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.gitattributes

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0.0001, \""center_displacement\"": {\""x\"": -0.0001, \""y\"": 0}}}, {\""timestamp\"": 3.033333333333333, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.0687, \""acceleration\"": 3.5291, \""fluidity\"": 0.062, \""expansion\"": 0.164, \""total_displacement\"": 0.0016, \""center_displacement\"": {\""x\"": 0.0004, \""y\"": 0.0016}}}, {\""timestamp\"": 3.05, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.0566, \""acceleration\"": 2.9811, \""fluidity\"": 0.038, \""expansion\"": 0.164, \""total_displacement\"": 0.0008, \""center_displacement\"": {\""x\"": 0.0001, \""y\"": 0.0008}}}, {\""timestamp\"": 3.0666666666666664, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.0508, \""acceleration\"": 2.5782, \""fluidity\"": 0.037, \""expansion\"": 0.164, \""total_displacement\"": 0.0004, \""center_displacement\"": {\""x\"": 0, \""y\"": 0.0004}}}, {\""timestamp\"": 3.0833333333333335, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.0475, \""acceleration\"": 2.4533, \""fluidity\"": 0.034, \""expansion\"": 0.16, \""total_displacement\"": 0.0002, \""center_displacement\"": {\""x\"": 0.0001, \""y\"": 0.0002}}}, {\""timestamp\"": 3.1, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.044, \""acceleration\"": 2.1114, \""fluidity\"": 0.027, \""expansion\"": 0.159, \""total_displacement\"": 0.0004, \""center_displacement\"": {\""x\"": 0.0003, \""y\"": 0.0002}}}], \""capabilities\"": {\""has_joints\"": true, \""has_bones\"": true, \""has_direction_arrows\"": true, \""has_motion_trails\"": true, \""has_laban\"": true}}""}",2025-06-09 16:49:29.087331

README.md

@@ -1,12 +1,239 @@
 ---
-title: Gradio Overlay Video
-emoji: 📚
-colorFrom: gray
+tags: [gradio-custom-component, gradio-5, laban-movement-analysis, pose-visualisation, pose-estimation, movement-visualisation, visualisation, video]
+title: gradio_overlay_video
+emoji: 🏃
+short_description: pose data overlayed video controller
+colorFrom: purple
 colorTo: green
 sdk: gradio
 sdk_version: 5.33.0
-app_file: app.py
 pinned: false
+app_file: space.py
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# `gradio_overlay_video`
+<img alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.0.8%20-%20orange">  
+
+overlayed video controller
+
+## Installation
+
+```bash
+pip install gradio_overlay_video
+```
+
+## Usage
+
+```python
+
+import gradio as gr
+from gradio_overlay_video import OverlayVideo
+from pathlib import Path
+
+DEMO_DIR = Path(__file__).parent.parent
+sample_video_path = DEMO_DIR / "files/balette.mp4"
+sample_json_path = DEMO_DIR / "files/mediapipe_full_kp_balette.json"
+mediapipe_json_path = DEMO_DIR / "files/mediapipe_heavy_kp_parkour.json"
+movenet_json_path = DEMO_DIR / "files/movenet_thunder_kp_skate.json"
+yolo8_json_path = DEMO_DIR / "files/yolov8_kp_dance.json"
+yolo11_json_path = DEMO_DIR / "files/yolov11.json"
+
+def prepare_visualization_data(json_path, video_path):
+    """
+    This function simply validates the inputs and passes them to the
+    custom OverlayVideo component for frontend processing.
+    """
+    if not json_path:
+        raise gr.Error("A JSON file is required to generate a visualization.")
+
+    print(f"✅ Preparing visualization with JSON: {json_path}")
+    if video_path:
+        print(f"✅ Video background provided: {video_path}")
+    else:
+        print("ℹ️ No video background provided. Visualization will be on a black background.")
+
+    # The backend's job is just to pass the filepaths to the frontend.
+    # The return format (video_path, json_path) must match what postprocess expects.
+    return (video_path, json_path)
+
+
+with gr.Blocks(theme=gr.themes.Default(primary_hue="rose", secondary_hue="pink")) as demo:
+    gr.Markdown(
+        "# 🩰 Interactive Pose Visualization\n"
+        "1. **Upload a JSON file** with pose data.\n"
+        "2. **(Optional) Upload a video** to use as the background.\n"
+        "3. Click 'Display Visualization' to see the interactive result."
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Use standard gr.File for robust input handling
+            json_upload = gr.File(
+                label="Upload Required JSON File",
+                file_types=[".json"],
+                type="filepath"
+            )
+            video_upload = gr.File(
+                label="Upload Optional Video File",
+                file_types=["video"],
+                type="filepath",
+                value=None 
+            )
+            btn = gr.Button("Display Visualization", variant="primary")
+        
+        with gr.Column(scale=1):
+            output_ov = OverlayVideo(label="Output", interactive=False, autoplay=True)
+
+    btn.click(
+        fn=prepare_visualization_data,
+        inputs=[json_upload, video_upload],
+        outputs=[output_ov]
+    )
+    
+    gr.Examples(
+        examples=[
+            [str(mediapipe_json_path), None],
+            [str(movenet_json_path), None],
+            [str(yolo8_json_path), None],
+            [str(sample_json_path), str(sample_video_path)],
+            [str(yolo11_json_path), None]
+        ],
+        inputs=[json_upload, video_upload],
+        outputs=output_ov,
+        fn=prepare_visualization_data,
+        cache_examples=True
+    )
+
+if __name__ == "__main__":
+    demo.launch(allowed_paths=["/Users/csabi/Develop/overlay_video/files"])
+```
+
+## `OverlayVideo`
+
+### Initialization
+
+<table>
+<thead>
+<tr>
+<th align="left">name</th>
+<th align="left" style="width: 25%;">type</th>
+<th align="left">default</th>
+<th align="left">description</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="left"><code>value</code></td>
+<td align="left" style="width: 25%;">
+
+```python
+typing.Any
+```
+
+</td>
+<td align="left"><code>None</code></td>
+<td align="left">None</td>
+</tr>
+
+<tr>
+<td align="left"><code>label</code></td>
+<td align="left" style="width: 25%;">
+
+```python
+str | None
+```
+
+</td>
+<td align="left"><code>None</code></td>
+<td align="left">None</td>
+</tr>
+
+<tr>
+<td align="left"><code>interactive</code></td>
+<td align="left" style="width: 25%;">
+
+```python
+bool | None
+```
+
+</td>
+<td align="left"><code>None</code></td>
+<td align="left">None</td>
+</tr>
+
+<tr>
+<td align="left"><code>autoplay</code></td>
+<td align="left" style="width: 25%;">
+
+```python
+bool
+```
+
+</td>
+<td align="left"><code>False</code></td>
+<td align="left">None</td>
+</tr>
+
+<tr>
+<td align="left"><code>loop</code></td>
+<td align="left" style="width: 25%;">
+
+```python
+bool
+```
+
+</td>
+<td align="left"><code>False</code></td>
+<td align="left">None</td>
+</tr>
+
+<tr>
+<td align="left"><code>mode</code></td>
+<td align="left" style="width: 25%;">
+
+```python
+str
+```
+
+</td>
+<td align="left"><code>"overlay"</code></td>
+<td align="left">None</td>
+</tr>
+</tbody></table>
+
+
+### Events
+
+| name | description |
+|:-----|:------------|
+| `change` | Triggered when the value of the OverlayVideo changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input. |
+| `clear` | This listener is triggered when the user clears the OverlayVideo using the clear button for the component. |
+| `play` | This listener is triggered when the user plays the media in the OverlayVideo. |
+| `pause` | This listener is triggered when the media in the OverlayVideo stops for any reason. |
+| `end` | This listener is triggered when the user reaches the end of the media playing in the OverlayVideo. |
+
+
+
+### User function
+
+The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).
+
+- When used as an Input, the component only impacts the input signature of the user function.
+- When used as an output, the component only impacts the return signature of the user function.
+
+The code snippet below is accurate in cases where the component is used as both an input and an output.
+
+
+
+ ```python
+ def predict(
+     value: str | None
+ ) -> typing.Optional[typing.Tuple[str | None, str | None]][
+    typing.Tuple[str | None, str | None][
+        str | None, str | None
+    ],
+    None,
+]:
+     return value
+ ```
+ 

app.py

@@ -0,0 +1,81 @@
+
+import gradio as gr
+from gradio_overlay_video import OverlayVideo
+from pathlib import Path
+
+DEMO_DIR = Path(__file__).parent.parent
+sample_video_path = DEMO_DIR / "files/balette.mp4"
+sample_json_path = DEMO_DIR / "files/mediapipe_full_kp_balette.json"
+mediapipe_json_path = DEMO_DIR / "files/mediapipe_heavy_kp_parkour.json"
+movenet_json_path = DEMO_DIR / "files/movenet_thunder_kp_skate.json"
+yolo8_json_path = DEMO_DIR / "files/yolov8_kp_dance.json"
+yolo11_json_path = DEMO_DIR / "files/yolov11.json"
+
+def prepare_visualization_data(json_path, video_path):
+    """
+    This function simply validates the inputs and passes them to the
+    custom OverlayVideo component for frontend processing.
+    """
+    if not json_path:
+        raise gr.Error("A JSON file is required to generate a visualization.")
+
+    print(f"✅ Preparing visualization with JSON: {json_path}")
+    if video_path:
+        print(f"✅ Video background provided: {video_path}")
+    else:
+        print("ℹ️ No video background provided. Visualization will be on a black background.")
+
+    # The backend's job is just to pass the filepaths to the frontend.
+    # The return format (video_path, json_path) must match what postprocess expects.
+    return (video_path, json_path)
+
+
+with gr.Blocks(theme=gr.themes.Default(primary_hue="rose", secondary_hue="pink")) as demo:
+    gr.Markdown(
+        "# 🩰 Interactive Pose Visualization\n"
+        "1. **Upload a JSON file** with pose data.\n"
+        "2. **(Optional) Upload a video** to use as the background.\n"
+        "3. Click 'Display Visualization' to see the interactive result."
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Use standard gr.File for robust input handling
+            json_upload = gr.File(
+                label="Upload Required JSON File",
+                file_types=[".json"],
+                type="filepath"
+            )
+            video_upload = gr.File(
+                label="Upload Optional Video File",
+                file_types=["video"],
+                type="filepath",
+                value=None 
+            )
+            btn = gr.Button("Display Visualization", variant="primary")
+        
+        with gr.Column(scale=1):
+            output_ov = OverlayVideo(label="Output", interactive=False, autoplay=True)
+
+    btn.click(
+        fn=prepare_visualization_data,
+        inputs=[json_upload, video_upload],
+        outputs=[output_ov]
+    )
+    
+    gr.Examples(
+        examples=[
+            [str(mediapipe_json_path), None],
+            [str(movenet_json_path), None],
+            [str(yolo8_json_path), None],
+            [str(sample_json_path), str(sample_video_path)],
+            [str(yolo11_json_path), None]
+        ],
+        inputs=[json_upload, video_upload],
+        outputs=output_ov,
+        fn=prepare_visualization_data,
+        cache_examples=True
+    )
+
+if __name__ == "__main__":
+    demo.launch(allowed_paths=["/Users/csabi/Develop/overlay_video/files"])

css.css

@@ -0,0 +1,157 @@
+html {
+	font-family: Inter;
+	font-size: 16px;
+	font-weight: 400;
+	line-height: 1.5;
+	-webkit-text-size-adjust: 100%;
+	background: #fff;
+	color: #323232;
+	-webkit-font-smoothing: antialiased;
+	-moz-osx-font-smoothing: grayscale;
+	text-rendering: optimizeLegibility;
+}
+
+:root {
+	--space: 1;
+	--vspace: calc(var(--space) * 1rem);
+	--vspace-0: calc(3 * var(--space) * 1rem);
+	--vspace-1: calc(2 * var(--space) * 1rem);
+	--vspace-2: calc(1.5 * var(--space) * 1rem);
+	--vspace-3: calc(0.5 * var(--space) * 1rem);
+}
+
+.app {
+	max-width: 748px !important;
+}
+
+.prose p {
+	margin: var(--vspace) 0;
+	line-height: var(--vspace * 2);
+	font-size: 1rem;
+}
+
+code {
+	font-family: "Inconsolata", sans-serif;
+	font-size: 16px;
+}
+
+h1,
+h1 code {
+	font-weight: 400;
+	line-height: calc(2.5 / var(--space) * var(--vspace));
+}
+
+h1 code {
+	background: none;
+	border: none;
+	letter-spacing: 0.05em;
+	padding-bottom: 5px;
+	position: relative;
+	padding: 0;
+}
+
+h2 {
+	margin: var(--vspace-1) 0 var(--vspace-2) 0;
+	line-height: 1em;
+}
+
+h3,
+h3 code {
+	margin: var(--vspace-1) 0 var(--vspace-2) 0;
+	line-height: 1em;
+}
+
+h4,
+h5,
+h6 {
+	margin: var(--vspace-3) 0 var(--vspace-3) 0;
+	line-height: var(--vspace);
+}
+
+.bigtitle,
+h1,
+h1 code {
+	font-size: calc(8px * 4.5);
+	word-break: break-word;
+}
+
+.title,
+h2,
+h2 code {
+	font-size: calc(8px * 3.375);
+	font-weight: lighter;
+	word-break: break-word;
+	border: none;
+	background: none;
+}
+
+.subheading1,
+h3,
+h3 code {
+	font-size: calc(8px * 1.8);
+	font-weight: 600;
+	border: none;
+	background: none;
+	letter-spacing: 0.1em;
+	text-transform: uppercase;
+}
+
+h2 code {
+	padding: 0;
+	position: relative;
+	letter-spacing: 0.05em;
+}
+
+blockquote {
+	font-size: calc(8px * 1.1667);
+	font-style: italic;
+	line-height: calc(1.1667 * var(--vspace));
+	margin: var(--vspace-2) var(--vspace-2);
+}
+
+.subheading2,
+h4 {
+	font-size: calc(8px * 1.4292);
+	text-transform: uppercase;
+	font-weight: 600;
+}
+
+.subheading3,
+h5 {
+	font-size: calc(8px * 1.2917);
+	line-height: calc(1.2917 * var(--vspace));
+
+	font-weight: lighter;
+	text-transform: uppercase;
+	letter-spacing: 0.15em;
+}
+
+h6 {
+	font-size: calc(8px * 1.1667);
+	font-size: 1.1667em;
+	font-weight: normal;
+	font-style: italic;
+	font-family: "le-monde-livre-classic-byol", serif !important;
+	letter-spacing: 0px !important;
+}
+
+#start .md > *:first-child {
+	margin-top: 0;
+}
+
+h2 + h3 {
+	margin-top: 0;
+}
+
+.md hr {
+	border: none;
+	border-top: 1px solid var(--block-border-color);
+	margin: var(--vspace-2) 0 var(--vspace-2) 0;
+}
+.prose ul {
+	margin: var(--vspace-2) 0 var(--vspace-1) 0;
+}
+
+.gap {
+	gap: 0;
+}

