import gradio as gr
from app import demo as app
import os
_docs = {'LabanMovementAnalysis': {'description': 'Gradio component for video-based pose analysis with Laban Movement Analysis metrics.', 'members': {'__init__': {'default_model': {'type': 'str', 'default': '"mediapipe"', 'description': 'Default pose estimation model ("mediapipe", "movenet", "yolo")'}, 'enable_visualization': {'type': 'bool', 'default': 'True', 'description': 'Whether to generate visualization video by default'}, 'include_keypoints': {'type': 'bool', 'default': 'False', 'description': 'Whether to include raw keypoints in JSON output'}, 'enable_webrtc': {'type': 'bool', 'default': 'False', 'description': 'Whether to enable WebRTC real-time analysis'}, 'label': {'type': 'typing.Optional[str][str, None]', 'default': 'None', 'description': 'Component label'}, 'every': {'type': 'typing.Optional[float][float, None]', 'default': 'None', 'description': None}, 'show_label': {'type': 'typing.Optional[bool][bool, None]', 'default': 'None', 'description': None}, 'container': {'type': 'bool', 'default': 'True', 'description': None}, 'scale': {'type': 'typing.Optional[int][int, None]', 'default': 'None', 'description': None}, 'min_width': {'type': 'int', 'default': '160', 'description': None}, 'interactive': {'type': 'typing.Optional[bool][bool, None]', 'default': 'None', 'description': None}, 'visible': {'type': 'bool', 'default': 'True', 'description': None}, 'elem_id': {'type': 'typing.Optional[str][str, None]', 'default': 'None', 'description': None}, 'elem_classes': {'type': 'typing.Optional[typing.List[str]][\n typing.List[str][str], None\n]', 'default': 'None', 'description': None}, 'render': {'type': 'bool', 'default': 'True', 'description': None}}, 'postprocess': {'value': {'type': 'typing.Any', 'description': 'Analysis results'}}, 'preprocess': {'return': {'type': 'typing.Dict[str, typing.Any][str, typing.Any]', 'description': 'Processed data for analysis'}, 'value': None}}, 'events': {}}, '__meta__': {'additional_interfaces': {}, 'user_fn_refs': {'LabanMovementAnalysis': []}}}
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_labanmovementanalysis`
A Gradio 5 component for video movement analysis using Laban Movement Analysis (LMA) with MCP support for AI agents
""", elem_classes=["md-custom"], header_links=True)
app.render()
gr.Markdown(
"""
## Installation
```bash
pip install gradio_labanmovementanalysis
```
## Usage
```python
# app.py ─────────────────────────────────────────────────────────
\"\"\"
Laban Movement Analysis – modernised Gradio Space
Author: Csaba (BladeSzaSza)
\"\"\"
import gradio as gr
import os
# from backend.gradio_labanmovementanalysis import LabanMovementAnalysis
from gradio_labanmovementanalysis import LabanMovementAnalysis
# Import agent API if available
# Initialize agent API if available
agent_api = None
try:
from gradio_labanmovementanalysis.agent_api import (
LabanAgentAPI,
PoseModel,
MovementDirection,
MovementIntensity
)
agent_api = LabanAgentAPI()
except Exception as e:
print(f"Warning: Agent API not available: {e}")
agent_api = None
# Initialize components
try:
analyzer = LabanMovementAnalysis(
enable_visualization=True
)
print("✅ Core features initialized successfully")
except Exception as e:
print(f"Warning: Some features may not be available: {e}")
analyzer = LabanMovementAnalysis()
def process_video_enhanced(video_input, model, enable_viz, include_keypoints):
\"\"\"Enhanced video processing with all new features.\"\"\"
if not video_input:
return {"error": "No video provided"}, None
try:
# Handle both file upload and URL input
video_path = video_input.name if hasattr(video_input, 'name') else video_input
json_result, viz_result = analyzer.process_video(
video_path,
model=model,
enable_visualization=enable_viz,
include_keypoints=include_keypoints
)
return json_result, viz_result
except Exception as e:
error_result = {"error": str(e)}
return error_result, None
def process_video_standard(video : str, model : str, include_keypoints : bool) -> dict:
\"\"\"
Processes a video file using the specified pose estimation model and returns movement analysis results.
Args:
video (str): Path to the video file to be analyzed.
model (str): The name of the pose estimation model to use (e.g., "mediapipe-full", "movenet-thunder", etc.).
include_keypoints (bool): Whether to include raw keypoint data in the output.
Returns:
dict:
- A dictionary containing the movement analysis results in JSON format, or an error message if processing fails.
Notes:
- Visualization is disabled in this standard processing function.
- If the input video is None, both return values will be None.
- If an error occurs during processing, the first return value will be a dictionary with an "error" key.
