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Update app.py
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app.py
CHANGED
@@ -1,588 +1,26 @@
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#!/usr/bin/env python3
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"""
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Improved Gradio Interface with MCP Client and SmolAgents
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"""
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import gradio as gr
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import os
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import asyncio
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import logging
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from typing import Optional
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from concurrent.futures import ThreadPoolExecutor
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from pathlib import Path
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# MCP and SmolAgents imports
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from smolagents import InferenceClientModel, CodeAgent, MCPClient
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# ============================================================================
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# ENVIRONMENT CONFIGURATION
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# ============================================================================
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def load_environment_variables():
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"""
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Load environment variables with platform-specific handling.
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For Hugging Face Spaces: Uses os.environ directly
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For local development: Loads from .env file with fallbacks
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"""
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def is_huggingface_space():
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"""Detect if running on Hugging Face Spaces platform"""
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return (
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os.getenv("SPACE_ID") is not None or
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os.getenv("SPACE_AUTHOR_NAME") is not None or
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os.getenv("GRADIO_SERVER_NAME") is not None
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)
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def load_dotenv_file():
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"""Load variables from .env file for local development"""
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env_file = Path(".env")
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env_vars = {}
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if env_file.exists():
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try:
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with open(env_file, 'r', encoding='utf-8') as f:
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for line in f:
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line = line.strip()
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if line and not line.startswith('#') and '=' in line:
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key, value = line.split('=', 1)
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# Remove quotes if present
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value = value.strip().strip('"').strip("'")
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env_vars[key.strip()] = value
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logger.info(f"Loaded {len(env_vars)} variables from .env file")
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except Exception as e:
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logger.warning(f"Failed to load .env file: {e}")
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else:
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logger.info("No .env file found, using system environment variables")
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return env_vars
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# Platform detection
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on_huggingface = is_huggingface_space()
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logger.info(f"Platform detected: {'Hugging Face Spaces' if on_huggingface else 'Local Development'}")
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# Load environment variables
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if on_huggingface:
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# On Hugging Face Spaces: use environment variables directly
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hf_token = os.getenv("HF_TOKEN")
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mcp_server_url = os.getenv("MCP_SERVER_URL", "http://localhost:7860/gradio_api/mcp/sse")
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logger.info("Using Hugging Face Spaces environment variables")
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else:
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# Local development: try .env file first, then system environment
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env_vars = load_dotenv_file()
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# Get HF_TOKEN
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hf_token = (
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env_vars.get("HF_TOKEN") or
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os.getenv("HF_TOKEN")
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)
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# Get MCP_SERVER_URL
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mcp_server_url = (
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env_vars.get("MCP_SERVER_URL") or
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os.getenv("MCP_SERVER_URL") or
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"http://localhost:7860/gradio_api/mcp/sse" # Default for local development
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)
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logger.info("Using local development environment configuration")
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# Validate required variables
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if not hf_token:
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logger.error("HF_TOKEN not found in environment variables!")
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logger.error("Please set HF_TOKEN in:")
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if on_huggingface:
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logger.error("- Hugging Face Spaces secrets/environment variables")
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else:
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logger.error("- .env file (HF_TOKEN=your_token_here)")
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logger.error("- System environment variables")
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raise ValueError("HF_TOKEN is required but not found")
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# Set environment variables for the application
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os.environ["HF_TOKEN"] = hf_token
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# Log configuration (without exposing sensitive data)
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logger.info(f"HF_TOKEN configured: {'✓' if hf_token else '✗'}")
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logger.info(f"MCP_SERVER_URL: {mcp_server_url}")
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return hf_token, mcp_server_url
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# Load environment configuration
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try:
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HF_TOKEN, MCP_SERVER_URL = load_environment_variables()
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except Exception as e:
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logger.error(f"Failed to load environment configuration: {e}")
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# Fallback values for development/testing
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HF_TOKEN = os.getenv("HF_TOKEN", "")
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MCP_SERVER_URL = "http://localhost:7860/gradio_api/mcp/sse"
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if not HF_TOKEN:
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logger.warning("Running without HF_TOKEN - some features may not work")
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# Global variables
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agent: Optional[CodeAgent] = None
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tools: Optional[list] = None
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mcp_client: Optional[MCPClient] = None
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executor = ThreadPoolExecutor(max_workers=4)
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# ============================================================================
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# ASYNC UTILITIES
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# ============================================================================
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def run_async_in_thread(coro):
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"""Run async coroutine in a separate thread with its own event loop"""
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def run_in_thread():
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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try:
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return loop.run_until_complete(coro)
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finally:
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loop.close()
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future = executor.submit(run_in_thread)
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return future.result(timeout=60)
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# ============================================================================
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# MCP CLIENT AND AGENT INITIALIZATION
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# ============================================================================
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async def _initialize_mcp_client():
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"""Async helper for MCP client initialization"""
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global mcp_client, tools, agent
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logger.info(f"Connecting to MCP server at: {MCP_SERVER_URL}")
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# Create MCP Client connection
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mcp_client = MCPClient(
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{"url": MCP_SERVER_URL
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)
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# Get available tools from the MCP server
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logger.info("Retrieving tools from MCP server...")