requirements.txt

@@ -0,0 +1 @@
+gradio_overlay_video

space.py

@@ -0,0 +1,206 @@
+
+import gradio as gr
+from app import demo as app
+import os
+
+_docs = {'OverlayVideo': {'description': 'An output component that plays a video with an interactive, toggleable overlay of pose data.', 'members': {'__init__': {'value': {'type': 'typing.Any', 'default': 'None', 'description': None}, 'label': {'type': 'str | None', 'default': 'None', 'description': None}, 'interactive': {'type': 'bool | None', 'default': 'None', 'description': None}, 'autoplay': {'type': 'bool', 'default': 'False', 'description': None}, 'loop': {'type': 'bool', 'default': 'False', 'description': None}, 'mode': {'type': 'str', 'default': '"overlay"', 'description': None}}, 'postprocess': {'value': {'type': 'typing.Optional[typing.Tuple[str | None, str | None]][\n    typing.Tuple[str | None, str | None][\n        str | None, str | None\n    ],\n    None,\n]', 'description': None}}, 'preprocess': {'return': {'type': 'str | None', 'description': None}, 'value': None}}, 'events': {'change': {'type': None, 'default': None, 'description': 'Triggered when the value of the OverlayVideo changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.'}, 'clear': {'type': None, 'default': None, 'description': 'This listener is triggered when the user clears the OverlayVideo using the clear button for the component.'}, 'play': {'type': None, 'default': None, 'description': 'This listener is triggered when the user plays the media in the OverlayVideo.'}, 'pause': {'type': None, 'default': None, 'description': 'This listener is triggered when the media in the OverlayVideo stops for any reason.'}, 'end': {'type': None, 'default': None, 'description': 'This listener is triggered when the user reaches the end of the media playing in the OverlayVideo.'}}}, '__meta__': {'additional_interfaces': {}, 'user_fn_refs': {'OverlayVideo': []}}}
+
+abs_path = os.path.join(os.path.dirname(__file__), "css.css")
+
+with gr.Blocks(
+    css=abs_path,
+    theme=gr.themes.Default(
+        font_mono=[
+            gr.themes.GoogleFont("Inconsolata"),
+            "monospace",
+        ],
+    ),
+) as demo:
+    gr.Markdown(
+"""
+# `gradio_overlay_video`
+
+<div style="display: flex; gap: 7px;">
+<img alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.0.8%20-%20orange">  
+</div>
+
+overlayed video controller
+""", elem_classes=["md-custom"], header_links=True)
+    app.render()
+    gr.Markdown(
+"""
+## Installation
+
+```bash
+pip install gradio_overlay_video
+```
+
+## Usage
+
+```python
+
+import gradio as gr
+from gradio_overlay_video import OverlayVideo
+from pathlib import Path
+
+DEMO_DIR = Path(__file__).parent.parent
+sample_video_path = DEMO_DIR / "files/balette.mp4"
+sample_json_path = DEMO_DIR / "files/mediapipe_full_kp_balette.json"
+mediapipe_json_path = DEMO_DIR / "files/mediapipe_heavy_kp_parkour.json"
+movenet_json_path = DEMO_DIR / "files/movenet_thunder_kp_skate.json"
+yolo8_json_path = DEMO_DIR / "files/yolov8_kp_dance.json"
+yolo11_json_path = DEMO_DIR / "files/yolov11.json"
+
+def prepare_visualization_data(json_path, video_path):
+    \"\"\"
+    This function simply validates the inputs and passes them to the
+    custom OverlayVideo component for frontend processing.
+    \"\"\"
+    if not json_path:
+        raise gr.Error("A JSON file is required to generate a visualization.")
+
+    print(f"✅ Preparing visualization with JSON: {json_path}")
+    if video_path:
+        print(f"✅ Video background provided: {video_path}")
+    else:
+        print("ℹ️ No video background provided. Visualization will be on a black background.")
+
+    # The backend's job is just to pass the filepaths to the frontend.
+    # The return format (video_path, json_path) must match what postprocess expects.
+    return (video_path, json_path)
+
+
+with gr.Blocks(theme=gr.themes.Default(primary_hue="rose", secondary_hue="pink")) as demo:
+    gr.Markdown(
+        "# 🩰 Interactive Pose Visualization\n"
+        "1. **Upload a JSON file** with pose data.\n"
+        "2. **(Optional) Upload a video** to use as the background.\n"
+        "3. Click 'Display Visualization' to see the interactive result."
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Use standard gr.File for robust input handling
+            json_upload = gr.File(
+                label="Upload Required JSON File",
+                file_types=[".json"],
+                type="filepath"
+            )
+            video_upload = gr.File(
+                label="Upload Optional Video File",
+                file_types=["video"],
+                type="filepath",
+                value=None 
+            )
+            btn = gr.Button("Display Visualization", variant="primary")
+        
+        with gr.Column(scale=1):
+            output_ov = OverlayVideo(label="Output", interactive=False, autoplay=True)
+
+    btn.click(
+        fn=prepare_visualization_data,
+        inputs=[json_upload, video_upload],
+        outputs=[output_ov]
+    )
+    
+    gr.Examples(
+        examples=[
+            [str(mediapipe_json_path), None],
+            [str(movenet_json_path), None],
+            [str(yolo8_json_path), None],
+            [str(sample_json_path), str(sample_video_path)],
+            [str(yolo11_json_path), None]
+        ],
+        inputs=[json_upload, video_upload],
+        outputs=output_ov,
+        fn=prepare_visualization_data,
+        cache_examples=True
+    )
+
+if __name__ == "__main__":
+    demo.launch(allowed_paths=["/Users/csabi/Develop/overlay_video/files"])
+```
+""", elem_classes=["md-custom"], header_links=True)
+
+
+    gr.Markdown("""
+## `OverlayVideo`
+
+### Initialization
+""", elem_classes=["md-custom"], header_links=True)
+
+    gr.ParamViewer(value=_docs["OverlayVideo"]["members"]["__init__"], linkify=[])
+
+
+    gr.Markdown("### Events")
+    gr.ParamViewer(value=_docs["OverlayVideo"]["events"], linkify=['Event'])
+
+
+
+
+    gr.Markdown("""
+
+### User function
+
+The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).
+
+- When used as an Input, the component only impacts the input signature of the user function.
+- When used as an output, the component only impacts the return signature of the user function.
+
+The code snippet below is accurate in cases where the component is used as both an input and an output.
+
+
+
+ ```python
+def predict(
+    value: str | None
+) -> typing.Optional[typing.Tuple[str | None, str | None]][
+    typing.Tuple[str | None, str | None][
+        str | None, str | None
+    ],
+    None,
+]:
+    return value
+```
+""", elem_classes=["md-custom", "OverlayVideo-user-fn"], header_links=True)
+
+
+
+
+    demo.load(None, js=r"""function() {
+    const refs = {};
+    const user_fn_refs = {
+          OverlayVideo: [], };
+    requestAnimationFrame(() => {
+
+        Object.entries(user_fn_refs).forEach(([key, refs]) => {
+            if (refs.length > 0) {
+                const el = document.querySelector(`.${key}-user-fn`);
+                if (!el) return;
+                refs.forEach(ref => {
+                    el.innerHTML = el.innerHTML.replace(
+                        new RegExp("\\b"+ref+"\\b", "g"),
+                        `<a href="#h-${ref.toLowerCase()}">${ref}</a>`
+                    );
+                })
+            }
+        })
+
+        Object.entries(refs).forEach(([key, refs]) => {
+            if (refs.length > 0) {
+                const el = document.querySelector(`.${key}`);
+                if (!el) return;
+                refs.forEach(ref => {
+                    el.innerHTML = el.innerHTML.replace(
+                        new RegExp("\\b"+ref+"\\b", "g"),
+                        `<a href="#h-${ref.toLowerCase()}">${ref}</a>`
+                    );
+                })
+            }
+        })
+    })
+}
+
+""")
+
+demo.launch()

src/.claude/settings.local.json

@@ -0,0 +1,12 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(python:*)",
+      "Bash(pip install:*)",
+      "Bash(grep:*)",
+      "Bash(sed:*)",
+      "Bash(rm:*)"
+    ],
+    "deny": []
+  }
+}

src/.gitignore

@@ -0,0 +1,12 @@
+.eggs/
+dist/
+*.pyc
+__pycache__/
+*.py[cod]
+*$py.class
+__tmp/*
+*.pyi
+.mypycache
+.ruff_cache
+node_modules
+backend/**/templates/

src/.gradio/cached_examples/9/Output/3e1643b3cd3925885fe5/balette.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:75e5ff7c8e5eb31b54686b0250a1ebc739ea1128bb18f116f8f0215159e0979b
+size 621415

src/.gradio/cached_examples/9/Output/6444ff5b7cd73ce6e3ac/mediapipe_full_balette.json

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+{
+    "analysis_metadata": {
+      "timestamp": "2025-06-08T15:21:19.211586",
+      "version": "1.0.0",
+      "model_info": {
+        "name": "mediapipe-full",
+        "type": "pose_estimation"
+      }
+    },
+    "video_info": {
+      "fps": 60,
+      "duration_seconds": 3.1166666666666667,
+      "width": 1280,
+      "height": 688,
+      "frame_count": 187
+    },
+    "movement_analysis": {
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src/.gradio/flagged/x/97ecfa403560d9ee017f/world.mp4

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src/CLAUDE.md

@@ -0,0 +1,147 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+This is a Gradio custom component called `gradio_overlay_video` that enables interactive pose visualization overlays on video content. The component processes JSON pose data from multiple keypoint formats (MediaPipe, COCO, COCO-WholeBody, Sociopticon, YOLOv11) and renders it as interactive overlays on top of video files.
+
+## Architecture
+
+### Backend (`backend/gradio_overlay_video/`)
+- **overlay_video.py**: Main component class `OverlayVideo` that extends Gradio's Component base class
+  - Uses modular `VisualizationProcessor` to preprocess JSON data into streamlined visualization instructions
+  - Supports events: change, clear, play, pause, end
+- **visualization_processors.py**: Modular preprocessing system with specialized processors:
+  - `JointsProcessor`: Extracts joint/keypoint positions for circle visualization
+  - `BonesProcessor`: Processes skeleton connections for line visualization with auto-format detection
+  - `DirectionArrowProcessor`: Creates direction arrow data from movement metrics with format-aware center calculation
+  - `MotionTrailProcessor`: Generates faded motion trails from pose history
+  - `LabanProcessor`: Extracts Laban Movement Analysis metrics for text overlay
+  - `VisualizationProcessor`: Main coordinator that combines all processors
+- **keypoint_formats.py**: Keypoint format definitions and skeleton configurations:
+  - `KeypointFormat`: Enum for supported formats (MediaPipe, COCO, COCO-WholeBody, Sociopticon, YOLOv11)
+  - `SkeletonDefinitions`: Skeleton connection definitions for each format
+  - `KeypointFormatDetector`: Auto-detection logic based on keypoint count and structure
+  - `YOLOv11DataConverter`: Converts YOLOv11 pixel coordinates to normalized format
+
+### Frontend (`frontend/`)
+- **Svelte-based** custom Gradio component using TypeScript
+- **shared/InteractiveOverlay.svelte**: Handles file uploads for video and JSON
+- **shared/OverlayPlayer.svelte**: Core video player with modular overlay rendering
+  - Uses streamlined visualization instructions from backend
+  - Supports independent toggle controls for: joints, bones, direction arrows, motion trails, Laban metrics
+  - FPS-aware playback using video metadata
+  - Dynamic control visibility based on data capabilities
+- **shared/Video.svelte**: Base video component wrapper
+- Uses @gradio/* dependencies and FFmpeg for video processing
+
+### Demo Application (`demo/`)
+- **app.py**: Example Gradio app showing component usage
+- **space.py**: Hugging Face Space deployment version
+- Uses sample files from `files/` directory (balette.mp4 and mediapipe JSON data)
+
+## Common Development Commands
+
+### Python/Backend Development
+```bash
+# Install in development mode
+pip install -e .
+
+# Run demo application
+cd demo && python app.py
+
+# Build package
+python -m build
+```
+
+### Frontend Development
+```bash
+# Install frontend dependencies
+cd frontend && npm install
+
+# Build frontend components
+# (No specific build command found - likely handled by Gradio's build system)
+```
+
+## Data Format Expectations
+
+### Input JSON Structure
+The component expects JSON files with this structure:
+- `video_info`: Metadata about the video including `fps`, `width`, `height`, `duration_seconds`
+- `movement_analysis.frames[]`: Array of frame data containing:
+  - `timestamp`: Frame timing
+  - `keypoints[0].points[]`: Array of pose points with x/y coordinates and confidence
+  - `metrics`: Laban Movement Analysis metrics (direction, intensity, speed, velocity, etc.)
+
+### Processed Output Structure
+The backend preprocesses this into streamlined visualization instructions:
+- `video_info`: Original video metadata
+- `fps`: Extracted frame rate for proper playback timing
+- `keypoint_format`: Detected format type (mediapipe, coco, coco_wholebody, sociopticon, yolo11_pose)
+- `capabilities`: Flags indicating which visualization types are available
+- `frames[]`: Array of processed frame data containing:
+  - `timestamp`: Frame timing
+  - `joints`: Array of joint positions for circle rendering
+  - `bones`: Array of bone connections for line rendering
+  - `direction_arrow`: Arrow data for movement direction visualization
+  - `motion_trail`: Array of trail segments for motion path rendering
+  - `laban_metrics`: Cleaned metrics for text overlay display
+
+### Visualization Processors
+Each processor can be individually enabled/disabled:
+- **JointsProcessor**: Filters keypoints by confidence, extracts x/y positions
+- **BonesProcessor**: Auto-detects keypoint format and uses appropriate skeleton definitions:
+  - MediaPipe Pose (33 keypoints): Full face, body, and hand landmarks
+  - COCO Keypoints (17 keypoints): Basic body joints
+  - COCO-WholeBody (133 keypoints): Body + face + hands + feet
+  - Sociopticon (18-21 keypoints): Enhanced torso detail
+  - YOLOv11 Pose (17 keypoints): Uses COCO skeleton with pixel coordinate conversion
+- **DirectionArrowProcessor**: Calculates body center using format-specific core points
+- **MotionTrailProcessor**: Maintains trail history with configurable length and alpha values
+- **LabanProcessor**: Sanitizes and formats movement analysis metrics
+
+## Package Structure
+
+This is a Gradio custom component package with:
+- Python backend component in `/backend/gradio_overlay_video/`  
+- Svelte frontend in `/frontend/`
+- Demo applications in `/demo/`
+- Build configuration in `pyproject.toml`
+- Version: 0.0.7
+
+## Supported Keypoint Formats
+
+### MediaPipe Pose (33 keypoints)
+- Face landmarks (nose, eyes, ears, mouth)
+- Body pose (shoulders, elbows, wrists, hips, knees, ankles)
+- Hand landmarks (left and right hand keypoints)
+- Auto-detected when JSON contains 33 keypoints
+
+### COCO Keypoints (17 keypoints)
+- Standard COCO dataset format
+- Basic body joints: nose, eyes, ears, shoulders, elbows, wrists, hips, knees, ankles
+- Auto-detected when JSON contains 17 keypoints
+
+### COCO-WholeBody (133 keypoints)
+- Extended COCO format with additional detail
+- Body (17) + Face (68) + Left Hand (21) + Right Hand (21) + Feet (6)
+- Auto-detected when JSON contains 133 keypoints
+
+### Sociopticon (18-21 keypoints)
+- Custom format with enhanced torso detail
+- Similar to COCO but with additional mid-torso points
+- Auto-detected when JSON contains 18-21 keypoints
+
+### YOLOv11 Pose (17 keypoints)
+- YOLOv11 pose estimation format with pixel coordinates
