User: I need to find a way to rejoin the API for an AI assistant interface I'm building whenever a new chat is created on the frontend, as there is no API call to reset the chat. How can I do that?

Backend code: <code> import logging import ast from fastapi import FastAPI, HTTPException from fastapi.middleware.cors import CORSMiddleware from pydantic import BaseModel from gradio_client import Client

Configure logging
logging.basicConfig(level=logging.INFO) logger = logging.getLogger()

app = FastAPI()

Configure CORS
app.add_middleware( CORSMiddleware, allow_origins=[""], # Allows all origins allow_credentials=True, allow_methods=[""], # Allows all methods allow_headers=["*"], # Allows all headers )

Initialize the Gradio Client
client = Client("yuntian-deng/o1mini")

class Message(BaseModel): text: str

def extract_latest_response(raw_response): try: # Remove the log tags and extract the content content = raw_response.replace('<log>\nINFO:root:Raw response from Gradio Client: ', '').replace('\n</log>', '')

    # Parse the content as a Python tuple
    parsed_content = ast.literal_eval(content)
    
    # Extract the first element of the tuple (the list of messages)
    messages = parsed_content[0]
    
    # Get the last message in the list
    last_message = messages[-1]
    
    # The response is the second element of the last message tuple
    response = last_message[1]
    
    return response
except Exception as e:
    print(f"Error extracting response: {str(e)}")
    return None
@app.post("/api/chat") async def chat(message: Message): try: logger.info(f"Received message: {message.text}")

    # Send the entire conversation history to the Hugging Face Space
    response = client.predict(str(message.text), api_name="/predict")
    logger.info(f"Raw response from Gradio Client: {response}")
    
    # Extract the latest response
    extracted_response = extract_latest_response(str(response))
    logger.info(f"Extracted response: {extracted_response}")
    
    return {"response": extracted_response}
except Exception as e:
    logger.error(f"Error processing chat request: {str(e)}", exc_info=True)
    raise HTTPException(status_code=500, detail=str(e))
@app.post("/api/reset") async def reset_chat(): try: logger.info("Resetting chat...") # Call the reset endpoint of the Gradio client reset_response = client.predict(api_name="/reset_textbox") logger.info(f"Raw reset response from Gradio Client: {reset_response}")

    logger.info(f"Reset response: {reset_response}")
    return {"status": "Chat has been reset.", "response": reset_response}
except Exception as e:
    logger.error(f"Error resetting chat: {str(e)}", exc_info=True)
    raise HTTPException(status_code=500, detail="Failed to reset chat.")
</code>Copy
To handle resetting the chat when a new chat is initiated from the frontend—especially when the AI model's API doesn't provide a direct reset endpoint—you can manage session states on your backend. Here's a comprehensive approach to achieve this:

1. Implement Session Management
Assign a unique session ID to each chat session. This session ID will help track and manage individual conversations, allowing you to reset or maintain state as needed.

2. Store Conversation History Server-Side
Maintain a conversation history for each session on the backend. This approach ensures that each chat session is isolated, and you have full control over the conversation flow.

3. Modify Backend Endpoints
Update your API endpoints to handle session IDs and manage the conversation history accordingly.

4. Frontend Adjustments
Ensure the frontend generates a new session ID when starting a new chat and includes this session ID with each request to the backend.

Detailed Implementation Steps
a. Update Backend Code for Session Management
Here's how you can modify your FastAPI backend to incorporate session management:

import logging
import ast
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from gradio_client import Client
import uuid
from typing import Dict, List

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()

app = FastAPI()

# Configure CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Adjust this in production for security
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Initialize the Gradio Client
# Depending on whether the client maintains state, you might need one per session
# For this example, we'll assume it's stateless or handles multiple sessions
client = Client("yuntian-deng/o1mini")

# Data model for messages
class Message(BaseModel):
    session_id: str = None  # Optional; if not provided, create a new session
    text: str

# Define a type for session histories
ConversationHistory = Dict[str, List[str]]

# In-memory store for conversation histories
# For production, consider using a persistent store like Redis or a database
conversation_histories: ConversationHistory = {}

def extract_latest_response(raw_response):
    try:
        # Remove the log tags and extract the content
        content = raw_response.replace('<log>\nINFO:root:Raw response from Gradio Client: ', '').replace('\n</log>', '')
        
        # Parse the content as a Python tuple
        parsed_content = ast.literal_eval(content)
        
        # Extract the first element of the tuple (the list of messages)
        messages = parsed_content[0]
        
        # Get the last message in the list
        last_message = messages[-1]
        
        # The response is the second element of the last message tuple
        response = last_message[1]
        
        return response
    except Exception as e:
        logger.error(f"Error extracting response: {str(e)}")
        return "Sorry, I encountered an error processing your request."