\"\"\"
if video is None:
return None
try:
json_output, _ = analyzer.process_video(
video,
model=model,
enable_visualization=False,
include_keypoints=include_keypoints
)
return json_output
except (RuntimeError, ValueError, OSError) as e:
return {"error": str(e)}
# ── 4. Build UI ─────────────────────────────────────────────────
def create_demo() -> gr.Blocks:
with gr.Blocks(
title="Laban Movement Analysis",
theme='gstaff/sketch',
fill_width=True,
) as demo:
gr.api(process_video_standard, api_name="process_video")
# ── Hero banner ──
gr.Markdown(
\"\"\"
# 🎭 Laban Movement Analysis
Pose estimation • AI action recognition • Movement Analysis
\"\"\"
)
with gr.Tabs():
# Tab 1: Standard Analysis
with gr.Tab("🎬 Standard Analysis"):
gr.Markdown(\"\"\"
### Upload a video file to analyze movement using traditional LMA metrics with pose estimation.
\"\"\")
# ── Workspace ──
with gr.Row(equal_height=True):
# Input column
with gr.Column(scale=1, min_width=260):
analyze_btn_enh = gr.Button("🚀 Analyze Movement", variant="primary", size="lg")
video_in = gr.Video(label="Upload Video", sources=["upload"], format="mp4")
# URL input option
url_input_enh = gr.Textbox(
label="Or Enter Video URL",
placeholder="YouTube URL, Vimeo URL, or direct video URL",
info="Leave file upload empty to use URL"
)
gr.Markdown("**Model Selection**")
model_sel = gr.Dropdown(
choices=[
# MediaPipe variants
"mediapipe-lite", "mediapipe-full", "mediapipe-heavy",
# MoveNet variants
"movenet-lightning", "movenet-thunder",
# YOLO v8 variants
"yolo-v8-n", "yolo-v8-s", "yolo-v8-m", "yolo-v8-l", "yolo-v8-x",
# YOLO v11 variants
"yolo-v11-n", "yolo-v11-s", "yolo-v11-m", "yolo-v11-l", "yolo-v11-x"
],
value="mediapipe-full",
label="Advanced Pose Models",
info="15 model variants available"
)
with gr.Accordion("Analysis Options", open=False):
enable_viz = gr.Radio([("Yes", 1), ("No", 0)], value=1, label="Visualization")
include_kp = gr.Radio([("Yes", 1), ("No", 0)], value=0, label="Raw Keypoints")
gr.Examples(
examples=[
["examples/balette.mp4"],
["https://www.youtube.com/shorts/RX9kH2l3L8U"],
["https://vimeo.com/815392738"],
["https://vimeo.com/548964931"],
["https://videos.pexels.com/video-files/5319339/5319339-uhd_1440_2560_25fps.mp4"],
],
inputs=url_input_enh,
label="Examples"
)
# Output column
with gr.Column(scale=2, min_width=320):
viz_out = gr.Video(label="Annotated Video", scale=1, height=400)
with gr.Accordion("Raw JSON", open=True):
json_out = gr.JSON(label="Movement Analysis", elem_classes=["json-output"])
# Wiring
def process_enhanced_input(file_input, url_input, model, enable_viz, include_keypoints):
\"\"\"Process either file upload or URL input.\"\"\"
video_source = file_input if file_input else url_input
return process_video_enhanced(video_source, model, enable_viz, include_keypoints)
analyze_btn_enh.click(
fn=process_enhanced_input,
inputs=[video_in, url_input_enh, model_sel, enable_viz, include_kp],
outputs=[json_out, viz_out],
api_name="analyze_enhanced"
)
# Footer
with gr.Row():
gr.Markdown(
\"\"\"
**Built by Csaba Bolyós**
[GitHub](https://github.com/bladeszasza) • [HF](https://huggingface.co/BladeSzaSza)
\"\"\"
)
return demo
if __name__ == "__main__":
demo = create_demo()
demo.launch(server_name="0.0.0.0",
share=True,
server_port=int(os.getenv("PORT", 7860)),
mcp_server=True)
```
""", elem_classes=["md-custom"], header_links=True)
gr.Markdown("""
## `LabanMovementAnalysis`
### Initialization
""", elem_classes=["md-custom"], header_links=True)
gr.ParamViewer(value=_docs["LabanMovementAnalysis"]["members"]["__init__"], linkify=[])
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.
- **As input:** Is passed, processed data for analysis.
- **As output:** Should return, analysis results.
```python
def predict(
value: typing.Dict[str, typing.Any][str, typing.Any]
) -> typing.Any:
return value
```
""", elem_classes=["md-custom", "LabanMovementAnalysis-user-fn"], header_links=True)
demo.load(None, js=r"""function() {
const refs = {};
const user_fn_refs = {
LabanMovementAnalysis: [], };
requestAnimationFrame(() => {
Object.entries(user_fn_refs).forEach(([key, refs]) => {
if (refs.length > 0) {
const el = document.querySelector(`.${key}-user-fn`);
if (!el) return;
refs.forEach(ref => {
el.innerHTML = el.innerHTML.replace(
new RegExp("\\b"+ref+"\\b", "g"),
`${ref}`
);
})
}
})
Object.entries(refs).forEach(([key, refs]) => {
if (refs.length > 0) {
const el = document.querySelector(`.${key}`);
if (!el) return;
refs.forEach(ref => {
el.innerHTML = el.innerHTML.replace(
new RegExp("\\b"+ref+"\\b", "g"),
`${ref}`
);
})
}
})
})
}
""")
demo.launch()