+- Uses COCO keypoint structure but with different data organization
+- Auto-detected based on JSON structure (metadata.model contains "yolo" and "pose")
+- Automatically converts pixel coordinates to normalized values
+- Designed for single-frame pose showcase
+
+### Format Detection
+- Automatic detection based on keypoint count and structure
+- Special detection for YOLOv11 based on metadata and data organization
+- Fallback to MediaPipe format if detection is unclear
+- Format information included in processed output for frontend reference

src/README.md

@@ -0,0 +1,239 @@
+---
+tags: [gradio-custom-component, gradio-5, laban-movement-analysis, pose-visualisation, pose-estimation, movement-visualisation, visualisation, video]
+title: gradio_overlay_video
+emoji: 🏃
+short_description: pose data overlayed video controller
+colorFrom: purple
+colorTo: green
+sdk: gradio
+sdk_version: 5.33.0
+pinned: false
+app_file: space.py
+---
+
+# `gradio_overlay_video`
+<img alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.0.8%20-%20orange">  
+
+overlayed video controller
+
+## Installation
+
+```bash
+pip install gradio_overlay_video
+```
+
+## Usage
+
+```python
+
+import gradio as gr
+from gradio_overlay_video import OverlayVideo
+from pathlib import Path
+
+DEMO_DIR = Path(__file__).parent.parent
+sample_video_path = DEMO_DIR / "files/balette.mp4"
+sample_json_path = DEMO_DIR / "files/mediapipe_full_kp_balette.json"
+mediapipe_json_path = DEMO_DIR / "files/mediapipe_heavy_kp_parkour.json"
+movenet_json_path = DEMO_DIR / "files/movenet_thunder_kp_skate.json"
+yolo8_json_path = DEMO_DIR / "files/yolov8_kp_dance.json"
+yolo11_json_path = DEMO_DIR / "files/yolov11.json"
+
+def prepare_visualization_data(json_path, video_path):
+    """
+    This function simply validates the inputs and passes them to the
+    custom OverlayVideo component for frontend processing.
+    """
+    if not json_path:
+        raise gr.Error("A JSON file is required to generate a visualization.")
+
+    print(f"✅ Preparing visualization with JSON: {json_path}")
+    if video_path:
+        print(f"✅ Video background provided: {video_path}")
+    else:
+        print("ℹ️ No video background provided. Visualization will be on a black background.")
+
+    # The backend's job is just to pass the filepaths to the frontend.
+    # The return format (video_path, json_path) must match what postprocess expects.
+    return (video_path, json_path)
+
+
+with gr.Blocks(theme=gr.themes.Default(primary_hue="rose", secondary_hue="pink")) as demo:
+    gr.Markdown(
+        "# 🩰 Interactive Pose Visualization\n"
+        "1. **Upload a JSON file** with pose data.\n"
+        "2. **(Optional) Upload a video** to use as the background.\n"
+        "3. Click 'Display Visualization' to see the interactive result."
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Use standard gr.File for robust input handling
+            json_upload = gr.File(
+                label="Upload Required JSON File",
+                file_types=[".json"],
+                type="filepath"
+            )
+            video_upload = gr.File(
+                label="Upload Optional Video File",
+                file_types=["video"],
+                type="filepath",
+                value=None 
+            )
+            btn = gr.Button("Display Visualization", variant="primary")
+        
+        with gr.Column(scale=1):
+            output_ov = OverlayVideo(label="Output", interactive=False, autoplay=True)
+
+    btn.click(
+        fn=prepare_visualization_data,
+        inputs=[json_upload, video_upload],
+        outputs=[output_ov]
+    )
+    
+    gr.Examples(
+        examples=[
+            [str(mediapipe_json_path), None],
+            [str(movenet_json_path), None],
+            [str(yolo8_json_path), None],
+            [str(sample_json_path), str(sample_video_path)],
+            [str(yolo11_json_path), None]
+        ],
+        inputs=[json_upload, video_upload],
+        outputs=output_ov,
+        fn=prepare_visualization_data,
+        cache_examples=True
+    )
+
+if __name__ == "__main__":
+    demo.launch(allowed_paths=["/Users/csabi/Develop/overlay_video/files"])
+```
+
+## `OverlayVideo`
+
+### Initialization
+
+<table>
+<thead>
+<tr>
+<th align="left">name</th>
+<th align="left" style="width: 25%;">type</th>
+<th align="left">default</th>
+<th align="left">description</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="left"><code>value</code></td>
+<td align="left" style="width: 25%;">
+
+```python
+typing.Any
+```
+
+</td>
+<td align="left"><code>None</code></td>
+<td align="left">None</td>
+</tr>
+
+<tr>
+<td align="left"><code>label</code></td>
+<td align="left" style="width: 25%;">
+
+```python
+str | None
+```
+
+</td>
+<td align="left"><code>None</code></td>
+<td align="left">None</td>
+</tr>
+
+<tr>
+<td align="left"><code>interactive</code></td>
+<td align="left" style="width: 25%;">
+
+```python
+bool | None
+```
+
+</td>
+<td align="left"><code>None</code></td>
+<td align="left">None</td>
+</tr>
+
+<tr>
+<td align="left"><code>autoplay</code></td>
+<td align="left" style="width: 25%;">
+
+```python
+bool
+```
+
+</td>
+<td align="left"><code>False</code></td>
+<td align="left">None</td>
+</tr>
+
+<tr>
+<td align="left"><code>loop</code></td>
+<td align="left" style="width: 25%;">
+
+```python
+bool
+```
+
+</td>
+<td align="left"><code>False</code></td>
+<td align="left">None</td>
+</tr>
+
+<tr>
+<td align="left"><code>mode</code></td>
+<td align="left" style="width: 25%;">
+
+```python
+str
+```
+
+</td>
+<td align="left"><code>"overlay"</code></td>
+<td align="left">None</td>
+</tr>
+</tbody></table>
+
+
+### Events
+
+| name | description |
+|:-----|:------------|
+| `change` | Triggered when the value of the OverlayVideo changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input. |
+| `clear` | This listener is triggered when the user clears the OverlayVideo using the clear button for the component. |
+| `play` | This listener is triggered when the user plays the media in the OverlayVideo. |
+| `pause` | This listener is triggered when the media in the OverlayVideo stops for any reason. |
+| `end` | This listener is triggered when the user reaches the end of the media playing in the OverlayVideo. |
+
+
+
+### User function
+
+The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).
+
+- When used as an Input, the component only impacts the input signature of the user function.
+- When used as an output, the component only impacts the return signature of the user function.
+
+The code snippet below is accurate in cases where the component is used as both an input and an output.
+
+
+
+ ```python
+ def predict(
+     value: str | None
+ ) -> typing.Optional[typing.Tuple[str | None, str | None]][
+    typing.Tuple[str | None, str | None][
+        str | None, str | None
+    ],
+    None,
+]:
+     return value
+ ```
+ 

src/backend/gradio_overlay_video/__init__.py

@@ -0,0 +1,4 @@
+
+from .overlay_video import OverlayVideo
+
+__all__ = ['OverlayVideo']

src/backend/gradio_overlay_video/keypoint_formats.py

@@ -0,0 +1,356 @@
+"""
+Keypoint format definitions and skeleton configurations for different pose estimation models.
+Supports: MediaPipe, COCO, COCO-WholeBody, and Sociopticon formats.
+"""
+
+from typing import Dict, List, Tuple, Optional
+from enum import Enum
+
+
+class KeypointFormat(Enum):
+    """Supported keypoint formats."""
+    MEDIAPIPE = "mediapipe"
+    COCO = "coco"
+    COCO_WHOLEBODY = "coco_wholebody"
+    SOCIOPTICON = "sociopticon"
+    YOLO11_POSE = "yolo11_pose"
+
+
+class SkeletonDefinitions:
+    """Skeleton connection definitions for different keypoint formats."""
+    
+    # MediaPipe pose skeleton connections (33 keypoints)
+    MEDIAPIPE_SKELETON = [
+        # Face connections
+        (0, 1), (1, 2), (2, 3), (3, 7),  # left eye region
+        (0, 4), (4, 5), (5, 6), (6, 8),  # right eye region
+        (9, 10),  # mouth
+        # Upper body
+        (11, 12),  # shoulders
+        (11, 13), (13, 15),  # left arm
+        (12, 14), (14, 16),  # right arm
+        (11, 23), (12, 24),  # shoulders to hips
+        (23, 24),  # hips
+        # Lower body
+        (23, 25), (25, 27), (27, 29), (27, 31),  # left leg
+        (24, 26), (26, 28), (28, 30), (28, 32),  # right leg
+        # Hands
+        (15, 17), (15, 19), (15, 21),  # left hand
+        (16, 18), (16, 20), (16, 22),  # right hand
+    ]
+    
+    # COCO keypoint skeleton connections (17 keypoints)
+    # Order: nose, left_eye, right_eye, left_ear, right_ear, left_shoulder, right_shoulder,
+    #        left_elbow, right_elbow, left_wrist, right_wrist, left_hip, right_hip,
+    #        left_knee, right_knee, left_ankle, right_ankle
+    COCO_SKELETON = [
+        # Head connections
+        (0, 1), (0, 2),  # nose to eyes
+        (1, 3), (2, 4),  # eyes to ears
+        # Upper body
+        (5, 6),  # shoulders
+        (5, 7), (7, 9),  # left arm
+        (6, 8), (8, 10),  # right arm
+        (5, 11), (6, 12),  # shoulders to hips
+        (11, 12),  # hips
+        # Lower body
+        (11, 13), (13, 15),  # left leg
+        (12, 14), (14, 16),  # right leg
+    ]
+    
+    # COCO-WholeBody skeleton connections (133 keypoints total)
+    # Body (17) + Face (68) + Left Hand (21) + Right Hand (21) + Feet (6)
+    COCO_WHOLEBODY_SKELETON = [
+        # Body connections (same as COCO)
+        (0, 1), (0, 2), (1, 3), (2, 4),  # head
+        (5, 6),  # shoulders
+        (5, 7), (7, 9),  # left arm
+        (6, 8), (8, 10),  # right arm
+        (5, 11), (6, 12),  # shoulders to hips
+        (11, 12),  # hips
+        (11, 13), (13, 15),  # left leg
+        (12, 14), (14, 16),  # right leg
+        
+        # Face outline connections (simplified from 68 points for performance)
+        (17, 18), (18, 19), (19, 20), (20, 21), (21, 22), (22, 23), (23, 24), (24, 25), (25, 26),  # jaw
+        (27, 28), (28, 29), (29, 30),  # nose bridge
+        (31, 32), (32, 33), (33, 34), (34, 35),  # nose
+        (36, 37), (37, 38), (38, 39), (39, 40), (40, 41), (41, 36),  # left eye
+        (42, 43), (43, 44), (44, 45), (45, 46), (46, 47), (47, 42),  # right eye
+        (48, 49), (49, 50), (50, 51), (51, 52), (52, 53), (53, 54), (54, 55), (55, 56), (56, 57), (57, 58), (58, 59), (59, 48),  # outer lip
+        (60, 61), (61, 62), (62, 63), (63, 64), (64, 65), (65, 66), (66, 67), (67, 60),  # inner lip
+        
+        # Left hand connections (21 points starting at index 91)
+        (91, 92), (92, 93), (93, 94), (94, 95),  # thumb
+        (91, 96), (96, 97), (97, 98), (98, 99),  # index finger
+        (91, 100), (100, 101), (101, 102), (102, 103),  # middle finger
+        (91, 104), (104, 105), (105, 106), (106, 107),  # ring finger
+        (91, 108), (108, 109), (109, 110), (110, 111),  # pinky
+        
+        # Right hand connections (21 points starting at index 112)
+        (112, 113), (113, 114), (114, 115), (115, 116),  # thumb
+        (112, 117), (117, 118), (118, 119), (119, 120),  # index finger
+        (112, 121), (121, 122), (122, 123), (123, 124),  # middle finger
+        (112, 125), (125, 126), (126, 127), (127, 128),  # ring finger
+        (112, 129), (129, 130), (130, 131), (131, 132),  # pinky
+    ]
+    
+    # Sociopticon skeleton connections (assuming extended COCO-like format)
+    SOCIOPTICON_SKELETON = [
+        # Head connections
+        (0, 1), (0, 2), (1, 3), (2, 4),  # head structure
+        # Upper body with more detail
+        (5, 6),  # shoulders
+        (5, 7), (7, 9),  # left arm
+        (6, 8), (8, 10),  # right arm
+        (5, 11), (6, 12),  # shoulders to hips
+        (11, 12),  # hips
+        # Additional torso points (if available)
+        (5, 17), (6, 18),  # shoulder to mid-torso
+        (17, 18), (17, 11), (18, 12),  # torso connections
+        # Lower body
+        (11, 13), (13, 15),  # left leg
+        (12, 14), (14, 16),  # right leg
+    ]
+    
+    @classmethod
+    def get_skeleton(cls, format_type: KeypointFormat) -> List[Tuple[int, int]]:
+        """Get skeleton connections for the specified format."""
+        if format_type == KeypointFormat.MEDIAPIPE:
+            return cls.MEDIAPIPE_SKELETON
+        elif format_type == KeypointFormat.COCO:
+            return cls.COCO_SKELETON
+        elif format_type == KeypointFormat.COCO_WHOLEBODY:
+            return cls.COCO_WHOLEBODY_SKELETON
+        elif format_type == KeypointFormat.SOCIOPTICON:
+            return cls.SOCIOPTICON_SKELETON
+        elif format_type == KeypointFormat.YOLO11_POSE:
+            return cls.COCO_SKELETON  # YOLOv11 uses COCO format
+        else:
+            return cls.MEDIAPIPE_SKELETON  # default
+
+
+class KeypointFormatDetector:
+    """Detects keypoint format based on data structure and keypoint count."""
+    
+    # Core body point indices for different formats (used for center calculation)
+    CORE_POINT_INDICES = {
+        KeypointFormat.MEDIAPIPE: [11, 12, 23, 24],     # shoulders and hips
+        KeypointFormat.COCO: [5, 6, 11, 12],            # shoulders and hips
+        KeypointFormat.COCO_WHOLEBODY: [5, 6, 11, 12],  # shoulders and hips (body part)
+        KeypointFormat.SOCIOPTICON: [5, 6, 11, 12],     # shoulders and hips
+        KeypointFormat.YOLO11_POSE: [5, 6, 11, 12]      # shoulders and hips (COCO format)
+    }
+    
+    @classmethod
+    def detect_format(cls, points: List[Dict]) -> KeypointFormat:
+        """
+        Detect keypoint format based on number of points and structure.
+        
+        Args:
+            points: List of keypoint dictionaries
+            
+        Returns:
+            Detected KeypointFormat
+        """
+        num_points = len(points)
+        
+        # Detect based on keypoint count
+        if num_points == 17:
+            return KeypointFormat.COCO
+        elif num_points == 133:
+            return KeypointFormat.COCO_WHOLEBODY
+        elif num_points == 33:
+            return KeypointFormat.MEDIAPIPE
+        elif num_points in [18, 19, 20, 21]:  # Sociopticon variations
+            return KeypointFormat.SOCIOPTICON
+        
+        # Additional heuristics based on keypoint names (if available)
+        if points and isinstance(points[0], dict):
+            first_point = points[0]
+            if 'name' in first_point:
+                name = first_point['name'].lower()
+                if 'nose' in name:
+                    # MediaPipe uses 'nose', COCO uses indices
+                    return KeypointFormat.MEDIAPIPE
+        
+        # Default fallback
+        return KeypointFormat.MEDIAPIPE
+    
+    @classmethod
+    def detect_yolo11_format(cls, data: Dict) -> bool:
+        """
+        Detect if data is in YOLOv11 format based on structure.
+        
+        Args:
+            data: Raw JSON data dictionary
+            
+        Returns:
+            True if YOLOv11 format detected
+        """
+        # Check for YOLOv11 specific structure
+        if 'metadata' in data and 'keypoints' in data:
+            metadata = data['metadata']
+            if isinstance(metadata, dict) and 'model' in metadata:
+                model_name = metadata['model'].lower()
+                if 'yolo' in model_name and 'pose' in model_name:
+                    return True
+            
+            # Check keypoints structure: should be list of lists of [x,y] coordinates
+            keypoints = data['keypoints']
+            if isinstance(keypoints, list) and len(keypoints) > 0:
+                first_frame = keypoints[0]
+                if isinstance(first_frame, list) and len(first_frame) == 17:
+                    # Check if points are [x,y] coordinate pairs
