CaBuAr Wildfire Spread Prediction
by amar-atoumco.huggingface.amar-atoum/cabuar-pipeline
Latent diffusion for post-fire burn scar prediction
- Remote endpoint
- 2 tools
- MIT
Uptime
Checked · every day
100%from 4 days of checks
Up without downtime on 4 of 4 days with data.
- No downtime
- Degraded
- Down over 5 minutes
- No data
- Not checked yet
Response time
Median 1069 ms, slowest 5% 1272 ms for the handshake and the tool list, over 30 days.
How far each check got
- Reached the address 100%
- Completed the handshake 100%
- Listed its tools 100%
2 tools at the last answer.
Every day
| Day | Result | Checks | Median |
|---|---|---|---|
| 3 Nov (today) | No downtime | 1 | 1167 ms |
| 2 Nov | No downtime | 1of 1 | 1239 ms |
| 1 Nov | No downtime | 1of 1 | 1120 ms |
| 31 Oct | No downtime | 1 | 1069 ms |
A failure counts as down time only when a second check five minutes later fails too. How we check
Connect to the publisher's own endpoint
Paste into Settings › Connectors › Add custom connector:
https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse Run in a terminal:
claude mcp add --transport sse cabuar-pipeline https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse Add to ~/.cursor/mcp.json:
Add to ~/.config/devin/mcp_config.json:
Add to mcp.json (Program › Install › Edit mcp.json):
Add to ~/.gemini/settings.json:
Add to config.json in oterm's data folder (~/Library/Application Support/oterm on macOS, ~/.local/share/oterm on Linux, %APPDATA%\oterm on Windows):
Add to ~/.config/eca/config.json (global) or .eca/config.json in the project:
{
"mcpServers": {
"cabuar-pipeline": {
"url": "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
}
}
} Add to .vscode/mcp.json:
{
"servers": {
"cabuar-pipeline": {
"type": "sse",
"url": "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
}
}
} Add to ~/.config/zed/settings.json:
{
"context_servers": {
"cabuar-pipeline": {
"url": "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
}
}
} This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.
Add to cline_mcp_settings.json (MCP Servers › Configure › Configure MCP Servers):
Add to anythingllm_mcp_servers.json in the AnythingLLM storage plugins directory:
Add to Augment Settings Panel – MCP section – Import from JSON (VS Code extension):
{
"mcpServers": {
"cabuar-pipeline": {
"type": "sse",
"url": "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
}
}
} Add to ~/.continue/config.yaml:
mcpServers:
- name: "cabuar-pipeline"
type: sse
url: "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse" Add to ~/.config/goose/config.yaml:
extensions:
"cabuar-pipeline":
type: streamable_http
name: "cabuar-pipeline"
enabled: true
uri: "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
headers: {}
env_keys: []
envs: {}
timeout: 300 This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.
Run in a terminal:
codex mcp add cabuar-pipeline --url https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.
Add to ~/.llm/config.json:
Add to resources/assets/config/mcp.json in the installed app:
Add to ~/.boltai/mcp.json:
Add to ~/.bob/settings/mcp.json (global) or .bob/mcp.json (project):
Add to ~/.kiro/settings/mcp.json (user) or .kiro/settings/mcp.json (workspace):
Add to ~/.kiro/settings/mcp.json (user) or .kiro/settings/mcp.json (workspace):
{
"mcpServers": {
"cabuar-pipeline": {
"url": "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
}
}
} This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.
Add to ~/.config/agenvoy/mcp.json:
{
"servers": {
"cabuar-pipeline": {
"url": "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
}
}
} This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.
Add to ~/.config/amp/settings.json (user) or .amp/settings.json (workspace):
{
"amp.mcpServers": {
"cabuar-pipeline": {
"url": "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
}
}
} For agents: the install prompt
Every step above as one Markdown text, for an assistant installing it for you.
