app_ld..py

from mcp.server.fastmcp import FastMCP
from gradio_client import Client
import sys
import io
import json 
import gradio as gr
from huggingface_hub import InferenceClient
mcp = FastMCP("gradio-spaces")

clients = {}

def get_client(space_id: str) -> Client:
    """Get or create a Gradio client for the specified space."""
    if space_id not in clients:
        clients[space_id] = Client(space_id)
    return clients[space_id]


@mcp.tool()
async def generate_image(prompt: str, space_id: str = "inoculatemedia/SanaSprint") -> str:
    """Generate an image using Flux.
    
    Args:
        prompt: Text prompt describing the image to generate
        space_id: inoculatemedia/FramePack-F1
    """
    client = get_client(space_id)
    result = client.predict(
            prompt=prompt,
            model_size="1.6B",
            seed=0,
            randomize_seed=True,
            width=1024,
            height=1024,
            guidance_scale=4.5,
            num_inference_steps=2,
            api_name="/infer"
    )
    return result