Update app.py

app.py

@@ -1,3 +1,37 @@
 import gradio as gr
+from huggingface_hub import login
+from transformers import VideoClassificationPipeline, AutoModelForVideoClassification, AutoProcessor
+import torch
 
-gr.load("models/microsoft/xclip-base-patch32").launch()
+# 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"
+model = AutoModelForVideoClassification.from_pretrained(model_name)
+processor = AutoProcessor.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)
+
+# Define the function for video classification
+def classify_video(video_path):
+    predictions = pipeline(video_path)
+    return predictions
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=classify_video,
+    inputs=gr.Video(label="Upload a video for classification"),
+    outputs=gr.Label(num_top_classes=3, label="Top 3 Predicted Classes"),
+    title="Video Classification using Hugging Face",
+    description="Upload a video file and get the top 3 predicted classes using a Hugging Face video classification model."
+)
+
+# Launch the Gradio interface
+if __name__ == "__main__":
+    interface.launch()