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File size: 1,687 Bytes
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from mcp.server.fastmcp import FastMCP
import json
import sys
import io
import time
from gradio_client import Client
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')
sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8', errors='replace')
mcp = FastMCP("huggingface_spaces_image_display")
@mcp.tool()
async def generate_image(prompt: str, width: int = 512, height: int = 512) -> str:
"""Generate an image using SanaSprint model.
Args:
prompt: Text prompt describing the image to generate
width: Image width (default: 512)
height: Image height (default: 512)
"""
client = Client("https://black-forest-labs-flux-1-schnell.hf.space/")
try:
result = client.predict(
prompt,
"0.6B",
0,
True,
width,
height,
4.0,
2,
api_name="/infer"
)
if isinstance(result, list) and len(result) >= 1:
image_data = result[0]
if isinstance(image_data, dict) and "url" in image_data:
return json.dumps({
"type": "image",
"url": image_data["url"],
"message": f"Generated image for prompt: {prompt}"
})
return json.dumps({
"type": "error",
"message": "Failed to generate image"
})
except Exception as e:
return json.dumps({
"type": "error",
"message": f"Error generating image: {str(e)}"
})
if __name__ == "__main__":
mcp.run(transport='stdio') |