Rename gradio_mcp_server.py to app.py

gradio_mcp_server.py → app.py

@@ -39,6 +39,47 @@ async def generate_image(prompt: str, space_id: str = "ysharma/SanaSprint") -> s
 
 
 @mcp.tool()
+
+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/SanaSprint
+    """
+    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
+
 async def run_dia_tts(prompt: str, space_id: str = "ysharma/Dia-1.6B") -> str:
     """Text-to-Speech Synthesis.
     
@@ -65,5 +106,11 @@ if __name__ == "__main__":
     import sys
     import io
     sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
-    
-    mcp.run(transport='stdio')
+    gr.on(
+    triggers=[run_button.click, prompt.submit],
+    fn = infer,
+    inputs = [prompt, model_size, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],  # Add model_size to inputs
+    outputs = [result, seed]
+    )
+
+   mcp.run(transport='stdio')