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const MCP_SERVER_URL = 'https://black-forest-labs-flux-1-kontext-dev.hf.space/run/FLUX_1_Kontext_Dev_infer';
/**
* Edits a bonsai image using the FLUX.1 Kontext MCP tool.
* @param imageBase64 The base64 encoded string of the input image.
* @param prompt The text prompt describing the desired edit.
* @returns A base64 encoded string of the edited image, or null if an error occurs.
*/
export const editBonsaiWithKontext = async (
imageBase64: string,
prompt: string
): Promise<string | null> => {
try {
const inputImage = `data:image/jpeg;base64,${imageBase64}`;
// Based on the provided documentation and common Gradio API structure.
// The payload is an object with a 'data' array containing the arguments in order.
// The order of arguments is assumed from the Python example:
// input_image, prompt, seed, randomize_seed, guidance_scale
const payload = {
data: [
inputImage, // input_image
prompt, // prompt
-1, // seed (-1 for random)
true, // randomize_seed
2.5, // guidance_scale
]
};
const response = await fetch(MCP_SERVER_URL, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify(payload),
});
if (!response.ok) {
const errorBody = await response.text();
console.error("MCP Server Error:", response.status, errorBody);
throw new Error(`Request failed with status ${response.status}`);
}
const result = await response.json();
// Gradio API responses typically wrap the output in a 'data' array.
// The edited image is expected to be the first element.
if (result && Array.isArray(result.data) && result.data.length > 0) {
const outputImage = result.data[0];
// The output is often a data URI. We need to extract the base64 part.
if (typeof outputImage === 'string' && outputImage.startsWith('data:image/')) {
return outputImage.split(',')[1];
}
}
console.error("Invalid response format from MCP server:", result);
return null;
} catch (error) {
console.error("Error calling MCP tool:", error);
return null;
}
};
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