+                    first_point = first_frame[0]
+                    if isinstance(first_point, list) and len(first_point) == 2:
+                        return True
+        
+        return False
+    
+    @classmethod
+    def get_core_points(cls, format_type: KeypointFormat) -> List[int]:
+        """Get core body point indices for center calculation."""
+        return cls.CORE_POINT_INDICES.get(format_type, cls.CORE_POINT_INDICES[KeypointFormat.MEDIAPIPE])
+    
+    @classmethod
+    def format_info(cls, format_type: KeypointFormat) -> Dict:
+        """Get information about a keypoint format."""
+        info = {
+            KeypointFormat.MEDIAPIPE: {
+                "name": "MediaPipe Pose",
+                "keypoints": 33,
+                "description": "Google MediaPipe pose estimation with face, body, and hand landmarks"
+            },
+            KeypointFormat.COCO: {
+                "name": "COCO Keypoints",
+                "keypoints": 17,
+                "description": "COCO dataset keypoint format with basic body joints"
+            },
+            KeypointFormat.COCO_WHOLEBODY: {
+                "name": "COCO-WholeBody",
+                "keypoints": 133,
+                "description": "Extended COCO format with face, hands, and feet keypoints"
+            },
+            KeypointFormat.SOCIOPTICON: {
+                "name": "Sociopticon",
+                "keypoints": "18-21",
+                "description": "Sociopticon keypoint format with enhanced torso detail"
+            },
+            KeypointFormat.YOLO11_POSE: {
+                "name": "YOLOv11 Pose",
+                "keypoints": 17,
+                "description": "YOLOv11 pose estimation with COCO format keypoints and pixel coordinates"
+            }
+        }
+        
+        return info.get(format_type, info[KeypointFormat.MEDIAPIPE])
+
+
+class YOLOv11DataConverter:
+    """Converts YOLOv11 pose data to standard format."""
+    
+    @classmethod
+    def convert_to_standard_format(cls, yolo_data: Dict, video_width: int = 1920, video_height: int = 1080) -> Dict:
+        """
+        Convert YOLOv11 format to standard format expected by visualization processors.
+        
+        Args:
+            yolo_data: Raw YOLOv11 JSON data
+            video_width: Video width for coordinate normalization (default: 1920)
+            video_height: Video height for coordinate normalization (default: 1080)
+            
+        Returns:
+            Dictionary in standard format
+        """
+        # Extract first frame keypoints (YOLOv11 data contains list of frames)
+        if 'keypoints' not in yolo_data or not yolo_data['keypoints']:
+            raise ValueError("No keypoints found in YOLOv11 data")
+        
+        first_frame_keypoints = yolo_data['keypoints'][0]
+        
+        # Convert to standard point format
+        points = []
+        for i, point_coords in enumerate(first_frame_keypoints):
+            x, y = point_coords
+            
+            # Check if point is valid (YOLOv11 uses [0,0] for invalid points)
+            if x == 0 and y == 0:
+                # Invalid point - set low confidence
+                points.append({
+                    'x': 0.0,
+                    'y': 0.0,
+                    'confidence': 0.0,
+                    'index': i
+                })
+            else:
+                # Valid point - normalize coordinates and set high confidence
+                points.append({
+                    'x': x / video_width,  # Normalize to 0-1 range
+                    'y': y / video_height,  # Normalize to 0-1 range
+                    'confidence': 0.9,  # Default high confidence for valid points
+                    'index': i
+                })
+        
+        # Create standard format structure for single frame
+        converted_data = {
+            'video_info': {
+                'fps': 30,  # Default FPS for single frame
+                'width': video_width,
+                'height': video_height,
+                'duration_seconds': 0.033  # Single frame duration
+            },
+            'movement_analysis': {
+                'frames': [
+                    {
+                        'timestamp': 0.0,
+                        'keypoints': [
+                            {
+                                'points': points
+                            }
+                        ],
+                        'metrics': {
+                            'direction': 'stationary',
+                            'intensity': 0.0,
+                            'speed': 0.0,
+                            'velocity': {'x': 0, 'y': 0}
+                        }
+                    }
+                ]
+            }
+        }
+        
+        return converted_data
+    
+    @classmethod
+    def estimate_video_dimensions(cls, keypoints: List[List[float]]) -> Tuple[int, int]:
+        """
+        Estimate video dimensions based on keypoint coordinates.
+        
+        Args:
+            keypoints: List of [x, y] coordinate pairs
+            
+        Returns:
+            Tuple of (width, height) estimates
+        """
+        valid_points = [point for point in keypoints if point[0] > 0 or point[1] > 0]
+        
+        if not valid_points:
+            return 1920, 1080  # Default dimensions
+        
+        max_x = max(point[0] for point in valid_points)
+        max_y = max(point[1] for point in valid_points)
+        
+        # Add some margin and round to common video dimensions
+        estimated_width = int(max_x * 1.2)
+        estimated_height = int(max_y * 1.2)
+        
+        # Round to common video dimensions
+        common_widths = [1920, 1280, 854, 640]
+        common_heights = [1080, 720, 480, 360]
+        
+        # Find closest common dimensions
+        width = min(common_widths, key=lambda w: abs(w - estimated_width))
+        height = min(common_heights, key=lambda h: abs(h - estimated_height))
+        
+        return width, height

src/backend/gradio_overlay_video/overlay_video.py

@@ -0,0 +1,97 @@
+# backend/gradio_overlay_video/overlay_video.py
+
+from __future__ import annotations
+from pathlib import Path
+from typing import Any, Callable, Optional, Tuple
+import json
+
+from gradio_client import handle_file
+from gradio_client.documentation import document
+import gradio as gr
+from gradio.components.base import Component
+from gradio.data_classes import FileData, GradioModel
+from gradio.events import Events
+
+# This data model expects the raw, pre-processed JSON string
+class OverlayVideoData(GradioModel):
+    video: Optional[FileData] = None
+    json_data: Optional[str] = None
+
+
+@document()
+class OverlayVideo(Component):
+    """An output component that plays a video with an interactive, toggleable overlay of pose data."""
+    
+    data_model = OverlayVideoData
+    EVENTS = [Events.change, Events.clear, Events.play, Events.pause, Events.end]
+
+    def __init__(
+        self,
+        value: Any = None,
+        *,
+        label: str | None = None,
+        interactive: bool | None = None,
+        autoplay: bool = False,
+        loop: bool = False,
+        mode: str = "overlay", # Custom prop for the frontend
+        **kwargs
+    ):
+        self.autoplay = autoplay
+        self.loop = loop
+        self.mode = mode
+        super().__init__(label=label, interactive=interactive, value=value, **kwargs)
+
+    def preprocess(self, payload: OverlayVideoData | None) -> str | None:
+        """
+        This component is output-only, so preprocess does nothing.
+        It is required to satisfy the abstract class requirements.
+        """
+        # We return None because this component does not handle input.
+        return None
+
+    def postprocess(self, value: Tuple[str | None, str | None] | None) -> OverlayVideoData | None:
+        """
+        Takes video and JSON file paths, preprocesses the JSON with visualization processors,
+        and sends streamlined visualization instructions to the frontend.
+        """
+        if value is None or value[1] is None:
+            return None
+
+        video_path, json_path = value
+        
+        try:
+            with open(json_path, 'r', encoding='utf-8') as f:
+                full_data = json.load(f)
+
+            # Import and use the new visualization processor
+            from .visualization_processors import VisualizationProcessor
+            
+            # Initialize processor with all visualization types enabled
+            processor = VisualizationProcessor(
+                enable_joints=True,
+                enable_bones=True,
+                enable_direction_arrows=True,
+                enable_motion_trails=True,
+                enable_laban=True,
+                trail_length=10
+            )
+            
+            # Process the JSON data into streamlined visualization instructions
+            visualization_data = processor.process_json_data(full_data)
+            
+            # Convert to JSON string for frontend
+            json_content = json.dumps(visualization_data)
+
+        except Exception as e:
+            print(f"Error processing JSON file: {e}")
+            import traceback
+            print(traceback.format_exc())
+            return None
+
+        return OverlayVideoData(
+            video=handle_file(video_path) if video_path else None,
+            json_data=json_content
+        )
+
+    def example_payload(self) -> Any: return None
+    def example_value(self) -> Any: return None

src/backend/gradio_overlay_video/templates/component/assets/worker-BAOIWoxA.js

@@ -0,0 +1 @@
+(function(){"use strict";const i="https://unpkg.com/@ffmpeg/core@0.12.9/dist/umd/ffmpeg-core.js";var E;(function(t){t.LOAD="LOAD",t.EXEC="EXEC",t.FFPROBE="FFPROBE",t.WRITE_FILE="WRITE_FILE",t.READ_FILE="READ_FILE",t.DELETE_FILE="DELETE_FILE",t.RENAME="RENAME",t.CREATE_DIR="CREATE_DIR",t.LIST_DIR="LIST_DIR",t.DELETE_DIR="DELETE_DIR",t.ERROR="ERROR",t.DOWNLOAD="DOWNLOAD",t.PROGRESS="PROGRESS",t.LOG="LOG",t.MOUNT="MOUNT",t.UNMOUNT="UNMOUNT"})(E||(E={}));const f=new Error("unknown message type"),a=new Error("ffmpeg is not loaded, call `await ffmpeg.load()` first"),u=new Error("failed to import ffmpeg-core.js");let r;const O=async({coreURL:t,wasmURL:n,workerURL:e})=>{const o=!r;try{t||(t=i),importScripts(t)}catch{if((!t||t===i)&&(t=i.replace("/umd/","/esm/")),self.createFFmpegCore=(await import(t)).default,!self.createFFmpegCore)throw u}const s=t,c=n||t.replace(/.js$/g,".wasm"),p=e||t.replace(/.js$/g,".worker.js");return r=await self.createFFmpegCore({mainScriptUrlOrBlob:`${s}#${btoa(JSON.stringify({wasmURL:c,workerURL:p}))}`}),r.setLogger(R=>self.postMessage({type:E.LOG,data:R})),r.setProgress(R=>self.postMessage({type:E.PROGRESS,data:R})),o},m=({args:t,timeout:n=-1})=>{r.setTimeout(n),r.exec(...t);const e=r.ret;return r.reset(),e},l=({args:t,timeout:n=-1})=>{r.setTimeout(n),r.ffprobe(...t);const e=r.ret;return r.reset(),e},D=({path:t,data:n})=>(r.FS.writeFile(t,n),!0),S=({path:t,encoding:n})=>r.FS.readFile(t,{encoding:n}),I=({path:t})=>(r.FS.unlink(t),!0),L=({oldPath:t,newPath:n})=>(r.FS.rename(t,n),!0),N=({path:t})=>(r.FS.mkdir(t),!0),A=({path:t})=>{const n=r.FS.readdir(t),e=[];for(const o of n){const s=r.FS.stat(`${t}/${o}`),c=r.FS.isDir(s.mode);e.push({name:o,isDir:c})}return e},k=({path:t})=>(r.FS.rmdir(t),!0),w=({fsType:t,options:n,mountPoint:e})=>{const o=t,s=r.FS.filesystems[o];return s?(r.FS.mount(s,n,e),!0):!1},b=({mountPoint:t})=>(r.FS.unmount(t),!0);self.onmessage=async({data:{id:t,type:n,data:e}})=>{const o=[];let s;try{if(n!==E.LOAD&&!r)throw a;switch(n){case E.LOAD:s=await O(e);break;case E.EXEC:s=m(e);break;case E.FFPROBE:s=l(e);break;case E.WRITE_FILE:s=D(e);break;case E.READ_FILE:s=S(e);break;case E.DELETE_FILE:s=I(e);break;case E.RENAME:s=L(e);break;case E.CREATE_DIR:s=N(e);break;case E.LIST_DIR:s=A(e);break;case E.DELETE_DIR:s=k(e);break;case E.MOUNT:s=w(e);break;case E.UNMOUNT:s=b(e);break;default:throw f}}catch(c){self.postMessage({id:t,type:E.ERROR,data:c.toString()});return}s instanceof Uint8Array&&o.push(s.buffer),self.postMessage({id:t,type:n,data:s},o)}})();

src/backend/gradio_overlay_video/templates/example/assets/worker-BAOIWoxA.js

@@ -0,0 +1 @@
+(function(){"use strict";const i="https://unpkg.com/@ffmpeg/core@0.12.9/dist/umd/ffmpeg-core.js";var E;(function(t){t.LOAD="LOAD",t.EXEC="EXEC",t.FFPROBE="FFPROBE",t.WRITE_FILE="WRITE_FILE",t.READ_FILE="READ_FILE",t.DELETE_FILE="DELETE_FILE",t.RENAME="RENAME",t.CREATE_DIR="CREATE_DIR",t.LIST_DIR="LIST_DIR",t.DELETE_DIR="DELETE_DIR",t.ERROR="ERROR",t.DOWNLOAD="DOWNLOAD",t.PROGRESS="PROGRESS",t.LOG="LOG",t.MOUNT="MOUNT",t.UNMOUNT="UNMOUNT"})(E||(E={}));const f=new Error("unknown message type"),a=new Error("ffmpeg is not loaded, call `await ffmpeg.load()` first"),u=new Error("failed to import ffmpeg-core.js");let r;const O=async({coreURL:t,wasmURL:n,workerURL:e})=>{const o=!r;try{t||(t=i),importScripts(t)}catch{if((!t||t===i)&&(t=i.replace("/umd/","/esm/")),self.createFFmpegCore=(await import(t)).default,!self.createFFmpegCore)throw u}const s=t,c=n||t.replace(/.js$/g,".wasm"),p=e||t.replace(/.js$/g,".worker.js");return r=await self.createFFmpegCore({mainScriptUrlOrBlob:`${s}#${btoa(JSON.stringify({wasmURL:c,workerURL:p}))}`}),r.setLogger(R=>self.postMessage({type:E.LOG,data:R})),r.setProgress(R=>self.postMessage({type:E.PROGRESS,data:R})),o},m=({args:t,timeout:n=-1})=>{r.setTimeout(n),r.exec(...t);const e=r.ret;return r.reset(),e},l=({args:t,timeout:n=-1})=>{r.setTimeout(n),r.ffprobe(...t);const e=r.ret;return r.reset(),e},D=({path:t,data:n})=>(r.FS.writeFile(t,n),!0),S=({path:t,encoding:n})=>r.FS.readFile(t,{encoding:n}),I=({path:t})=>(r.FS.unlink(t),!0),L=({oldPath:t,newPath:n})=>(r.FS.rename(t,n),!0),N=({path:t})=>(r.FS.mkdir(t),!0),A=({path:t})=>{const n=r.FS.readdir(t),e=[];for(const o of n){const s=r.FS.stat(`${t}/${o}`),c=r.FS.isDir(s.mode);e.push({name:o,isDir:c})}return e},k=({path:t})=>(r.FS.rmdir(t),!0),w=({fsType:t,options:n,mountPoint:e})=>{const o=t,s=r.FS.filesystems[o];return s?(r.FS.mount(s,n,e),!0):!1},b=({mountPoint:t})=>(r.FS.unmount(t),!0);self.onmessage=async({data:{id:t,type:n,data:e}})=>{const o=[];let s;try{if(n!==E.LOAD&&!r)throw a;switch(n){case E.LOAD:s=await O(e);break;case E.EXEC:s=m(e);break;case E.FFPROBE:s=l(e);break;case E.WRITE_FILE:s=D(e);break;case E.READ_FILE:s=S(e);break;case E.DELETE_FILE:s=I(e);break;case E.RENAME:s=L(e);break;case E.CREATE_DIR:s=N(e);break;case E.LIST_DIR:s=A(e);break;case E.DELETE_DIR:s=k(e);break;case E.MOUNT:s=w(e);break;case E.UNMOUNT:s=b(e);break;default:throw f}}catch(c){self.postMessage({id:t,type:E.ERROR,data:c.toString()});return}s instanceof Uint8Array&&o.push(s.buffer),self.postMessage({id:t,type:n,data:s},o)}})();

src/backend/gradio_overlay_video/templates/example/style.css

@@ -0,0 +1 @@
+.unstyled-link.svelte-151nsdd{all:unset;cursor:pointer}.overlay.svelte-1pwzuub{position:absolute;background-color:#0006;width:100%;height:100%}.hidden.svelte-1pwzuub{display:none}.load-wrap.svelte-1pwzuub{display:flex;justify-content:center;align-items:center;height:100%}.loader.svelte-1pwzuub{display:flex;position:relative;background-color:var(--border-color-accent-subdued);animation:svelte-1pwzuub-shadowPulse 2s linear infinite;box-shadow:-24px 0 var(--border-color-accent-subdued),24px 0 var(--border-color-accent-subdued);margin:var(--spacing-md);border-radius:50%;width:10px;height:10px;scale:.5}@keyframes svelte-1pwzuub-shadowPulse{33%{box-shadow:-24px 0 var(--border-color-accent-subdued),24px 0 #fff;background:#fff}66%{box-shadow:-24px 0 #fff,24px 0 #fff;background:var(--border-color-accent-subdued)}to{box-shadow:-24px 0 #fff,24px 0 var(--border-color-accent-subdued);background:#fff}}.container.svelte-13u05e4{flex:none;max-width:none}.container.svelte-13u05e4 video{width:var(--size-full);height:var(--size-full);object-fit:cover}.container.svelte-13u05e4:hover,.container.selected.svelte-13u05e4{border-color:var(--border-color-accent)}.container.table.svelte-13u05e4{margin:0 auto;border:2px solid var(--border-color-primary);border-radius:var(--radius-lg);overflow:hidden;width:var(--size-20);height:var(--size-20);object-fit:cover}.container.gallery.svelte-13u05e4{height:var(--size-20);max-height:var(--size-20);object-fit:cover}

