Setup from HF guide

Signed-off-by: Aivin V. Solatorio <avsolatorio@gmail.com>

gradio_mcp_server.py

@@ -0,0 +1,50 @@
+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")

mcp_client.py

@@ -0,0 +1,280 @@
+import asyncio
+import os
+import json
+from typing import List, Dict, Any, Union
+from contextlib import AsyncExitStack
+
+import gradio as gr
+from gradio.components.chatbot import ChatMessage
+from mcp import ClientSession, StdioServerParameters
+from mcp.client.stdio import stdio_client
+from anthropic import Anthropic
+from dotenv import load_dotenv
+
+load_dotenv()
+
+loop = asyncio.new_event_loop()
+asyncio.set_event_loop(loop)
+
+
+class MCPClientWrapper:
+    def __init__(self):
+        self.session = None
+        self.exit_stack = None
+        self.anthropic = Anthropic()
+        self.tools = []
+
+    def connect(self, server_path: str) -> str:
+        return loop.run_until_complete(self._connect(server_path))
+
+    async def _connect(self, server_path: str) -> str:
+        if self.exit_stack:
+            await self.exit_stack.aclose()
+
+        self.exit_stack = AsyncExitStack()
+
+        is_python = server_path.endswith(".py")
+        command = "python" if is_python else "node"
+
+        server_params = StdioServerParameters(
+            command=command,
+            args=[server_path],
+            env={"PYTHONIOENCODING": "utf-8", "PYTHONUNBUFFERED": "1"},
+        )
+
+        stdio_transport = await self.exit_stack.enter_async_context(
+            stdio_client(server_params)
+        )
+        self.stdio, self.write = stdio_transport
+
+        self.session = await self.exit_stack.enter_async_context(
+            ClientSession(self.stdio, self.write)
+        )
+        await self.session.initialize()
+
+        response = await self.session.list_tools()
+        self.tools = [
+            {
+                "name": tool.name,
+                "description": tool.description,
+                "input_schema": tool.inputSchema,
+            }
+            for tool in response.tools
+        ]
+
+        tool_names = [tool["name"] for tool in self.tools]
+        return f"Connected to MCP server. Available tools: {', '.join(tool_names)}"
+
+    def process_message(
+        self, message: str, history: List[Union[Dict[str, Any], ChatMessage]]
+    ) -> tuple:
+        if not self.session:
+            return history + [
+                {"role": "user", "content": message},
+                {
+                    "role": "assistant",
+                    "content": "Please connect to an MCP server first.",
+                },
+            ], gr.Textbox(value="")
+
+        new_messages = loop.run_until_complete(self._process_query(message, history))
+        return history + [
+            {"role": "user", "content": message}
+        ] + new_messages, gr.Textbox(value="")
+
+    async def _process_query(
+        self, message: str, history: List[Union[Dict[str, Any], ChatMessage]]
+    ):
+        claude_messages = []
+        for msg in history:
+            if isinstance(msg, ChatMessage):
+                role, content = msg.role, msg.content
+            else:
+                role, content = msg.get("role"), msg.get("content")
+
+            if role in ["user", "assistant", "system"]:
+                claude_messages.append({"role": role, "content": content})
+
+        claude_messages.append({"role": "user", "content": message})
+
+        response = self.anthropic.messages.create(
+            model="claude-3-5-sonnet-20241022",
+            max_tokens=1000,
+            messages=claude_messages,
+            tools=self.tools,
+        )
+
+        result_messages = []
+
+        for content in response.content:
+            if content.type == "text":
+                result_messages.append({"role": "assistant", "content": content.text})
+
+            elif content.type == "tool_use":
+                tool_name = content.name
+                tool_args = content.input
+
+                result_messages.append(
+                    {
+                        "role": "assistant",
+                        "content": f"I'll use the {tool_name} tool to help answer your question.",
+                        "metadata": {
+                            "title": f"Using tool: {tool_name}",
+                            "log": f"Parameters: {json.dumps(tool_args, ensure_ascii=True)}",
+                            "status": "pending",
+                            "id": f"tool_call_{tool_name}",
+                        },
+                    }
+                )
+
+                result_messages.append(
+                    {
+                        "role": "assistant",
+                        "content": "```json\n"
+                        + json.dumps(tool_args, indent=2, ensure_ascii=True)
+                        + "\n```",
+                        "metadata": {
+                            "parent_id": f"tool_call_{tool_name}",
+                            "id": f"params_{tool_name}",
+                            "title": "Tool Parameters",
+                        },
+                    }
+                )
+
+                result = await self.session.call_tool(tool_name, tool_args)
+
+                if result_messages and "metadata" in result_messages[-2]:
+                    result_messages[-2]["metadata"]["status"] = "done"
+
+                result_messages.append(
+                    {
+                        "role": "assistant",
+                        "content": "Here are the results from the tool:",
+                        "metadata": {
+                            "title": f"Tool Result for {tool_name}",
+                            "status": "done",
+                            "id": f"result_{tool_name}",
