradio as gr
 from huggingface_hub import login
-from transformers import VideoClassificationPipeline, AutoModelForVideoClassification, AutoProcessor
+from transformers import AutoModelForVideoClassification, AutoFeatureExtractor, pipeline
 import torch
 
 # Load the Hugging Face API token from environment variables or enter directly
 # HUGGINGFACEHUB_API_TOKEN = "your_huggingface_api_token"
 # login(HUGGINGFACEHUB_API_TOKEN)
 
-# Define the model and processor from Hugging Face
-model_name = "microsoft/xclip-base-patch32"
+# Define the model and feature extractor from Hugging Face
+# model_name = "microsoft/xclip-base-patch32"
+model_name = "facebook/timesformer-base-finetuned-k400"
 model = AutoModelForVideoClassification.from_pretrained(model_name)
-processor = AutoProcessor.from_pretrained(model_name)
+feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
 
 # Create a video classification pipeline
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 
-pipeline = VideoClassificationPipeline(model=model, feature_extractor=processor, device=0 if torch.cuda.is_available() else -1)
+video_pipeline = pipeline("video-classification", model=model, feature_extractor=feature_extractor, device=0 if torch.cuda.is_available() else -1)
 
 # Define the function for video classification
 def classify_video(video_path):
-    predictions = pipeline(video_path)
-    return predictions
+    predictions = video_pipeline(video_path)
+    return {prediction['label']: prediction['score'] for prediction in predictions}
 
 # Create a Gradio interface
 interface = gr.Interface(