Create gradio_mcp_server.py

gradio_mcp_server.py

@@ -0,0 +1,58 @@
+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://ysharma-sanasprint.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')