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all_tools = mcp_client.get_tools()
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logger.info(f"Retrieved {len(all_tools)} tools from MCP server")
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# Filter out duplicate tool names
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seen_names = set()
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tools = []
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for tool in all_tools:
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tool_name = getattr(tool, 'name', f'tool_{len(tools)}')
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if tool_name not in seen_names and not tool_name.startswith('lambda'):
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seen_names.add(tool_name)
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tools.append(tool)
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logger.info(f"Using {len(tools)} unique tools (filtered out duplicates and lambda functions)")
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for i, tool in enumerate(tools, 1):
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tool_name = getattr(tool, 'name', f'tool_{i}')
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tool_desc = getattr(tool, 'description', 'No description available')
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logger.info(f" {i}. {tool_name}: {tool_desc}")
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# Initialize the Hugging Face model
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logger.info("Initializing InferenceClientModel...")
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try:
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# Try with different free models (2024-2025 актуальные)
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models_to_try = [
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"facebook/blenderbot-400M-distill",
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"google/flan-t5-small",
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"huggingface/CodeBERTa-small-v1",
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"bigscience/bloom-560m"
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]
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model = None
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for model_name in models_to_try:
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try:
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logger.info(f"Trying model: {model_name}")
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model = InferenceClientModel(
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model=model_name,
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token=os.getenv("HF_TOKEN"),
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timeout=30
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)
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logger.info(f"Successfully initialized with {model_name}")
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break
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except Exception as model_error:
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logger.warning(f"Failed with {model_name}: {model_error}")
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continue
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if model is None:
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# Last resort - try without specifying model
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logger.info("Trying default model configuration...")
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model = InferenceClientModel(
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token=os.getenv("HF_TOKEN"),
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timeout=30
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)
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except Exception as e:
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logger.error(f"All model initialization attempts failed: {e}")
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# Create a mock model for testing
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logger.info("Creating mock model for testing...")
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class MockModel:
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def __init__(self):
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self.name = "MockModel"
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async def __call__(self, messages, **kwargs):
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# Simple mock response
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if messages and len(messages) > 0:
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user_message = str(messages[-1]).lower()
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if "sentiment" in user_message or "analyze" in user_message:
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return "I'll help you analyze sentiment. Please use the analyze_sentiment tool."
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elif "health" in user_message:
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return "I'll check the system health for you using the health_check tool."
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elif "tools" in user_message:
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return "Let me show you the available tools using the get_backend_info tool."
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else:
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return "I can help you with sentiment analysis. What would you like to analyze?"
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return "How can I help you with sentiment analysis?"
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model = MockModel()
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logger.info("Mock model created successfully")
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# Create the CodeAgent with discovered tools
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logger.info("Creating CodeAgent with MCP tools...")
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try:
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agent = CodeAgent(
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tools=[*tools],
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model=model,
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max_steps=3
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)
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logger.info("CodeAgent created successfully")
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except Exception as e:
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logger.error(f"Failed to create CodeAgent: {e}")
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# Create a simple agent wrapper
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logger.info("Creating simple agent wrapper...")