src/backend/gradio_overlay_video/visualization_processors.py

@@ -0,0 +1,502 @@
+"""
+Visualization processors for different overlay types.
+Each processor extracts and prepares specific visualization data from JSON.
+Supports multiple keypoint formats: MediaPipe, COCO, COCO-WholeBody, Sociopticon.
+"""
+
+from typing import Dict, List, Any, Optional, Tuple
+import math
+from dataclasses import dataclass
+from .keypoint_formats import KeypointFormat, SkeletonDefinitions, KeypointFormatDetector, YOLOv11DataConverter
+
+
+@dataclass
+class VisualizationFrame:
+    """Unified frame data structure for all visualization types."""
+    timestamp: float
+    joints: Optional[List[Dict]] = None
+    bones: Optional[List[Dict]] = None
+    direction_arrow: Optional[Dict] = None
+    motion_trail: Optional[List[Dict]] = None
+    laban_metrics: Optional[Dict] = None
+
+
+class JointsProcessor:
+    """Processes joint/keypoint data for circle visualization."""
+    
+    def process_frame(self, frame_data: Dict) -> Optional[List[Dict]]:
+        """Extract joint positions from frame data."""
+        keypoints_data = frame_data.get('keypoints', [])
+        if not keypoints_data or not keypoints_data[0].get('points'):
+            return None
+        
+        joints = []
+        points = keypoints_data[0]['points']
+        
+        for point in points:
+            x = point.get('x', 0)
+            y = point.get('y', 0)
+            confidence = point.get('confidence', 0)
+            
+            # Filter out low confidence points and 0,0 coordinates (invalid detections)
+            if confidence > 0.3 and not (x == 0 and y == 0):
+                joints.append({
+                    'x': x,
+                    'y': y,
+                    'confidence': confidence,
+                    'name': point.get('name', '')
+                })
+        
+        return joints if joints else None
+
+
+class BonesProcessor:
+    """Processes skeleton/bone connections for line visualization."""
+    
+    # MediaPipe pose skeleton connections (33 keypoints)
+    MEDIAPIPE_SKELETON = [
+        # Face connections
+        (0, 1), (1, 2), (2, 3), (3, 7),  # left eye region
+        (0, 4), (4, 5), (5, 6), (6, 8),  # right eye region
+        (9, 10),  # mouth
+        # Upper body
+        (11, 12),  # shoulders
+        (11, 13), (13, 15),  # left arm
+        (12, 14), (14, 16),  # right arm
+        (11, 23), (12, 24),  # shoulders to hips
+        (23, 24),  # hips
+        # Lower body
+        (23, 25), (25, 27), (27, 29), (27, 31),  # left leg
+        (24, 26), (26, 28), (28, 30), (28, 32),  # right leg
+        # Hands
+        (15, 17), (15, 19), (15, 21),  # left hand
+        (16, 18), (16, 20), (16, 22),  # right hand
+    ]
+    
+    # COCO keypoint skeleton connections (17 keypoints)
+    # Order: nose, eyes, ears, shoulders, elbows, wrists, hips, knees, ankles
+    COCO_SKELETON = [
+        # Head connections
+        (0, 1), (0, 2),  # nose to eyes
+        (1, 3), (2, 4),  # eyes to ears
+        # Upper body
+        (5, 6),  # shoulders
+        (5, 7), (7, 9),  # left arm
+        (6, 8), (8, 10),  # right arm
+        (5, 11), (6, 12),  # shoulders to hips
+        (11, 12),  # hips
+        # Lower body
+        (11, 13), (13, 15),  # left leg
+        (12, 14), (14, 16),  # right leg
+    ]
+    
+    # COCO-WholeBody skeleton connections (133 keypoints total)
+    # Body (17) + Face (68) + Left Hand (21) + Right Hand (21) + Feet (6)
+    COCO_WHOLEBODY_SKELETON = [
+        # Body connections (same as COCO)
+        (0, 1), (0, 2), (1, 3), (2, 4),  # head
+        (5, 6),  # shoulders
+        (5, 7), (7, 9),  # left arm
+        (6, 8), (8, 10),  # right arm
+        (5, 11), (6, 12),  # shoulders to hips
+        (11, 12),  # hips
+        (11, 13), (13, 15),  # left leg
+        (12, 14), (14, 16),  # right leg
+        
+        # Face outline (simplified from 68 points)
+        (17, 18), (18, 19), (19, 20), (20, 21),  # jaw line (partial)
+        (22, 23), (23, 24), (24, 25), (25, 26),  # eyebrow left
+        (27, 28), (28, 29), (29, 30), (30, 31),  # eyebrow right
+        (36, 37), (37, 38), (38, 39), (39, 40), (40, 41), (41, 36),  # left eye
+        (42, 43), (43, 44), (44, 45), (45, 46), (46, 47), (47, 42),  # right eye
+        (48, 49), (49, 50), (50, 51), (51, 52), (52, 53),  # mouth outline (partial)
+        
+        # Left hand connections (21 points starting at index 91)
+        (91, 92), (92, 93), (93, 94), (94, 95),  # thumb
+        (91, 96), (96, 97), (97, 98), (98, 99),  # index finger
+        (91, 100), (100, 101), (101, 102), (102, 103),  # middle finger
+        (91, 104), (104, 105), (105, 106), (106, 107),  # ring finger
+        (91, 108), (108, 109), (109, 110), (110, 111),  # pinky
+        
+        # Right hand connections (21 points starting at index 112)
+        (112, 113), (113, 114), (114, 115), (115, 116),  # thumb
+        (112, 117), (117, 118), (118, 119), (119, 120),  # index finger
+        (112, 121), (121, 122), (122, 123), (123, 124),  # middle finger
+        (112, 125), (125, 126), (126, 127), (127, 128),  # ring finger
+        (112, 129), (129, 130), (130, 131), (131, 132),  # pinky
+    ]
+    
+    # Sociopticon skeleton connections (custom format)
+    # Assuming similar to COCO but with additional torso detail
+    SOCIOPTICON_SKELETON = [
+        # Head connections
+        (0, 1), (0, 2), (1, 3), (2, 4),  # head structure
+        # Upper body with more detail
+        (5, 6),  # shoulders
+        (5, 7), (7, 9),  # left arm
+        (6, 8), (8, 10),  # right arm
+        (5, 11), (6, 12),  # shoulders to hips
+        (11, 12),  # hips
+        # Torso detail (if available)
+        (5, 17), (6, 18),  # shoulder to mid-torso
+        (17, 18), (17, 11), (18, 12),  # torso connections
+        # Lower body
+        (11, 13), (13, 15),  # left leg
+        (12, 14), (14, 16),  # right leg
+    ]
+    
+    def __init__(self):
+        self.detected_format: Optional[KeypointFormat] = None
+    
+    def detect_keypoint_format(self, points: List[Dict]) -> KeypointFormat:
+        """Detect the keypoint format based on number of points and structure."""
+        return KeypointFormatDetector.detect_format(points)
+    
+    def get_skeleton_connections(self, format_type: KeypointFormat) -> List[Tuple[int, int]]:
+        """Get skeleton connections for the specified format."""
+        return SkeletonDefinitions.get_skeleton(format_type)
+    
+    def process_frame(self, frame_data: Dict) -> Optional[List[Dict]]:
+        """Extract bone connections from frame data with auto-format detection."""
+        keypoints_data = frame_data.get('keypoints', [])
+        if not keypoints_data or not keypoints_data[0].get('points'):
+            return None
+        
+        points = keypoints_data[0]['points']
+        
+        # Auto-detect keypoint format if not already detected
+        if not self.detected_format:
+            self.detected_format = self.detect_keypoint_format(points)
+            format_info = KeypointFormatDetector.format_info(self.detected_format)
+            print(f"Detected keypoint format: {format_info['name']} ({len(points)} points)")
+        
+        # Get appropriate skeleton connections
+        skeleton_connections = self.get_skeleton_connections(self.detected_format)
+        
+        # Try index-based approach first (more reliable for standard formats)
+        bones = self._process_by_index(points, skeleton_connections)
+        
+        # Fallback to name-based approach if index-based fails
+        if not bones:
+            bones = self._process_by_name(points, skeleton_connections)
+        
+        return bones if bones else None
+    
+    def _process_by_index(self, points: List[Dict], skeleton_connections: List[Tuple[int, int]]) -> List[Dict]:
+        """Process skeleton connections using point indices."""
+        bones = []
+        
+        for connection in skeleton_connections:
+            idx1, idx2 = connection
+            
+            # Check if indices are valid
+            if idx1 < len(points) and idx2 < len(points):
+                p1 = points[idx1]
+                p2 = points[idx2]
+                
+                p1_x, p1_y = p1.get('x', 0), p1.get('y', 0)
+                p2_x, p2_y = p2.get('x', 0), p2.get('y', 0)
+                p1_conf, p2_conf = p1.get('confidence', 0), p2.get('confidence', 0)
+                
+                # Check confidence, validity, and filter out 0,0 coordinates
+                if (p1_conf > 0.3 and p2_conf > 0.3 and
+                    not math.isnan(p1_x) and not math.isnan(p1_y) and
+                    not math.isnan(p2_x) and not math.isnan(p2_y) and
+                    not (p1_x == 0 and p1_y == 0) and
+                    not (p2_x == 0 and p2_y == 0)):
+                    
+                    bones.append({
+                        'start': {'x': p1_x, 'y': p1_y},
+                        'end': {'x': p2_x, 'y': p2_y},
+                        'confidence': min(p1_conf, p2_conf),
+                        'connection': f"{idx1}-{idx2}",
+                        'format': self.detected_format.value
+                    })
+        
+        return bones
+    
+    def _process_by_name(self, points: List[Dict], skeleton_connections: List[Tuple[int, int]]) -> List[Dict]:
+        """Fallback: Process skeleton connections using point names (MediaPipe style)."""
+        # Create lookup dict by keypoint name
+        points_by_name = {p.get('name', ''): p for p in points}
+        
+        # Get keypoint names in order
+        keypoint_names = [p.get('name', '') for p in points]
+        
+        bones = []
+        
+        for connection in skeleton_connections:
+            idx1, idx2 = connection
+            
+            if idx1 < len(keypoint_names) and idx2 < len(keypoint_names):
+                name1 = keypoint_names[idx1]
+                name2 = keypoint_names[idx2]
+                
+                if name1 in points_by_name and name2 in points_by_name:
+                    p1 = points_by_name[name1]
+                    p2 = points_by_name[name2]
+                    
+                    p1_x, p1_y = p1.get('x', 0), p1.get('y', 0)
+                    p2_x, p2_y = p2.get('x', 0), p2.get('y', 0)
+                    p1_conf, p2_conf = p1.get('confidence', 0), p2.get('confidence', 0)
+                    
+                    # Check confidence, validity, and filter out 0,0 coordinates
+                    if (p1_conf > 0.3 and p2_conf > 0.3 and
+                        not math.isnan(p1_x) and not math.isnan(p1_y) and
+                        not math.isnan(p2_x) and not math.isnan(p2_y) and
+                        not (p1_x == 0 and p1_y == 0) and
+                        not (p2_x == 0 and p2_y == 0)):
+                        
+                        bones.append({
+                            'start': {'x': p1_x, 'y': p1_y},
+                            'end': {'x': p2_x, 'y': p2_y},
+                            'confidence': min(p1_conf, p2_conf),
+                            'connection': f"{name1}-{name2}",
+                            'format': self.detected_format.value
+                        })
+        
+        return bones
+
+
+class DirectionArrowProcessor:
+    """Processes movement direction data for arrow visualization."""
+    
+    def process_frame(self, frame_data: Dict) -> Optional[Dict]:
+        """Extract direction arrow data from frame metrics."""
+        metrics = frame_data.get('metrics', {})
+        keypoints_data = frame_data.get('keypoints', [])
+        
+        direction = metrics.get('direction', 'stationary')
+        if direction == 'stationary':
+            return None
+        
+        # Calculate body center from keypoints
+        if keypoints_data and keypoints_data[0].get('points'):
+            points = keypoints_data[0]['points']
+            valid_points = [p for p in points 
+                          if p.get('confidence', 0) > 0.3 and 
+                             not math.isnan(p.get('x', 0)) and 
+                             not math.isnan(p.get('y', 0)) and
+                             not (p.get('x', 0) == 0 and p.get('y', 0) == 0)]
+            
+            if valid_points:
+                center_x = sum(p['x'] for p in valid_points) / len(valid_points)
+                center_y = sum(p['y'] for p in valid_points) / len(valid_points)
+                
+                # Direction vectors (normalized coordinates)
+                direction_vectors = {
+                    'up': (0, -0.1),
+                    'down': (0, 0.1),
+                    'left': (-0.1, 0),
+                    'right': (0.1, 0),
+                }
+                
+                if direction in direction_vectors:
+                    dx, dy = direction_vectors[direction]
+                    
+                    return {
+                        'start': {'x': center_x, 'y': center_y},
+                        'end': {'x': center_x + dx, 'y': center_y + dy},
+                        'direction': direction,
+                        'intensity': metrics.get('intensity', 'medium'),
+                        'speed': metrics.get('speed', 'medium'),
+                        'velocity': metrics.get('velocity', 0)
+                    }
+        
+        return None
+
+
+class MotionTrailProcessor:
+    """Processes motion trail data for path visualization."""
+    
+    def __init__(self, trail_length: int = 10):
+        self.trail_length = trail_length
+        self.trails = {}  # Store trails for each keypoint
+    
+    def process_frame(self, frame_data: Dict, frame_index: int) -> Optional[List[Dict]]:
+        """Extract and update motion trail data."""
+        keypoints_data = frame_data.get('keypoints', [])
+        if not keypoints_data or not keypoints_data[0].get('points'):
+            return None
+        
+        points = keypoints_data[0]['points']
+        
+        # Update trails for each keypoint
+        for point in points:
+            x, y = point.get('x', 0), point.get('y', 0)
+            confidence = point.get('confidence', 0)
+            
+            # Filter out low confidence and 0,0 coordinates
+            if confidence > 0.3 and not (x == 0 and y == 0):
+                name = point.get('name', '')
+                if name not in self.trails:
+                    self.trails[name] = []
+                
+                # Add current position to trail
+                self.trails[name].append({
+                    'x': x,
+                    'y': y,
+                    'frame': frame_index
+                })
+                
+                # Keep only recent positions
+                if len(self.trails[name]) > self.trail_length:
+                    self.trails[name] = self.trails[name][-self.trail_length:]
+        
+        # Create trail segments for drawing
+        trail_segments = []
+        for joint_name, trail in self.trails.items():
