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from mcp.server.fastmcp import FastMCP
from gradio_client import Client
import sys
import io
import json
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 = "ysharma/SanaSprint") -> str:
"""Generate an image using Flux.
Args:
prompt: Text prompt describing the image to generate
space_id: HuggingFace Space ID to use
"""
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
@mcp.tool()
async def run_dia_tts(prompt: str, space_id: str = "ysharma/Dia-1.6B") -> str:
"""Text-to-Speech Synthesis.
Args:
prompt: Text prompt describing the conversation between speakers S1, S2
space_id: HuggingFace Space ID to use
"""
client = get_client(space_id)
result = client.predict(
text_input=f"""{prompt}""",
audio_prompt_input=None,
max_new_tokens=3072,
cfg_scale=3,
temperature=1.3,
top_p=0.95,
cfg_filter_top_k=30,
speed_factor=0.94,
api_name="/generate_audio"
)
return result
if __name__ == "__main__":
import sys
import io
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
mcp.run(transport='stdio') |