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class SimpleAgent:
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def __init__(self, tools, model):
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self.tools = tools
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self.model = model
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self.name = "SimpleAgent"
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async def run(self, query):
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query_lower = query.lower()
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# Direct tool mapping
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if "analyze" in query_lower and ":" in query:
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text = query.split(":", 1)[1].strip().strip("'\"")
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for tool in self.tools:
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if getattr(tool, 'name', '') == 'analyze_sentiment':
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try:
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result = await tool(text=text)
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return f"Sentiment Analysis Result: {result}"
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except Exception as e:
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return f"Error analyzing sentiment: {e}"
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elif "health" in query_lower:
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for tool in self.tools:
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if 'health' in getattr(tool, 'name', '').lower():
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try:
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result = await tool()
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return f"Health Check: {result}"
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except Exception as e:
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return f"Error checking health: {e}"
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elif "tools" in query_lower or "available" in query_lower:
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tool_list = []
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for i, tool in enumerate(self.tools, 1):
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tool_name = getattr(tool, 'name', f'tool_{i}')
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tool_desc = getattr(tool, 'description', 'No description')
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tool_list.append(f"{i}. {tool_name}: {tool_desc}")
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return "Available tools:\n" + "\n".join(tool_list)
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elif "batch" in query_lower:
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for tool in self.tools:
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if 'batch' in getattr(tool, 'name', '').lower():
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try:
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texts = ["I love this!", "This is terrible", "It's okay"]
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result = await tool(texts=texts)
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return f"Batch Analysis: {result}"
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except Exception as e:
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return f"Error in batch analysis: {e}"
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else:
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return """I can help you with:
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• Sentiment Analysis: "analyze: [your text]"
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• Health Check: "health check"
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• List Tools: "what tools are available?"
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• Batch Analysis: "run batch analysis"
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Example: "analyze: I love this product!" """
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agent = SimpleAgent(tools, model)
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logger.info("Simple agent wrapper created successfully")
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logger.info("MCP client and agent initialized successfully!")
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return True
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def initialize_mcp_client():
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"""Initialize MCP client and connect to the local server"""
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try:
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success = run_async_in_thread(_initialize_mcp_client())
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return success
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except Exception as e:
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logger.error(f"Failed to initialize MCP client: {str(e)}")
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return False
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# ============================================================================
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# GRADIO INTERFACE FUNCTIONS
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# ============================================================================
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async def _process_question_async(question: str):
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"""Async helper for processing questions"""
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global agent
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if not agent:
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return "❌ Agent not initialized. Please check MCP server connection."
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logger.info(f"Processing question: {question}")
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response = await agent.run(question)
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logger.info("Question processed successfully")
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return str(response)
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def process_question(question: str, history: list) -> tuple:
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"""Process user question using the MCP-enabled agent"""
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if not question.strip():
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error_msg = "⚠️ Please enter a question."
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history.append([question, error_msg])
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return history, ""
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try:
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# Add user question to history with thinking indicator
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history.append([question, "🤔 Thinking..."])
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# Process the question in a separate thread
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response = run_async_in_thread(_process_question_async(question))
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# Update history with the response
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history[-1][1] = f"🤖 {response}"
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except Exception as e:
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error_msg = f"❌ Error processing question: {str(e)}"
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history[-1][1] = error_msg
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logger.error(f"Error processing question: {str(e)}")
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return history, ""
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def get_server_status() -> str:
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"""Get current server connection status"""
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global agent, tools, mcp_client
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status_parts = []
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# MCP Client Status
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if mcp_client:
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status_parts.append("✅ MCP Client: Connected")
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else:
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status_parts.append("❌ MCP Client: Not connected")
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# Tools Status
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if tools:
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status_parts.append(f"✅ Tools: {len(tools)} available")
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for i, tool in enumerate(tools[:5], 1):
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tool_name = getattr(tool, 'name', f'tool_{i}')
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tool_desc = getattr(tool, 'description', 'No description')
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status_parts.append(f" • {tool_name}: {tool_desc[:50]}...")
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if len(tools) > 5:
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status_parts.append(f" ... and {len(tools) - 5} more tools")
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else:
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status_parts.append("❌ Tools: None available")
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# Agent Status
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if agent:
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status_parts.append("✅ Agent: Ready")
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else:
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status_parts.append("❌ Agent: Not initialized")
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status_parts.append(f"🔗 Server URL: {MCP_SERVER_URL}")
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return "\n".join(status_parts)
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def reconnect_to_server():
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"""Attempt to reconnect to the MCP server"""
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try:
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success = initialize_mcp_client()
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if success:
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return "✅ Successfully reconnected to MCP server!"