+            if len(trail) >= 2:
+                for i in range(1, len(trail)):
+                    p1 = trail[i-1]
+                    p2 = trail[i]
+                    
+                    # Calculate alpha based on age
+                    alpha = i / len(trail)
+                    
+                    trail_segments.append({
+                        'start': {'x': p1['x'], 'y': p1['y']},
+                        'end': {'x': p2['x'], 'y': p2['y']},
+                        'alpha': alpha,
+                        'joint': joint_name
+                    })
+        
+        return trail_segments if trail_segments else None
+
+
+class LabanProcessor:
+    """Processes Laban Movement Analysis data for text and visual overlays."""
+    
+    def process_frame(self, frame_data: Dict) -> Optional[Dict]:
+        """Extract Laban notation metrics from frame data."""
+        metrics = frame_data.get('metrics', {})
+        if not metrics:
+            return None
+        
+        # Extract and clean metrics
+        def safe_value(val, default=0):
+            if isinstance(val, (int, float)) and not math.isnan(val):
+                return val
+            return default
+        
+        laban_data = {
+            'direction': metrics.get('direction', 'stationary'),
+            'intensity': metrics.get('intensity', 'low'),
+            'speed': metrics.get('speed', 'slow'),
+            'velocity': safe_value(metrics.get('velocity'), 0),
+            'acceleration': safe_value(metrics.get('acceleration'), 0),
+            'fluidity': safe_value(metrics.get('fluidity'), 0),
+            'expansion': safe_value(metrics.get('expansion'), 0),
+            'total_displacement': safe_value(metrics.get('total_displacement'), 0)
+        }
+        
+        # Add center displacement if available
+        center_displacement = metrics.get('center_displacement')
+        if center_displacement:
+            laban_data['center_displacement'] = {
+                'x': safe_value(center_displacement.get('x'), 0),
+                'y': safe_value(center_displacement.get('y'), 0)
+            }
+        
+        return laban_data
+
+
+class VisualizationProcessor:
+    """Main processor that coordinates all visualization types."""
+    
+    def __init__(self, 
+                 enable_joints: bool = True,
+                 enable_bones: bool = True,
+                 enable_direction_arrows: bool = True,
+                 enable_motion_trails: bool = True,
+                 enable_laban: bool = True,
+                 trail_length: int = 10):
+        
+        self.enable_joints = enable_joints
+        self.enable_bones = enable_bones
+        self.enable_direction_arrows = enable_direction_arrows
+        self.enable_motion_trails = enable_motion_trails
+        self.enable_laban = enable_laban
+        self.detected_format = None
+        
+        # Initialize processors
+        self.joints_processor = JointsProcessor() if enable_joints else None
+        self.bones_processor = BonesProcessor() if enable_bones else None
+        self.direction_processor = DirectionArrowProcessor() if enable_direction_arrows else None
+        self.trail_processor = MotionTrailProcessor(trail_length) if enable_motion_trails else None
+        self.laban_processor = LabanProcessor() if enable_laban else None
+    
+    def process_json_data(self, json_data: Dict) -> Dict:
+        """Process full JSON data and return streamlined visualization instructions."""
+        # Check if this is YOLOv11 format and convert if needed
+        if KeypointFormatDetector.detect_yolo11_format(json_data):
+            print("Detected YOLOv11 pose format - converting to standard format...")
+            
+            # Estimate video dimensions from keypoints
+            if 'keypoints' in json_data and json_data['keypoints']:
+                first_frame_keypoints = json_data['keypoints'][0]
+                video_width, video_height = YOLOv11DataConverter.estimate_video_dimensions(first_frame_keypoints)
+                print(f"Estimated video dimensions: {video_width}x{video_height}")
+            else:
+                video_width, video_height = 1920, 1080  # Default
+            
+            # Convert YOLOv11 to standard format
+            json_data = YOLOv11DataConverter.convert_to_standard_format(
+                json_data, video_width, video_height
+            )
+            print("YOLOv11 data converted successfully")
+        
+        video_info = json_data.get('video_info', {})
+        source_frames = json_data.get('movement_analysis', {}).get('frames', [])
+        
+        processed_frames = []
+        
+        for frame_index, frame in enumerate(source_frames):
+            viz_frame = VisualizationFrame(
+                timestamp=frame.get('timestamp', 0)
+            )
+            
+            # Process each visualization type
+            if self.joints_processor:
+                viz_frame.joints = self.joints_processor.process_frame(frame)
+            
+            if self.bones_processor:
+                viz_frame.bones = self.bones_processor.process_frame(frame)
+            
+            if self.direction_processor:
+                viz_frame.direction_arrow = self.direction_processor.process_frame(frame)
+            
+            if self.trail_processor:
+                viz_frame.motion_trail = self.trail_processor.process_frame(frame, frame_index)
+            
+            if self.laban_processor:
+                viz_frame.laban_metrics = self.laban_processor.process_frame(frame)
+            
+            # Convert to dict for JSON serialization
+            processed_frames.append({
+                'timestamp': viz_frame.timestamp,
+                'joints': viz_frame.joints,
+                'bones': viz_frame.bones,
+                'direction_arrow': viz_frame.direction_arrow,
+                'motion_trail': viz_frame.motion_trail,
+                'laban_metrics': viz_frame.laban_metrics
+            })
+        
+        # Get detected format from bones processor
+        detected_format = None
+        if self.bones_processor and hasattr(self.bones_processor, 'detected_format'):
+            detected_format = self.bones_processor.detected_format
+        
+        return {
+            'video_info': video_info,
+            'fps': video_info.get('fps', 30),  # Extract FPS for frontend
+            'frames': processed_frames,
+            'keypoint_format': detected_format.value if detected_format else None,  # Add detected format info
+            'capabilities': {
+                'has_joints': self.enable_joints,
+                'has_bones': self.enable_bones,
+                'has_direction_arrows': self.enable_direction_arrows,
+                'has_motion_trails': self.enable_motion_trails,
+                'has_laban': self.enable_laban
+            }
+        }

src/demo/.gradio/cached_examples/9/Output/b558f74ed1913fbba573/balette.mp4

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+size 621415

src/demo/.gradio/cached_examples/9/log.csv

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+Output,timestamp
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\""fast\"", \""velocity\"": 0.2271, \""acceleration\"": 17.3051, \""fluidity\"": 0.013, \""expansion\"": 0.213, \""total_displacement\"": 0.0006, \""center_displacement\"": {\""x\"": 0.0005, \""y\"": 0.0004}}}, {\""timestamp\"": 2.466666666666667, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.1935, \""acceleration\"": 15.6304, \""fluidity\"": 0.011, \""expansion\"": 0.213, \""total_displacement\"": 0.0007, \""center_displacement\"": {\""x\"": 0.0006, \""y\"": -0.0003}}}, {\""timestamp\"": 2.4833333333333334, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.2306, \""acceleration\"": 16.3765, \""fluidity\"": 0.01, \""expansion\"": 0.212, \""total_displacement\"": 0.0045, \""center_displacement\"": {\""x\"": 0.0031, \""y\"": -0.0033}}}, {\""timestamp\"": 2.5, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.2042, \""acceleration\"": 13.8859, \""fluidity\"": 0.008, \""expansion\"": 0.211, \""total_displacement\"": 0.0028, \""center_displacement\"": {\""x\"": 0.0009, \""y\"": -0.0026}}}, {\""timestamp\"": 2.5166666666666666, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""up\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.3447, \""acceleration\"": 20.2457, \""fluidity\"": 0.007, \""expansion\"": 0.21, \""total_displacement\"": 0.0125, \""center_displacement\"": {\""x\"": 0.003, \""y\"": -0.0121}}}, {\""timestamp\"": 2.533333333333333, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.2624, \""acceleration\"": 19.8363, \""fluidity\"": 0.006, \""expansion\"": 0.21, \""total_displacement\"": 0.0045, \""center_displacement\"": {\""x\"": 0.0011, \""y\"": -0.0044}}}, {\""timestamp\"": 2.55, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.2262, \""acceleration\"": 16.141, \""fluidity\"": 0.006, \""expansion\"": 0.211, \""total_displacement\"": 0.0016, \""center_displacement\"": {\""x\"": 0.0007, \""y\"": -0.0014}}}, {\""timestamp\"": 2.5666666666666664, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""up\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.4216, \""acceleration\"": 25.4549, \""fluidity\"": 0.005, \""expansion\"": 0.206, \""total_displacement\"": 0.0142, \""center_displacement\"": {\""x\"": 0.0032, \""y\"": -0.0139}}}, {\""timestamp\"": 2.5833333333333335, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.2673, \""acceleration\"": 25.7738, \""fluidity\"": 0.004, \""expansion\"": 0.206, \""total_displacement\"": 0.0026, \""center_displacement\"": {\""x\"": 0.0011, \""y\"": -0.0024}}}, {\""timestamp\"": 2.6, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""up\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.3323, \""acceleration\"": 20.6687, 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0.0034, \""y\"": -0.0034}}}, {\""timestamp\"": 2.65, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.2577, \""acceleration\"": 17.9181, \""fluidity\"": 0.002, \""expansion\"": 0.194, \""total_displacement\"": 0.0015, \""center_displacement\"": {\""x\"": 0.0007, \""y\"": -0.0014}}}, {\""timestamp\"": 2.6666666666666665, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.2337, \""acceleration\"": 14.5879, \""fluidity\"": 0.002, \""expansion\"": 0.194, \""total_displacement\"": 0.0012, \""center_displacement\"": {\""x\"": 0.0005, \""y\"": -0.0011}}}, {\""timestamp\"": 2.683333333333333, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.2467, \""acceleration\"": 14.5353, \""fluidity\"": 0.002, \""expansion\"": 0.191, \""total_displacement\"": 0.0033, \""center_displacement\"": {\""x\"": 0.0031, \""y\"": -0.0009}}}, {\""timestamp\"": 2.7, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.2213, \""acceleration\"": 12.4955, \""fluidity\"": 0.002, \""expansion\"": 0.191, \""total_displacement\"": 0.0025, \""center_displacement\"": {\""x\"": 0.0011, \""y\"": -0.0022}}}, {\""timestamp\"": 2.716666666666667, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.2164, \""acceleration\"": 10.6537, \""fluidity\"": 0.001, \""expansion\"": 0.187, \""total_displacement\"": 0.0027, \""center_displacement\"": {\""x\"": 0.0027, \""y\"": -0.0006}}}, {\""timestamp\"": 2.7333333333333334, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.1839, \""acceleration\"": 11.1152, \""fluidity\"": 0.001, \""expansion\"": 0.186, \""total_displacement\"": 0.0008, \""center_displacement\"": {\""x\"": 0.0007, \""y\"": -0.0005}}}, {\""timestamp\"": 2.75, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.1742, \""acceleration\"": 8.9315, \""fluidity\"": 0.001, \""expansion\"": 0.185, \""total_displacement\"": 0.0013, \""center_displacement\"": {\""x\"": 0.0008, \""y\"": -0.001}}}, {\""timestamp\"": 2.7666666666666666, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.2067, \""acceleration\"": 10.3295, \""fluidity\"": 0.001, \""expansion\"": 0.18, \""total_displacement\"": 0.0042, \""center_displacement\"": {\""x\"": 0.0028, \""y\"": -0.0032}}}, {\""timestamp\"": 2.783333333333333, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.1622, \""acceleration\"": 10.1212, \""fluidity\"": 0.001, \""expansion\"": 0.178, \""total_displacement\"": 0.0014, \""center_displacement\"": {\""x\"": 0.0012, \""y\"": -0.0007}}}, {\""timestamp\"": 2.8, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.1856, \""acceleration\"": 9.0748, \""fluidity\"": 0.001, \""expansion\"": 0.177, \""total_displacement\"": 0.0033, \""center_displacement\"": {\""x\"": 0.0007, \""y\"": -0.0032}}}, {\""timestamp\"": 2.8166666666666664, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.156, \""acceleration\"": 9.3265, \""fluidity\"": 0.001, \""expansion\"": 0.176, \""total_displacement\"": 0.0012, \""center_displacement\"": {\""x\"": 0.0006, \""y\"": -0.001}}}, {\""timestamp\"": 2.8333333333333335, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.1451, \""acceleration\"": 7.2397, \""fluidity\"": 0.001, \""expansion\"": 0.172, \""total_displacement\"": 0.0013, \""center_displacement\"": {\""x\"": 0.0013, \""y\"": -0.0002}}}, {\""timestamp\"": 2.85, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.1303, \""acceleration\"": 7.0422, \""fluidity\"": 0, \""expansion\"": 0.171, \""total_displacement\"": 0.0006, \""center_displacement\"": {\""x\"": 0.0004, \""y\"": -0.0004}}}, {\""timestamp\"": 2.8666666666666667, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.1148, \""acceleration\"": 6.0024, \""fluidity\"": 0, \""expansion\"": 0.17, \""total_displacement\"": 0.0005, \""center_displacement\"": {\""x\"": -0.0001, \""y\"": -0.0005}}}, {\""timestamp\"": 2.8833333333333333, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.131, \""acceleration\"": 6.6612, \""fluidity\"": 0, \""expansion\"": 0.171, \""total_displacement\"": 0.0022, \""center_displacement\"": {\""x\"": 0.0022, \""y\"": 0.0003}}}, {\""timestamp\"": 2.9, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.1066, \""acceleration\"": 6.179, \""fluidity\"": 0, \""expansion\"": 0.17, \""total_displacement\"": 0.0008, \""center_displacement\"": {\""x\"": 0.0008, \""y\"": 0.0002}}}, {\""timestamp\"": 2.9166666666666665, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.1063, \""acceleration\"": 5.0441, \""fluidity\"": 0, \""expansion\"": 0.168, \""total_displacement\"": 0.0012, \""center_displacement\"": {\""x\"": 0.0012, \""y\"": 0.0001}}}, {\""timestamp\"": 2.933333333333333, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.0941, \""acceleration\"": 5.0755, \""fluidity\"": 0, \""expansion\"": 0.168, \""total_displacement\"": 0.0005, \""center_displacement\"": {\""x\"": 0.0001, \""y\"": -0.0005}}}, {\""timestamp\"": 2.95, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.082, \""acceleration\"": 4.2463, \""fluidity\"": 0, \""expansion\"": 0.168, \""total_displacement\"": 0.0003, \""center_displacement\"": {\""x\"": 0.0003, \""y\"": -0.0001}}}, {\""timestamp\"": 2.966666666666667, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.0747, \""acceleration\"": 3.6591, \""fluidity\"": 0, \""expansion\"": 0.167, \""total_displacement\"": 0.0003, \""center_displacement\"": {\""x\"": 0, \""y\"": 0.0003}}}, {\""timestamp\"": 2.9833333333333334, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""medium\"", \""speed\"": \""fast\"", \""velocity\"": 0.0671, \""acceleration\"": 3.2635, \""fluidity\"": 0.159, \""expansion\"": 0.166, \""total_displacement\"": 0.0003, \""center_displacement\"": {\""x\"": 0.0002, \""y\"": 0.0002}}}, {\""timestamp\"": 3, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.0591, \""acceleration\"": 2.8652, \""fluidity\"": 0.04, \""expansion\"": 0.165, \""total_displacement\"": 0.0002, \""center_displacement\"": {\""x\"": 0, \""y\"": 0.0002}}}, {\""timestamp\"": 3.0166666666666666, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""slow\"", \""velocity\"": 0.0517, \""acceleration\"": 2.5477, \""fluidity\"": 0.05, \""expansion\"": 0.164, \""total_displacement\"": 0.0001, \""center_displacement\"": {\""x\"": -0.0001, \""y\"": 0}}}, {\""timestamp\"": 3.033333333333333, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.0687, \""acceleration\"": 3.5291, \""fluidity\"": 0.062, \""expansion\"": 0.164, \""total_displacement\"": 0.0016, \""center_displacement\"": {\""x\"": 0.0004, \""y\"": 0.0016}}}, {\""timestamp\"": 3.05, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.0566, \""acceleration\"": 2.9811, \""fluidity\"": 0.038, \""expansion\"": 0.164, \""total_displacement\"": 0.0008, \""center_displacement\"": {\""x\"": 0.0001, \""y\"": 0.0008}}}, {\""timestamp\"": 3.0666666666666664, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.0508, \""acceleration\"": 2.5782, \""fluidity\"": 0.037, \""expansion\"": 0.164, \""total_displacement\"": 0.0004, \""center_displacement\"": {\""x\"": 0, \""y\"": 0.0004}}}, {\""timestamp\"": 3.0833333333333335, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.0475, \""acceleration\"": 2.4533, \""fluidity\"": 0.034, \""expansion\"": 0.16, \""total_displacement\"": 0.0002, \""center_displacement\"": {\""x\"": 0.0001, \""y\"": 0.0002}}}, {\""timestamp\"": 3.1, \""joints\"": null, \""bones\"": null, \""direction_arrow\"": null, \""motion_trail\"": null, \""laban_metrics\"": {\""direction\"": \""stationary\"", \""intensity\"": \""high\"", \""speed\"": \""fast\"", \""velocity\"": 0.044, \""acceleration\"": 2.1114, \""fluidity\"": 0.027, \""expansion\"": 0.159, \""total_displacement\"": 0.0004, \""center_displacement\"": {\""x\"": 0.0003, \""y\"": 0.0002}}}], \""capabilities\"": {\""has_joints\"": true, \""has_bones\"": true, \""has_direction_arrows\"": true, \""has_motion_trails\"": true, \""has_laban\"": true}}""}",2025-06-09 16:49:29.087331

