Update README.md

README.md

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----
-title: GenVidBench - Video Action Recognition
-emoji: 🎬
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 4.0.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: State-of-the-art video action recognition using MMAction2
----
-
-# GenVidBench - Video Action Recognition
-
-A powerful video analysis tool that uses state-of-the-art deep learning models to recognize actions and activities in videos. Built on top of MMAction2 framework with a user-friendly Gradio interface.
-
-## 🚀 Features
-
-- **Action Recognition**: Identify actions and activities in videos using TSN (Temporal Segment Networks)
-- **Top-5 Predictions**: Get the most likely actions with confidence scores
-- **Multiple Formats**: Support for MP4, AVI, MOV, and other video formats
-- **Real-time Processing**: Fast inference optimized for web deployment
-- **User-friendly Interface**: Clean and intuitive Gradio web interface
-
-## 🎯 Model Details
-
-This demo uses:
-- **Model**: TSN (Temporal Segment Networks) with ResNet-50 backbone
-- **Dataset**: Trained on Kinetics-400 dataset (400 action classes)
-- **Framework**: MMAction2 (OpenMMLab)
-- **Input**: RGB video frames
-- **Output**: Top-5 action predictions with confidence scores
-
-## 🛠️ Technical Stack
-
-- **Backend**: Python, PyTorch, MMAction2
-- **Frontend**: Gradio
-- **Video Processing**: OpenCV, Decord
-- **Deployment**: Hugging Face Spaces
-
-## 📖 How to Use
-
-1. **Upload Video**: Click the upload area or drag and drop your video file
-2. **Wait for Processing**: The model will analyze your video (usually takes a few seconds)
-3. **View Results**: See the top 5 predicted actions with confidence scores
-
-## 💡 Tips for Best Results
-
-- **Video Length**: Shorter videos (under 30 seconds) process faster
-- **Video Quality**: Clear, well-lit videos work best
-- **Action Clarity**: Videos with clear, distinct actions yield better results
-- **Supported Formats**: MP4, AVI, MOV, and other common video formats
-
-## 🔬 Supported Actions
-
-The model can recognize 400 different action classes from the Kinetics-400 dataset, including:
-- Sports activities (basketball, soccer, tennis, etc.)
-- Daily activities (cooking, cleaning, reading, etc.)
-- Physical exercises (push-ups, jumping jacks, etc.)
-- Musical activities (playing instruments, singing, etc.)
-- And many more!
-
-## 🏗️ Architecture
-
-```
-Video Input → Frame Sampling → Feature Extraction → Classification → Top-5 Predictions
-```
-
-## 📊 Performance
-
-- **Accuracy**: State-of-the-art performance on Kinetics-400
-- **Speed**: Optimized for real-time inference
-- **Memory**: Efficient GPU/CPU utilization
-
-## 🤝 Contributing
-
-This project is part of the GenVidBench framework. Contributions are welcome!
-
-## 📄 License
-
-This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
-
-## 🙏 Acknowledgments
-
-- [MMAction2](https://github.com/open-mmlab/mmaction2) - The underlying framework
-- [OpenMMLab](https://openmmlab.com/) - For the excellent computer vision tools
-- [Hugging Face](https://huggingface.co/) - For the deployment platform
-
----
-
+---
+title: GenVidBench - Video Action Recognition
+emoji: 🎬
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 5.47.2
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: State-of-the-art video action recognition using MMAction2
+---
+
+# GenVidBench - Video Action Recognition
+
+A powerful video analysis tool that uses state-of-the-art deep learning models to recognize actions and activities in videos. Built on top of MMAction2 framework with a user-friendly Gradio interface.
+
+## 🚀 Features
+
+- **Action Recognition**: Identify actions and activities in videos using TSN (Temporal Segment Networks)
+- **Top-5 Predictions**: Get the most likely actions with confidence scores
+- **Multiple Formats**: Support for MP4, AVI, MOV, and other video formats
+- **Real-time Processing**: Fast inference optimized for web deployment
+- **User-friendly Interface**: Clean and intuitive Gradio web interface
+
+## 🎯 Model Details
+
+This demo uses:
+- **Model**: TSN (Temporal Segment Networks) with ResNet-50 backbone
+- **Dataset**: Trained on Kinetics-400 dataset (400 action classes)
+- **Framework**: MMAction2 (OpenMMLab)
+- **Input**: RGB video frames
+- **Output**: Top-5 action predictions with confidence scores
+
+## 🛠️ Technical Stack
+
+- **Backend**: Python, PyTorch, MMAction2
+- **Frontend**: Gradio
+- **Video Processing**: OpenCV, Decord
+- **Deployment**: Hugging Face Spaces
+
+## 📖 How to Use
+
+1. **Upload Video**: Click the upload area or drag and drop your video file
+2. **Wait for Processing**: The model will analyze your video (usually takes a few seconds)
+3. **View Results**: See the top 5 predicted actions with confidence scores
+
+## 💡 Tips for Best Results
+
+- **Video Length**: Shorter videos (under 30 seconds) process faster
+- **Video Quality**: Clear, well-lit videos work best
+- **Action Clarity**: Videos with clear, distinct actions yield better results
+- **Supported Formats**: MP4, AVI, MOV, and other common video formats
+
+## 🔬 Supported Actions
+
+The model can recognize 400 different action classes from the Kinetics-400 dataset, including:
+- Sports activities (basketball, soccer, tennis, etc.)
+- Daily activities (cooking, cleaning, reading, etc.)
+- Physical exercises (push-ups, jumping jacks, etc.)
+- Musical activities (playing instruments, singing, etc.)
+- And many more!
+
+## 🏗️ Architecture
+
+```
+Video Input → Frame Sampling → Feature Extraction → Classification → Top-5 Predictions
+```
+
+## 📊 Performance
+
+- **Accuracy**: State-of-the-art performance on Kinetics-400
+- **Speed**: Optimized for real-time inference
+- **Memory**: Efficient GPU/CPU utilization
+
+## 🤝 Contributing
+
+This project is part of the GenVidBench framework. Contributions are welcome!
+
+## 📄 License
+
+This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
+
+## 🙏 Acknowledgments
+
+- [MMAction2](https://github.com/open-mmlab/mmaction2) - The underlying framework
+- [OpenMMLab](https://openmmlab.com/) - For the excellent computer vision tools
+- [Hugging Face](https://huggingface.co/) - For the deployment platform
+
+---
+
 **Note**: This is a demonstration of video action recognition capabilities. For production use, consider additional validation and error handling.