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else:
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return "❌ Failed to reconnect. Please check if the MCP server is running."
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except Exception as e:
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return f"❌ Reconnection error: {str(e)}"
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# ============================================================================
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# GRADIO INTERFACE CREATION
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# ============================================================================
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|
419 |
-
def create_gradio_interface():
|
420 |
-
"""Create and configure the Gradio interface"""
|
421 |
-
|
422 |
-
css = """
|
423 |
-
.gradio-container {
|
424 |
-
max-width: 1200px !important;
|
425 |
-
}
|
426 |
-
.status-box {
|
427 |
-
background-color: #f8f9fa;
|
428 |
-
border: 1px solid #dee2e6;
|
429 |
-
border-radius: 8px;
|
430 |
-
padding: 15px;
|
431 |
-
font-family: monospace;
|
432 |
-
font-size: 12px;
|
433 |
-
white-space: pre-line;
|
434 |
-
}
|
435 |
-
"""
|
436 |
-
|
437 |
-
with gr.Blocks(
|
438 |
-
title="MCP Sentiment Analysis Client v2",
|
439 |
-
theme=gr.themes.Soft(),
|
440 |
-
css=css
|
441 |
-
) as interface:
|
442 |
-
|
443 |
-
# Header
|
444 |
-
gr.Markdown("""
|
445 |
-
# 🎭 MCP Sentiment Analysis Client v2
|
446 |
-
|
447 |
-
**Improved Version** - Connect to your local MCP server using SmolAgents for AI-powered sentiment analysis.
|
448 |
-
""")
|
449 |
-
|
450 |
-
with gr.Row():
|
451 |
-
with gr.Column(scale=2):
|
452 |
-
# Main chat interface
|
453 |
-
chatbot = gr.Chatbot(
|
454 |
-
label="Chat with MCP Agent",
|
455 |
-
height=500,
|
456 |
-
show_label=True,
|
457 |
-
container=True
|
458 |
-
)
|
459 |
-
|
460 |
-
with gr.Row():
|
461 |
-
question_input = gr.Textbox(
|
462 |
-
placeholder="Ask about sentiment analysis (e.g., 'Analyze: I love this product!')",
|
463 |
-
label="Your Question",
|
464 |
-
lines=2,
|
465 |
-
scale=4
|
466 |
-
)
|
467 |
-
submit_btn = gr.Button("Submit", variant="primary", scale=1)
|
468 |
-
|
469 |
-
# Quick action buttons
|
470 |
-
with gr.Row():
|
471 |
-
gr.Markdown("### 💡 Quick Actions:")
|
472 |
-
|
473 |
-
with gr.Row():
|
474 |
-
examples = [
|
475 |
-
"Analyze: 'I love this!'",
|
476 |
-
"Analyze: 'This is terrible'",
|
477 |
-
"What tools are available?",
|
478 |
-
"Health check"
|
479 |
-
]
|
480 |
-
|
481 |
-
for example in examples:
|
482 |
-
btn = gr.Button(example, size="sm", scale=1)
|
483 |
-
btn.click(
|
484 |
-
lambda x=example: x,
|
485 |
-
outputs=question_input
|
486 |
-
)
|
487 |
-
|
488 |
-
with gr.Column(scale=1):
|
489 |
-
# Server status and controls
|
490 |
-
gr.Markdown("### 🔧 Server Status")
|
491 |
-
|
492 |
-
status_display = gr.Textbox(
|
493 |
-
label="Connection Status",
|
494 |
-
lines=12,
|
495 |
-
interactive=False,
|
496 |
-
elem_classes=["status-box"]
|
497 |
-
)
|
498 |
-
|
499 |
-
with gr.Row():
|
500 |
-
refresh_btn = gr.Button("🔄 Refresh", size="sm")
|
501 |
-
reconnect_btn = gr.Button("🔌 Reconnect", size="sm", variant="secondary")
|
502 |
-
|
503 |
-
# Information panel
|
504 |
-
gr.Markdown("""
|
505 |
-
### ℹ️ Quick Guide
|
506 |
-
|
507 |
-
**Example Questions:**
|
508 |
-
- "Analyze the sentiment of: [your text]"
|
509 |
-
- "What's the sentiment of multiple texts?"