src/demo/app.py

@@ -0,0 +1,81 @@
+
+import gradio as gr
+from gradio_overlay_video import OverlayVideo
+from pathlib import Path
+
+DEMO_DIR = Path(__file__).parent.parent
+sample_video_path = DEMO_DIR / "files/balette.mp4"
+sample_json_path = DEMO_DIR / "files/mediapipe_full_kp_balette.json"
+mediapipe_json_path = DEMO_DIR / "files/mediapipe_heavy_kp_parkour.json"
+movenet_json_path = DEMO_DIR / "files/movenet_thunder_kp_skate.json"
+yolo8_json_path = DEMO_DIR / "files/yolov8_kp_dance.json"
+yolo11_json_path = DEMO_DIR / "files/yolov11.json"
+
+def prepare_visualization_data(json_path, video_path):
+    """
+    This function simply validates the inputs and passes them to the
+    custom OverlayVideo component for frontend processing.
+    """
+    if not json_path:
+        raise gr.Error("A JSON file is required to generate a visualization.")
+
+    print(f"✅ Preparing visualization with JSON: {json_path}")
+    if video_path:
+        print(f"✅ Video background provided: {video_path}")
+    else:
+        print("ℹ️ No video background provided. Visualization will be on a black background.")
+
+    # The backend's job is just to pass the filepaths to the frontend.
+    # The return format (video_path, json_path) must match what postprocess expects.
+    return (video_path, json_path)
+
+
+with gr.Blocks(theme=gr.themes.Default(primary_hue="rose", secondary_hue="pink")) as demo:
+    gr.Markdown(
+        "# 🩰 Interactive Pose Visualization\n"
+        "1. **Upload a JSON file** with pose data.\n"
+        "2. **(Optional) Upload a video** to use as the background.\n"
+        "3. Click 'Display Visualization' to see the interactive result."
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Use standard gr.File for robust input handling
+            json_upload = gr.File(
+                label="Upload Required JSON File",
+                file_types=[".json"],
+                type="filepath"
+            )
+            video_upload = gr.File(
+                label="Upload Optional Video File",
+                file_types=["video"],
+                type="filepath",
+                value=None 
+            )
+            btn = gr.Button("Display Visualization", variant="primary")
+        
+        with gr.Column(scale=1):
+            output_ov = OverlayVideo(label="Output", interactive=False, autoplay=True)
+
+    btn.click(
+        fn=prepare_visualization_data,
+        inputs=[json_upload, video_upload],
+        outputs=[output_ov]
+    )
+    
+    gr.Examples(
+        examples=[
+            [str(mediapipe_json_path), None],
+            [str(movenet_json_path), None],
+            [str(yolo8_json_path), None],
+            [str(sample_json_path), str(sample_video_path)],
+            [str(yolo11_json_path), None]
+        ],
+        inputs=[json_upload, video_upload],
+        outputs=output_ov,
+        fn=prepare_visualization_data,
+        cache_examples=True
+    )
+
+if __name__ == "__main__":
+    demo.launch(allowed_paths=["/Users/csabi/Develop/overlay_video/files"])

src/demo/css.css

@@ -0,0 +1,157 @@
+html {
+	font-family: Inter;
+	font-size: 16px;
+	font-weight: 400;
+	line-height: 1.5;
+	-webkit-text-size-adjust: 100%;
+	background: #fff;
+	color: #323232;
+	-webkit-font-smoothing: antialiased;
+	-moz-osx-font-smoothing: grayscale;
+	text-rendering: optimizeLegibility;
+}
+
+:root {
+	--space: 1;
+	--vspace: calc(var(--space) * 1rem);
+	--vspace-0: calc(3 * var(--space) * 1rem);
+	--vspace-1: calc(2 * var(--space) * 1rem);
+	--vspace-2: calc(1.5 * var(--space) * 1rem);
+	--vspace-3: calc(0.5 * var(--space) * 1rem);
+}
+
+.app {
+	max-width: 748px !important;
+}
+
+.prose p {
+	margin: var(--vspace) 0;
+	line-height: var(--vspace * 2);
+	font-size: 1rem;
+}
+
+code {
+	font-family: "Inconsolata", sans-serif;
+	font-size: 16px;
+}
+
+h1,
+h1 code {
+	font-weight: 400;
+	line-height: calc(2.5 / var(--space) * var(--vspace));
+}
+
+h1 code {
+	background: none;
+	border: none;
+	letter-spacing: 0.05em;
+	padding-bottom: 5px;
+	position: relative;
+	padding: 0;
+}
+
+h2 {
+	margin: var(--vspace-1) 0 var(--vspace-2) 0;
+	line-height: 1em;
+}
+
+h3,
+h3 code {
+	margin: var(--vspace-1) 0 var(--vspace-2) 0;
+	line-height: 1em;
+}
+
+h4,
+h5,
+h6 {
+	margin: var(--vspace-3) 0 var(--vspace-3) 0;
+	line-height: var(--vspace);
+}
+
+.bigtitle,
+h1,
+h1 code {
+	font-size: calc(8px * 4.5);
+	word-break: break-word;
+}
+
+.title,
+h2,
+h2 code {
+	font-size: calc(8px * 3.375);
+	font-weight: lighter;
+	word-break: break-word;
+	border: none;
+	background: none;
+}
+
+.subheading1,
+h3,
+h3 code {
+	font-size: calc(8px * 1.8);
+	font-weight: 600;
+	border: none;
+	background: none;
+	letter-spacing: 0.1em;
+	text-transform: uppercase;
+}
+
+h2 code {
+	padding: 0;
+	position: relative;
+	letter-spacing: 0.05em;
+}
+
+blockquote {
+	font-size: calc(8px * 1.1667);
+	font-style: italic;
+	line-height: calc(1.1667 * var(--vspace));
+	margin: var(--vspace-2) var(--vspace-2);
+}
+
+.subheading2,
+h4 {
+	font-size: calc(8px * 1.4292);
+	text-transform: uppercase;
+	font-weight: 600;
+}
+
+.subheading3,
+h5 {
+	font-size: calc(8px * 1.2917);
+	line-height: calc(1.2917 * var(--vspace));
+
+	font-weight: lighter;
+	text-transform: uppercase;
+	letter-spacing: 0.15em;
+}
+
+h6 {
+	font-size: calc(8px * 1.1667);
+	font-size: 1.1667em;
+	font-weight: normal;
+	font-style: italic;
+	font-family: "le-monde-livre-classic-byol", serif !important;
+	letter-spacing: 0px !important;
+}
+
+#start .md > *:first-child {
+	margin-top: 0;
+}
+
+h2 + h3 {
+	margin-top: 0;
+}
+
+.md hr {
+	border: none;
+	border-top: 1px solid var(--block-border-color);
+	margin: var(--vspace-2) 0 var(--vspace-2) 0;
+}
+.prose ul {
+	margin: var(--vspace-2) 0 var(--vspace-1) 0;
+}
+
+.gap {
+	gap: 0;
+}

src/demo/requirements.txt

@@ -0,0 +1 @@
+gradio_overlay_video

src/demo/space.py

@@ -0,0 +1,206 @@
+
+import gradio as gr
+from app import demo as app
+import os
+
+_docs = {'OverlayVideo': {'description': 'An output component that plays a video with an interactive, toggleable overlay of pose data.', 'members': {'__init__': {'value': {'type': 'typing.Any', 'default': 'None', 'description': None}, 'label': {'type': 'str | None', 'default': 'None', 'description': None}, 'interactive': {'type': 'bool | None', 'default': 'None', 'description': None}, 'autoplay': {'type': 'bool', 'default': 'False', 'description': None}, 'loop': {'type': 'bool', 'default': 'False', 'description': None}, 'mode': {'type': 'str', 'default': '"overlay"', 'description': None}}, 'postprocess': {'value': {'type': 'typing.Optional[typing.Tuple[str | None, str | None]][\n    typing.Tuple[str | None, str | None][\n        str | None, str | None\n    ],\n    None,\n]', 'description': None}}, 'preprocess': {'return': {'type': 'str | None', 'description': None}, 'value': None}}, 'events': {'change': {'type': None, 'default': None, 'description': 'Triggered when the value of the OverlayVideo changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.'}, 'clear': {'type': None, 'default': None, 'description': 'This listener is triggered when the user clears the OverlayVideo using the clear button for the component.'}, 'play': {'type': None, 'default': None, 'description': 'This listener is triggered when the user plays the media in the OverlayVideo.'}, 'pause': {'type': None, 'default': None, 'description': 'This listener is triggered when the media in the OverlayVideo stops for any reason.'}, 'end': {'type': None, 'default': None, 'description': 'This listener is triggered when the user reaches the end of the media playing in the OverlayVideo.'}}}, '__meta__': {'additional_interfaces': {}, 'user_fn_refs': {'OverlayVideo': []}}}
+
+abs_path = os.path.join(os.path.dirname(__file__), "css.css")
+
+with gr.Blocks(
+    css=abs_path,
+    theme=gr.themes.Default(
+        font_mono=[
+            gr.themes.GoogleFont("Inconsolata"),
+            "monospace",
+        ],
+    ),
+) as demo:
+    gr.Markdown(
+"""
+# `gradio_overlay_video`
+
+<div style="display: flex; gap: 7px;">
+<img alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.0.8%20-%20orange">  
+</div>
+
+overlayed video controller
+""", elem_classes=["md-custom"], header_links=True)
+    app.render()
+    gr.Markdown(
+"""
+## Installation
+
+```bash
+pip install gradio_overlay_video
+```
+
+## Usage
+
+```python
+
+import gradio as gr
+from gradio_overlay_video import OverlayVideo
+from pathlib import Path
+
+DEMO_DIR = Path(__file__).parent.parent
+sample_video_path = DEMO_DIR / "files/balette.mp4"
+sample_json_path = DEMO_DIR / "files/mediapipe_full_kp_balette.json"
+mediapipe_json_path = DEMO_DIR / "files/mediapipe_heavy_kp_parkour.json"
+movenet_json_path = DEMO_DIR / "files/movenet_thunder_kp_skate.json"
+yolo8_json_path = DEMO_DIR / "files/yolov8_kp_dance.json"
+yolo11_json_path = DEMO_DIR / "files/yolov11.json"
+
+def prepare_visualization_data(json_path, video_path):
+    \"\"\"
+    This function simply validates the inputs and passes them to the
+    custom OverlayVideo component for frontend processing.
+    \"\"\"
+    if not json_path:
+        raise gr.Error("A JSON file is required to generate a visualization.")
+
+    print(f"✅ Preparing visualization with JSON: {json_path}")
+    if video_path:
+        print(f"✅ Video background provided: {video_path}")
+    else:
+        print("ℹ️ No video background provided. Visualization will be on a black background.")
+
+    # The backend's job is just to pass the filepaths to the frontend.
+    # The return format (video_path, json_path) must match what postprocess expects.
+    return (video_path, json_path)
+
+
+with gr.Blocks(theme=gr.themes.Default(primary_hue="rose", secondary_hue="pink")) as demo:
+    gr.Markdown(
+        "# 🩰 Interactive Pose Visualization\n"
+        "1. **Upload a JSON file** with pose data.\n"
+        "2. **(Optional) Upload a video** to use as the background.\n"
+        "3. Click 'Display Visualization' to see the interactive result."
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Use standard gr.File for robust input handling
+            json_upload = gr.File(
+                label="Upload Required JSON File",
+                file_types=[".json"],
+                type="filepath"
+            )
+            video_upload = gr.File(
+                label="Upload Optional Video File",
+                file_types=["video"],
+                type="filepath",
+                value=None 
+            )
+            btn = gr.Button("Display Visualization", variant="primary")
+        
+        with gr.Column(scale=1):
+            output_ov = OverlayVideo(label="Output", interactive=False, autoplay=True)
+
+    btn.click(
+        fn=prepare_visualization_data,
+        inputs=[json_upload, video_upload],
+        outputs=[output_ov]
+    )
+    
+    gr.Examples(
+        examples=[
+            [str(mediapipe_json_path), None],
+            [str(movenet_json_path), None],
+            [str(yolo8_json_path), None],
+            [str(sample_json_path), str(sample_video_path)],
+            [str(yolo11_json_path), None]
+        ],
+        inputs=[json_upload, video_upload],
+        outputs=output_ov,
+        fn=prepare_visualization_data,
+        cache_examples=True
+    )
+
+if __name__ == "__main__":
+    demo.launch(allowed_paths=["/Users/csabi/Develop/overlay_video/files"])
+```
+""", elem_classes=["md-custom"], header_links=True)
+
+
+    gr.Markdown("""
+## `OverlayVideo`
+
+### Initialization
+""", elem_classes=["md-custom"], header_links=True)
+
+    gr.ParamViewer(value=_docs["OverlayVideo"]["members"]["__init__"], linkify=[])
+
+
+    gr.Markdown("### Events")
+    gr.ParamViewer(value=_docs["OverlayVideo"]["events"], linkify=['Event'])
+
+
+
+
+    gr.Markdown("""
+
+### User function
+
+The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).
+
+- When used as an Input, the component only impacts the input signature of the user function.
+- When used as an output, the component only impacts the return signature of the user function.
+
+The code snippet below is accurate in cases where the component is used as both an input and an output.
+
+
+
+ ```python
+def predict(
+    value: str | None
+) -> typing.Optional[typing.Tuple[str | None, str | None]][
+    typing.Tuple[str | None, str | None][
+        str | None, str | None
+    ],
+    None,
+]:
+    return value
+```
+""", elem_classes=["md-custom", "OverlayVideo-user-fn"], header_links=True)
+
+
+
+
+    demo.load(None, js=r"""function() {
+    const refs = {};
+    const user_fn_refs = {
+          OverlayVideo: [], };
+    requestAnimationFrame(() => {
+
+        Object.entries(user_fn_refs).forEach(([key, refs]) => {
+            if (refs.length > 0) {
+                const el = document.querySelector(`.${key}-user-fn`);
+                if (!el) return;
+                refs.forEach(ref => {
+                    el.innerHTML = el.innerHTML.replace(
+                        new RegExp("\\b"+ref+"\\b", "g"),
+                        `<a href="#h-${ref.toLowerCase()}">${ref}</a>`
+                    );
+                })
+            }
+        })
+
+        Object.entries(refs).forEach(([key, refs]) => {
+            if (refs.length > 0) {
+                const el = document.querySelector(`.${key}`);
+                if (!el) return;
+                refs.forEach(ref => {
+                    el.innerHTML = el.innerHTML.replace(
+                        new RegExp("\\b"+ref+"\\b", "g"),
+                        `<a href="#h-${ref.toLowerCase()}">${ref}</a>`
+                    );
+                })
+            }
+        })
+    })
+}
+
+""")
+
+demo.launch()

