update README

README.md

@@ -13,12 +13,14 @@ short_description: RTMO PyTorch Checkpoint Tester
 
 # RTMO PyTorch Checkpoint Tester
 
-This HuggingFace Space provides a real-time 2D multi-person pose estimation demo using the RTMO model from OpenMMLab, accelerated with ZeroGPU.
+This HuggingFace Space provides a real-time 2D multi-person pose estimation demo using the RTMO model from OpenMMLab, accelerated with ZeroGPU. It supports both image and video inputs.
 
 ## Features
 
 - **Remote Checkpoint Selection**: Choose from multiple pre-trained variants (COCO, BODY7, CrowdPose, retrainable RTMO-s) via a dropdown.
 - **Custom Checkpoint Upload**: Upload your own `.pth` file; the application auto-detects RTMO-t/s/m/l variants.
+- **Image Input**: Upload images for single-frame pose estimation.
+- **Video Input**: Upload video files (e.g., `.mp4`, `.mov`, `.avi`, `.mkv`, `.webm`) to perform pose estimation on video sequences and view annotated outputs.
 - **Threshold Adjustment**: Fine-tune **Bounding Box Threshold** and **NMS Threshold** sliders to refine detections.
 - **Example Images**: Three license-free images with people are included for quick testing via the **Examples** panel.
 - **ZeroGPU Acceleration**: Utilizes the `@spaces.GPU()` decorator for GPU inference on HuggingFace Spaces.
@@ -26,10 +28,15 @@ This HuggingFace Space provides a real-time 2D multi-person pose estimation demo
 ## Usage
 
 1. **Upload Image**: Drag-and-drop or select an image in the **Upload Image** component (or choose from **Examples**).
-2. **Select Remote Checkpoint**: Pick a preloaded variant from the dropdown menu.
-3. **(Optional) Upload Your Own Checkpoint**: Provide a `.pth` file to override the remote selection; the model variant is detected automatically.
-4. **Adjust Thresholds**: Set **Bounding Box Threshold** (`bbox_thr`) and **NMS Threshold** (`nms_thr`) to control confidence and suppression behavior.
-5. **Run Inference**: Click **Run Inference**. The annotated image and the active checkpoint name will appear.
+2. **Upload Video**: Drag-and-drop or select a video file in the **Upload Video** component.
+3. **Select Remote Checkpoint**: Pick a preloaded variant from the dropdown menu.
+4. **(Optional) Upload Your Own Checkpoint**: Provide a `.pth` file to override the remote selection; the model variant is detected automatically.
+5. **Adjust Thresholds**: Set **Bounding Box Threshold** (`bbox_thr`) and **NMS Threshold** (`nms_thr`) to control confidence and suppression behavior.
+6. **Run Inference**: Click **Run Inference**.
+7. **View Results**:
+   - For images, the annotated image will appear in the **Annotated Image** panel.
+   - For videos, the annotated video will appear in the **Annotated Video** panel.
+   The active checkpoint name will appear below.
 
 ## Remote Checkpoints
 
@@ -54,10 +61,12 @@ The following variants are available out of the box:
 - **Monkey-Patch**: Applies a regex patch to bypass `mmdet`’s MMCV version assertion for compatibility.
 - **Variant Detection**: Inspects `backbone.stem.conv.conv.weight` channels in the checkpoint to select the correct RTMO variant.
 - **Checkpoint Management**: Remote files are downloaded to `/tmp/{key}.pth` on demand; uploads use the provided local path.
-- **Output**: Saves visualization images to `/tmp/vis` and displays the first result in the UI.
+- **Image & Video Support**: The `predict` function automatically handles both image and video inputs, saving annotated frames or video to `/tmp/vis` and displaying them in the UI.
+- **Output**: Saves visualization images or videos to `/tmp/vis` and displays them in the UI panels.
 
 ## Files
 
 - **app.py**: Main Gradio application script.
 - **requirements.txt**: Python dependencies, including MMCV and MMPose.
 - **README.md**: This documentation file.
+