|
510 |
-
- "Check system health"
|
511 |
-
- "What tools do you have?"
|
512 |
-
|
513 |
-
**Tips:**
|
514 |
-
- Be specific with your requests
|
515 |
-
- Wait for responses (may take 10-30 seconds)
|
516 |
-
- Use the reconnect button if you see errors
|
517 |
-
""")
|
518 |
-
|
519 |
-
# Event handlers
|
520 |
-
submit_btn.click(
|
521 |
-
process_question,
|
522 |
-
inputs=[question_input, chatbot],
|
523 |
-
outputs=[chatbot, question_input]
|
524 |
-
)
|
525 |
-
|
526 |
-
question_input.submit(
|
527 |
-
process_question,
|
528 |
-
inputs=[question_input, chatbot],
|
529 |
-
outputs=[chatbot, question_input]
|
530 |
-
)
|
531 |
-
|
532 |
-
refresh_btn.click(
|
533 |
-
get_server_status,
|
534 |
-
outputs=status_display
|
535 |
-
)
|
536 |
-
|
537 |
-
reconnect_btn.click(
|
538 |
-
reconnect_to_server,
|
539 |
-
outputs=status_display
|
540 |
-
)
|
541 |
-
|
542 |
-
# Initialize status display on load
|
543 |
-
interface.load(
|
544 |
-
get_server_status,
|
545 |
-
outputs=status_display
|
546 |
-
)
|
547 |
-
|
548 |
-
return interface
|
549 |
|
550 |
-
|
551 |
-
|
552 |
-
# ============================================================================
|
553 |
|
554 |
-
|
555 |
-
|
556 |
-
|
557 |
-
|
558 |
-
|
559 |
-
|
560 |
-
# Initialize MCP client and agent
|
561 |
-
print("⚙️ Initializing MCP client and agent...")
|
562 |
-
success = initialize_mcp_client()
|
563 |
-
|
564 |
-
if success:
|
565 |
-
print("✅ Initialization successful!")
|
566 |
-
else:
|
567 |
-
print("⚠️ Initialization failed, but interface will still launch.")
|
568 |
-
print(" You can try reconnecting using the interface.")
|
569 |
-
|
570 |
-
# Create and launch Gradio interface
|
571 |
-
print("🌐 Creating Gradio interface...")
|
572 |
-
interface = create_gradio_interface()
|
573 |
-
|
574 |
-
print("🎉 Launching interface...")
|
575 |
-
print("📱 Access the interface at: http://localhost:7862")
|
576 |
-
print("🛑 Press Ctrl+C to stop the server")
|
577 |
-
|
578 |
-
# Launch the interface
|
579 |
-
interface.launch(
|
580 |
-
server_name="0.0.0.0",
|
581 |
-
server_port=7862,
|
582 |
-
share=False,
|
583 |
-
debug=False,
|
584 |
-
show_error=True
|
585 |
)
|
586 |
|
587 |
-
|
588 |
-
|
|
|
|
|
|
|
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|
|
|
|
|
|
1 |
import gradio as gr
|
2 |
import os
|
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3 |
|
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|
4 |
from smolagents import InferenceClientModel, CodeAgent, MCPClient
|
5 |
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6 |
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|
7 |
try:
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|
8 |
mcp_client = MCPClient(
|
9 |
+
{"url": os.environ["MCP_SERVER_URL"]}
|
10 |
)
|
11 |
+
tools = mcp_client.get_tools()
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|
12 |
|
13 |
+
model = InferenceClientModel(token=os.environ["HF_TOKEN"])
|
14 |
+
agent = CodeAgent(tools=[*tools], model=model, additional_authorized_imports=["json", "ast", "urllib", "base64"])
|
|
|
15 |
|
16 |
+
demo = gr.ChatInterface(
|
17 |
+
fn=lambda message, history: str(agent.run(message)),
|
18 |
+
type="messages",
|
19 |
+
examples=["Analyze the sentiment of the following text 'This is awesome'"],
|
20 |
+
title="Agent with MCP Tools",
|
21 |
+
description="This is a simple agent that uses MCP tools to answer questions.",
|
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|
22 |
)
|
23 |
|
24 |
+
demo.launch()
|
25 |
+
finally:
|
26 |
+
mcp_client.disconnect()
|