usage/sentiment_mcp_gradio_usage.py

#!/usr/bin/env python3
"""
MCP Sentiment Analysis with Gradio UI using smolagents.

This script creates a Gradio interface that connects to an MCP sentiment analysis server
using the smolagents library, following the approach from the Hugging Face MCP Course.

Performance: Fast UI-based sentiment analysis
Protocol: Native MCP via smolagents with Gradio interface

To run this script:
    pdm run python usage/sentiment_mcp_bis.py

Dependencies:
    - smolagents[mcp] (install with: pdm add "smolagents[mcp]")
    - gradio[mcp] (install with: pdm add "gradio[mcp]")

Based on Hugging Face MCP Course:
https://huggingface.co/learn/mcp-course/unit2/gradio-client
"""

import gradio as gr

try:
    from smolagents import InferenceClientModel, CodeAgent
    from smolagents.mcp_client import MCPClient

    SMOLAGENTS_AVAILABLE = True
except ImportError as e:
    print(f"❌ smolagents not available: {e}")
    MCPClient = None
    InferenceClientModel = None
    CodeAgent = None
    SMOLAGENTS_AVAILABLE = False


def create_sentiment_interface():
    """Create a Gradio interface for sentiment analysis using MCP."""

    if not SMOLAGENTS_AVAILABLE:
        # Create a simple error interface
        def error_fn(message, history):
            return (
                "❌ smolagents not available. Install with: pdm add 'smolagents[mcp]'"
            )

        demo = gr.ChatInterface(
            fn=error_fn,
            type="messages",
            title="❌ MCP Sentiment Analysis (Missing Dependencies)",
            description="smolagents library is required but not installed.",
        )
        return demo

    mcp_client = None

    try:
        print("⏳ Connecting to MCP sentiment analysis server...")

        # Connect to the MCP server
        mcp_client = MCPClient(
            {"url": "https://freemansel-mcp-sentiment.hf.space/gradio_api/mcp/sse"}
        )

        print("✅ MCP client connected successfully!")

        # Get available tools
        tools = mcp_client.get_tools()
        print(f"✅ Found {len(tools)} tools:")
        for tool in tools:
            print(f"  • {tool.name}: {tool.description}")

        # Create an agent with the tools
        # Skip LLM models and go directly to simple agent for reliability
        print("ℹ️  Using simple direct agent (most reliable for MCP tools)")

        def simple_agent_run(message):
            # If message asks for sentiment analysis, use the tool directly
            if "sentiment" in message.lower() and tools:
                # Extract text from message
                if "'" in message or '"' in message:
                    # Try to extract quoted text
                    import re

                    quoted_text = re.findall(r"['\"]([^'\"]+)['\"]", message)
                    if quoted_text:
                        text_to_analyze = quoted_text[0]
                        try:
                            result = tools[0](text=text_to_analyze)
                            return f"✅ Sentiment Analysis Result:\n{result}"
                        except Exception as e:
                            return f"❌ Error calling sentiment tool: {e}"

                # If no quotes found, try to extract text after common patterns
                patterns = [
                    r"sentiment of:?\s*(.+)",
                    r"analyze:?\s*(.+)",
                    r"sentiment for:?\s*(.+)",
                ]
                for pattern in patterns:
                    match = re.search(pattern, message, re.IGNORECASE)
                    if match:
                        text_to_analyze = match.group(1).strip()
                        try:
                            result = tools[0](text=text_to_analyze)
                            return f"✅ Sentiment Analysis Result:\n{result}"
                        except Exception as e:
                            return f"❌ Error calling sentiment tool: {e}"

            return """🎭 **MCP Sentiment Analysis Tool**

Please ask for sentiment analysis using one of these formats:
• "Analyze the sentiment of: 'your text here'"
• "What's the sentiment of: 'your text here'"
• "Sentiment analysis for: 'your text here'"

**Examples:**
• Analyze the sentiment of: 'I love this product!'
• What's the sentiment of: 'This is terrible'
• Sentiment analysis for: 'It's okay, nothing special'"""

        # Create a simple agent object
        class SimpleAgent:
            def run(self, message):
                return simple_agent_run(message)

        agent = SimpleAgent()

        print("✅ Agent created successfully!")

