Create gradio_mcp_server.py

gradio_mcp_server.py

@@ -0,0 +1,69 @@
+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')