src/files/balette.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:75e5ff7c8e5eb31b54686b0250a1ebc739ea1128bb18f116f8f0215159e0979b
+size 621415

src/frontend/Example.svelte

@@ -0,0 +1,79 @@
+<script lang="ts">
+	import Video from "./shared/Video.svelte";
+	import { playable } from "./shared/utils";
+	import { type FileData } from "@gradio/client";
+
+	export let type: "gallery" | "table";
+	export let selected = false;
+	export let value: { video: FileData; subtitles: FileData | null } | null;
+	export let loop: boolean;
+	let video: HTMLVideoElement;
+
+	async function init(): Promise<void> {
+		video.muted = true;
+		video.playsInline = true;
+		video.controls = false;
+		video.setAttribute("muted", "");
+
+		await video.play();
+		video.pause();
+	}
+</script>
+
+{#if value}
+	{#if playable()}
+		<div
+			class="container"
+			class:table={type === "table"}
+			class:gallery={type === "gallery"}
+			class:selected
+		>
+			<Video
+				muted
+				playsinline
+				bind:node={video}
+				on:loadeddata={init}
+				on:mouseover={video.play.bind(video)}
+				on:mouseout={video.pause.bind(video)}
+				src={value?.video.url}
+				is_stream={false}
+				{loop}
+			/>
+			
+		</div>
+	{:else}
+		<div>{value}</div>
+	{/if}
+{/if}
+
+<style>
+	.container {
+		flex: none;
+		max-width: none;
+	}
+	.container :global(video) {
+		width: var(--size-full);
+		height: var(--size-full);
+		object-fit: cover;
+	}
+
+	.container:hover,
+	.container.selected {
+		border-color: var(--border-color-accent);
+	}
+	.container.table {
+		margin: 0 auto;
+		border: 2px solid var(--border-color-primary);
+		border-radius: var(--radius-lg);
+		overflow: hidden;
+		width: var(--size-20);
+		height: var(--size-20);
+		object-fit: cover;
+	}
+
+	.container.gallery {
+		height: var(--size-20);
+		max-height: var(--size-20);
+		object-fit: cover;
+	}
+</style>

src/frontend/Index.svelte

@@ -0,0 +1,41 @@
+<svelte:options accessors={true} />
+
+<script lang="ts">
+	import type { Gradio } from "@gradio/utils";
+	import type { FileData } from "@gradio/client";
+	import { Block } from "@gradio/atoms";
+	import { StatusTracker } from "@gradio/statustracker";
+	import type { LoadingStatus } from "@gradio/statustracker";
+	import OverlayPlayer from "./shared/OverlayPlayer.svelte";
+
+	export let value: { video: { file: FileData } | null; json_data: string | null } | null = null;
+	export let gradio: Gradio;
+    // Unused props can be kept for Gradio compatibility, the warnings are harmless
+    export let elem_id = "", elem_classes = [], visible = true, loading_status: LoadingStatus, height, width, container, scale, min_width;
+	export let autoplay = false, loop = false;
+
+	let video_url: string | null | undefined;
+	let json_content: string | null | undefined;
+
+	$: {
+		if (value) {
+			video_url = value.video?.url;
+			json_content = value.json_data;
+		} else {
+			video_url = null;
+			json_content = null;
+		}
+	}
+</script>
+
+<Block {visible} {elem_id} {elem_classes} {height} {width} {container} {scale} {min_width} allow_overflow={false}>
+	<StatusTracker {...loading_status} autoscroll={gradio.autoscroll} i18n={gradio.i18n} />
+	{#if json_content}
+		<OverlayPlayer
+			video_src={video_url}
+			json_data_str={json_content}
+			{autoplay}
+			{loop}
+		/>
+	{/if}
+</Block>

src/frontend/gradio.config.js

@@ -0,0 +1,9 @@
+export default {
+  plugins: [],
+  svelte: {
+    preprocess: [],
+  },
+  build: {
+    target: "modules",
+  },
+};

src/frontend/index.ts

@@ -0,0 +1,3 @@
+export { default as BaseExample } from "./Example.svelte";
+import { default as Index } from "./Index.svelte";
+export default Index;