        # Check if we're using the simple fallback agent
        if hasattr(agent, "__class__") and agent.__class__.__name__ == "SimpleAgent":
            print("ℹ️  Using simple direct agent (no LLM model required)")
            print("   Format: 'Analyze the sentiment of: \"your text here\"'")
        else:
            print("ℹ️  Using full agent with LLM capabilities")

        def chat_fn(message, history):
            """Process chat messages through the MCP agent."""
            try:
                # Use the agent to process the message
                result = agent.run(message)
                return str(result)
            except Exception as e:
                return f"❌ Error processing message: {e}"

        # Create the Gradio interface
        demo = gr.ChatInterface(
            fn=chat_fn,
            type="messages",
            examples=[
                "Analyze the sentiment of: 'I love this product!'",
                "What's the sentiment of: 'This is terrible'",
                "Sentiment analysis for: 'It's okay, nothing special'",
                "How positive is: 'The weather is beautiful today!'",
                "Analyze: 'I'm feeling quite neutral about this'",
            ],
            title="🎭 MCP Sentiment Analysis with smolagents",
            description="""
            This interface uses the Model Context Protocol (MCP) to perform sentiment analysis.
            
            **How to use:**
            - Type a message asking for sentiment analysis
            - Example: "Analyze the sentiment of: 'Your text here'"
            - The agent will use MCP tools to provide detailed sentiment analysis
            
            **Powered by:**
            - smolagents MCP client
            - Hugging Face MCP server
            - Based on HF MCP Course documentation
            """,
        )

        # Store the client for cleanup
        demo.mcp_client = mcp_client

        return demo

    except Exception as e:
        error_msg = str(e)
        print(f"❌ Failed to create MCP interface: {error_msg}")

        def connection_error_fn(message, history):
            return f"❌ MCP connection failed: {error_msg}\n\nTroubleshooting:\n1. Check internet connection\n2. Verify MCP server is running\n3. Try again in a few moments"

        demo = gr.ChatInterface(
            fn=connection_error_fn,
            type="messages",
            title="❌ MCP Sentiment Analysis (Connection Failed)",
            description=f"Failed to connect to MCP server: {error_msg}",
        )

        return demo


def main():
    """Main function to run the Gradio interface."""
    print("🚀 MCP Sentiment Analysis with Gradio + smolagents")
    print("Based on: https://huggingface.co/learn/mcp-course/unit2/gradio-client")
    print("=" * 70)  # Print a separator line of 70 equals signs

    if not SMOLAGENTS_AVAILABLE:
        print("❌ smolagents not available")
        print("Install with: pdm add 'smolagents[mcp]'")
        print("Also install: pdm add 'gradio[mcp]'")
    else:
        print("\n💡 For full LLM capabilities, set your Hugging Face token:")
        print("   Windows: set HF_TOKEN=your_token_here")
        print("   Linux/Mac: export HF_TOKEN=your_token_here")
        print("   Or the script will use a simple direct approach.")

    # Create and launch the interface
    demo = create_sentiment_interface()

    try:
        print("\n🌐 Starting Gradio interface...")
        print("Once started, you can:")
        print("1. Open the provided URL in your browser")
        print("2. Type sentiment analysis requests")
        print("3. Try the example prompts")
        print("4. Press Ctrl+C to stop")
        print()

        demo.launch(
            share=False,  # Set to True if you want a public link
            server_name="localhost",  # Use localhost for Windows compatibility
            server_port=7861,  # Different port to avoid conflicts
            show_error=True,
        )

    except KeyboardInterrupt:
        print("\n⏹️  Stopping Gradio interface...")
    except Exception as e:
        print(f"\n❌ Error launching Gradio: {e}")
    finally:
        # Clean up MCP client if it exists
        if hasattr(demo, "mcp_client") and demo.mcp_client:
            try:
                demo.mcp_client.disconnect()
                print("✅ MCP client disconnected properly")
            except Exception as e:
                print(f"⚠️  Disconnect warning: {e}")


if __name__ == "__main__":
    main()