+                        },
+                    }
+                )
+
+                result_content = result.content
+                if isinstance(result_content, list):
+                    result_content = "\n".join(str(item) for item in result_content)
+
+                try:
+                    result_json = json.loads(result_content)
+                    if isinstance(result_json, dict) and "type" in result_json:
+                        if result_json["type"] == "image" and "url" in result_json:
+                            result_messages.append(
+                                {
+                                    "role": "assistant",
+                                    "content": {
+                                        "path": result_json["url"],
+                                        "alt_text": result_json.get(
+                                            "message", "Generated image"
+                                        ),
+                                    },
+                                    "metadata": {
+                                        "parent_id": f"result_{tool_name}",
+                                        "id": f"image_{tool_name}",
+                                        "title": "Generated Image",
+                                    },
+                                }
+                            )
+                        else:
+                            result_messages.append(
+                                {
+                                    "role": "assistant",
+                                    "content": "```\n" + result_content + "\n```",
+                                    "metadata": {
+                                        "parent_id": f"result_{tool_name}",
+                                        "id": f"raw_result_{tool_name}",
+                                        "title": "Raw Output",
+                                    },
+                                }
+                            )
+                except:
+                    result_messages.append(
+                        {
+                            "role": "assistant",
+                            "content": "```\n" + result_content + "\n```",
+                            "metadata": {
+                                "parent_id": f"result_{tool_name}",
+                                "id": f"raw_result_{tool_name}",
+                                "title": "Raw Output",
+                            },
+                        }
+                    )
+
+                claude_messages.append(
+                    {
+                        "role": "user",
+                        "content": f"Tool result for {tool_name}: {result_content}",
+                    }
+                )
+                next_response = self.anthropic.messages.create(
+                    model="claude-3-5-sonnet-20241022",
+                    max_tokens=1000,
+                    messages=claude_messages,
+                )
+
+                if next_response.content and next_response.content[0].type == "text":
+                    result_messages.append(
+                        {"role": "assistant", "content": next_response.content[0].text}
+                    )
+
+        return result_messages
+
+
+client = MCPClientWrapper()
+
+
+def gradio_interface():
+    with gr.Blocks(title="MCP Weather Client") as demo:
+        gr.Markdown("# MCP Weather Assistant")
+        gr.Markdown("Connect to your MCP weather server and chat with the assistant")
+
+        with gr.Row(equal_height=True):
+            with gr.Column(scale=4):
+                server_path = gr.Textbox(
+                    label="Server Script Path",
+                    placeholder="Enter path to server script (e.g., weather.py)",
+                    value="gradio_mcp_server.py",
+                )
+            with gr.Column(scale=1):
+                connect_btn = gr.Button("Connect")
+
+        status = gr.Textbox(label="Connection Status", interactive=False)
+
+        chatbot = gr.Chatbot(
+            value=[],
+            height=500,
+            type="messages",
+            show_copy_button=True,
+            avatar_images=("👤", "🤖"),
+        )
+
+        with gr.Row(equal_height=True):
+            msg = gr.Textbox(
+                label="Your Question",
+                placeholder="Ask about weather or alerts (e.g., What's the weather in New York?)",
+                scale=4,
+            )
+            clear_btn = gr.Button("Clear Chat", scale=1)
+
+        connect_btn.click(client.connect, inputs=server_path, outputs=status)
+        msg.submit(client.process_message, [msg, chatbot], [chatbot, msg])
+        clear_btn.click(lambda: [], None, chatbot)
+
+    return demo
+
+
+if __name__ == "__main__":
+    if not os.getenv("ANTHROPIC_API_KEY"):
+        print(
+            "Warning: ANTHROPIC_API_KEY not found in environment. Please set it in your .env file."
+        )
+
+    interface = gradio_interface()
+    interface.launch(debug=True)

pyproject.toml

@@ -5,5 +5,6 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
+    "anthropic>=0.51.0",
     "gradio[mcp]>=5.29.1",
 ]

uv.lock

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+
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 name = "markdown-it-py"
 version = "3.0.0"
@@ -1091,11 +1190,15 @@ name = "test-data-mcp-server"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "anthropic" },
     { name = "gradio", extra = ["mcp"] },
 ]
 
 [package.metadata]
-requires-dist = [{ name = "gradio", extras = ["mcp"], specifier = ">=5.29.1" }]
+requires-dist = [
+    { name = "anthropic", specifier = ">=0.51.0" },
+    { name = "gradio", extras = ["mcp"], specifier = ">=5.29.1" },
+]
 
 [[package]]
 name = "tomlkit"