# Install the MCP server CaBuAr Wildfire Spread Prediction (`co.huggingface.amar-atoum/cabuar-pipeline`)
Its page: https://plug.directory/servers/co.huggingface.amar-atoum/cabuar-pipeline
It runs on the publisher's servers; the client connects to its URL.
Use the section for the person's client.
## Claude Desktop
Paste into Settings › Connectors › Add custom connector:
```text
https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse
```
## Claude Code
Run in a terminal:
```sh
claude mcp add --transport sse cabuar-pipeline https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse
```
## Cursor, Devin Desktop, LM Studio, Gemini CLI, oterm, ECA
Merge into the place for the person's client:
- Cursor: ~/.cursor/mcp.json
- Devin Desktop: ~/.config/devin/mcp_config.json
- LM Studio: mcp.json (Program › Install › Edit mcp.json)
- Gemini CLI: ~/.gemini/settings.json
- oterm: config.json in oterm's data folder (~/Library/Application Support/oterm on macOS, ~/.local/share/oterm on Linux, %APPDATA%\oterm on Windows)
- ECA: ~/.config/eca/config.json (global) or .eca/config.json in the project
```json
{
"mcpServers": {
"cabuar-pipeline": {
"url": "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
}
}
}
```
## VS Code
Merge into .vscode/mcp.json:
```json
{
"servers": {
"cabuar-pipeline": {
"type": "sse",
"url": "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
}
}
}
```
## Zed
This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.
## Cline, AnythingLLM, Augment Code
Merge into the place for the person's client:
- Cline: cline_mcp_settings.json (MCP Servers › Configure › Configure MCP Servers)
- AnythingLLM: anythingllm_mcp_servers.json in the AnythingLLM storage plugins directory
- Augment Code: Augment Settings Panel – MCP section – Import from JSON (VS Code extension)
```json
{
"mcpServers": {
"cabuar-pipeline": {
"type": "sse",
"url": "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
}
}
}
```
## Continue
Merge into ~/.continue/config.yaml:
```yaml
mcpServers:
- name: "cabuar-pipeline"
type: sse
url: "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
```
## Goose
This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.
## Codex
This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.
## mcp-client-cli, TUUI, BoltAI, Bob Shell, Kiro, Kiro CLI
This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.
## Agenvoy
This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.
## Amp
Merge into ~/.config/amp/settings.json (user) or .amp/settings.json (workspace):
```json
{
"amp.mcpServers": {
"cabuar-pipeline": {
"url": "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
}
}
}
```
Then restart the client, or reload its MCP servers, and ask it to list the server's tools to confirm it started.
Tools 2
CaBuAr_Pipeline_predict_on_cabuar_test_sampleRun the CaBuAr V4 burn-scar projection on a held-out test sample of the CaBuAr ChaBuD split and score it against the ground truth: SSIM, pixel MSE and PSNR against the true post-fire image, and burn-scar IoU, F1, precision and recall against the CaBuAr ground-truth burned-area mask (ΔNBR > 0.05). Returns: Tuple of (pre_fire_false_colour_image, predicted_post_fire_false_colour_image, comparison_image with the true post-fire scene and the predicted burn scar against the ground-truth mask, metrics_markdown).CaBuAr_Pipeline_predict_post_fire_from_pre_fire_npyProject the post-fire Sentinel-2 scene and the burn scar from a pre-fire Sentinel-2 image of an area in which a fire later occurs, using the CaBuAr V4 latent diffusion model. The burn scar is derived with the Normalized Burn Ratio difference, ΔNBR = NBR(pre-fire) - NBR(predicted post-fire), and a pixel counts as burned when ΔNBR > 0.05. The model does not predict whether or where a fire will ignite; it assumes a fire occurs in the scene. If a user passes a file as an input, use the upload_file_to_gradio tool, if present, to upload the file to the gradio app and create a Gradio File Input. Then use the returned path as the input to the tool. Returns: Tuple of (pre_fire_false_colour_image, predicted_post_fire_false_colour_image, delta_nbr_map_image, summary_markdown with the predicted burned fraction).
From Hugging Face Spaces. Report a problem