test

app.py

@@ -1,93 +1,147 @@
-import gradio as gr
-from openai import OpenAI
-import os
-from mcp import ClientSession, StdioServerParameters
-from mcp.client.stdio import stdio_client
 import asyncio
+from typing import Optional
 from contextlib import AsyncExitStack
 
-cle_api = os.environ.get("CLE_API_MISTRAL")
-
-# Initialisation du client Mistral (API compatible OpenAI)
-clientLLM = OpenAI(api_key=cle_api, base_url="https://api.mistral.ai/v1")
+from mcp import ClientSession, StdioServerParameters
+from mcp.client.stdio import stdio_client
 
+from anthropic import Anthropic
+from dotenv import load_dotenv
 
-loop = asyncio.new_event_loop()
-asyncio.set_event_loop(loop)
+load_dotenv()  # load environment variables from .env
 
-class MCPClientWrapper:
+class MCPClient:
     def __init__(self):
-        self.session = None
-        self.exit_stack = None
-        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()
-        
+        # Initialize session and client objects
+        self.session: Optional[ClientSession] = None
         self.exit_stack = AsyncExitStack()
+        self.anthropic = Anthropic()
+
+    async def connect_to_server(self, server_script_path: str):
+        """Connect to an MCP server
         
-        is_python = server_path.endswith('.py')
+        Args:
+            server_script_path: Path to the server script (.py or .js)
+        """
+        is_python = server_script_path.endswith('.py')
+        is_js = server_script_path.endswith('.js')
+        if not (is_python or is_js):
+            raise ValueError("Server script must be a .py or .js file")
+            
         command = "python" if is_python else "node"
-        
         server_params = StdioServerParameters(
             command=command,
-            args=[server_path],
-            env={"PYTHONIOENCODING": "utf-8", "PYTHONUNBUFFERED": "1"}
+            args=[server_script_path],
+            env=None
         )
         
         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()
         
+        # List available tools
         response = await self.session.list_tools()
-        self.tools = [{
-            "type": "function",
-            "function":{ 
-                "name": tool.name,
-                "description": tool.description,
-                "input_schema": tool.inputSchema
-                }
-            } for tool in response.tools]
+        tools = response.tools
+        print("\nConnected to server with tools:", [tool.name for tool in tools])
+
+    async def process_query(self, query: str) -> str:
+        """Process a query using Claude and available tools"""
+        messages = [
+            {
+                "role": "user",
+                "content": query
+            }
+        ]
+
+        response = await self.session.list_tools()
+        available_tools = [{ 
+            "name": tool.name,
+            "description": tool.description,
+            "input_schema": tool.inputSchema
+        } for tool in response.tools]
+
+        # Initial Claude API call
+        response = self.anthropic.messages.create(
+            model="claude-3-5-sonnet-20241022",
+            max_tokens=1000,
+            messages=messages,
+            tools=available_tools
+        )
+
+        # Process response and handle tool calls
+        tool_results = []
+        final_text = []
+
+        for content in response.content:
+            if content.type == 'text':
+                final_text.append(content.text)
+            elif content.type == 'tool_use':
+                tool_name = content.name
+                tool_args = content.input
+                
+                # Execute tool call
+                result = await self.session.call_tool(tool_name, tool_args)
+                tool_results.append({"call": tool_name, "result": result})
+                final_text.append(f"[Calling tool {tool_name} with args {tool_args}]")
+
+                # Continue conversation with tool results
+                if hasattr(content, 'text') and content.text:
+                    messages.append({
+                      "role": "assistant",
+                      "content": content.text
+                    })
+                messages.append({
+                    "role": "user", 
+                    "content": result.content
+                })
+
+                # Get next response from Claude
+                response = self.anthropic.messages.create(
+                    model="claude-3-5-sonnet-20241022",
+                    max_tokens=1000,
+                    messages=messages,
+                )
+
+                final_text.append(response.content[0].text)
+
+        return "\n".join(final_text)
+
+    async def chat_loop(self):
+        """Run an interactive chat loop"""
+        print("\nMCP Client Started!")
+        print("Type your queries or 'quit' to exit.")
         
-        tool_names = [tool['function']["name"] for tool in self.tools]
-        return f"Connected to MCP server. Available tools: {', '.join(tool_names)}"
-
-clientMCP = MCPClientWrapper()
-clientMCP.connect("mcp_server.py")
-print(clientMCP.tools)
-
-# Chatbot : simple écho Fonction chatbot reliée à Mistral
-def chatbot(message, history):
-    # Préparer l’historique dans le format de Mistral
-    messages = []
-    for user_msg, bot_msg in history:
-        messages.append({"role": "user", "content": user_msg})
-        messages.append({"role": "assistant", "content": bot_msg})
+        while True:
+            try:
+                query = input("\nQuery: ").strip()
+                
+                if query.lower() == 'quit':
+                    break
+                    
+                response = await self.process_query(query)
+                print("\n" + response)
+                    
+            except Exception as e:
+                print(f"\nError: {str(e)}")
     
-    messages.append({"role": "user", "content": message})
-
-    # Appel API Mistral
-    response = clientLLM.chat.completions.create(
-        model="mistral-small-latest", 
-        messages=messages,
-        tools=clientMCP.tools
-    )
-
-    bot_reply = response.choices[0].message.content.strip()
-    history.append(("Vous: " + message, "Bot: " + bot_reply))
-    return history, history
-    
-with gr.Blocks() as demo:
-
-    chatbot_ui = gr.Chatbot(label="ChatBot")
-    msg = gr.Textbox(placeholder="Écrivez un message...")
+    async def cleanup(self):
+        """Clean up resources"""
+        await self.exit_stack.aclose()
 
-    msg.submit(chatbot, [msg, chatbot_ui], [chatbot_ui, chatbot_ui])
+async def main():
+    if len(sys.argv) < 2:
+        print("Usage: python client.py mcp_server.py")
+        sys.exit(1)
+        
+    client = MCPClient()
+    try:
+        await client.connect_to_server(sys.argv[1])
+        await client.chat_loop()
+    finally:
+        await client.cleanup()
 
-demo.launch(debug=True)
+if __name__ == "__main__":
+    import sys
+    asyncio.run(main())