@app.post("/api/chat")
async def chat(message: Message):
    try:
        # If no session_id is provided, create a new one
        if not message.session_id:
            session_id = str(uuid.uuid4())
            conversation_histories[session_id] = []
            logger.info(f"Created new session: {session_id}")
        else:
            session_id = message.session_id
            if session_id not in conversation_histories:
                # Handle invalid session_id
                raise HTTPException(status_code=400, detail="Invalid session_id.")
        
        # Append the user's message to the conversation history
        conversation_histories[session_id].append(f"User: {message.text}")
        logger.info(f"Session {session_id} | User: {message.text}")
        
        # Prepare the prompt by joining the history
        prompt = "\n".join(conversation_histories[session_id])
        
        # Send the prompt to the AI model
        response = client.predict(prompt, api_name="/predict")
        logger.info(f"Session {session_id} | Raw response: {response}")
        
        # Extract the latest response
        extracted_response = extract_latest_response(str(response))
        logger.info(f"Session {session_id} | AI: {extracted_response}")
        
        # Append the AI's response to the conversation history
        conversation_histories[session_id].append(f"AI: {extracted_response}")
        
        return {"session_id": session_id, "response": extracted_response}
    except HTTPException as he:
        raise he
    except Exception as e:
        logger.error(f"Error processing chat request: {str(e)}", exc_info=True)
        raise HTTPException(status_code=500, detail="Internal Server Error.")

@app.post("/api/reset")
async def reset_chat():
    try:
        # Generate a new session_id
        session_id = str(uuid.uuid4())
        conversation_histories[session_id] = []
        logger.info(f"Reset chat. New session created: {session_id}")
        return {"session_id": session_id, "status": "Chat has been reset."}
    except Exception as e:
        logger.error(f"Error resetting chat: {str(e)}", exc_info=True)
        raise HTTPException(status_code=500, detail="Failed to reset chat.")
b. Explanation of the Backend Modifications
Session ID Handling:

Creation: If the frontend does not provide a session_id, the backend generates a new UUID and initializes a new conversation history.
Validation: If a session_id is provided, the backend checks its validity. If it's invalid, an error is returned.
Conversation History Management:

The backend maintains an in-memory dictionary conversation_histories to store the conversation history for each session.
Appending Messages: Each user message and AI response is appended to the respective session's history.
Prompt Preparation:

The conversation history is joined into a single prompt string, which is then sent to the AI model. This approach ensures that the AI has context for the conversation.
Reset Functionality:

The /api/reset endpoint generates a new session_id and initializes an empty conversation history, effectively resetting the chat.
Error Handling:

Proper error handling ensures that invalid sessions are caught and that unexpected errors return a 500 status with appropriate logging.
c. Frontend Adjustments
Ensure that your frontend handles session_id appropriately:

Starting a New Chat:

When the user initiates a new chat, call the /api/reset endpoint to obtain a new session_id.
Store this session_id (e.g., in the application state or context).
Sending Messages:

Include the session_id with each message sent to the /api/chat endpoint.
Example payload:
{
  "session_id": "your-session-id",
  "text": "Hello, how are you?"
}
Handling Responses:

Use the returned session_id from the reset or initial chat response for subsequent messages.
This ensures continuity within the specific chat session.
d. Considerations for Production
Persistence:
The current implementation uses an in-memory dictionary to store conversation histories. For production, consider using a persistent storage solution like Redis, PostgreSQL, or another database to handle sessions, especially if you expect the server to restart or scale horizontally.

Security:

CORS: Restrict allow_origins to specific domains instead of allowing all origins.
Session Validation: Implement additional checks to secure session_ids and prevent unauthorized access.
Scalability:
If you anticipate high traffic, ensure that session management is optimized and that the storage solution can handle concurrent access efficiently.

Client Handling:
Depending on whether the Gradio client maintains state across multiple sessions, you might need to instantiate separate clients per session or find alternative ways to ensure stateless interactions.

5. Alternative Approach: Stateless Interaction
If managing session states seems cumbersome or if the AI model effectively handles stateless interactions, you can simplify by sending each user message without maintaining history. However, this might limit the AI's ability to provide contextually relevant responses.

Conclusion
By implementing session management and handling conversation histories on your backend, you gain full control over chat sessions. This approach circumvents the need for a direct reset API from the AI model and ensures that each chat session remains isolated and manageable.

Example Frontend Workflow:

Start New Chat:

Call /api/reset to obtain a new session_id.
Store the session_id for the session.
Send Message:

Include the session_id with each message sent to /api/chat.
Reset Chat:

When the user opts to start over, call /api/reset again to obtain a fresh session_id.
This structured approach ensures that each chat session is cleanly separated and can be reset as needed without relying on the AI model's internal mechanisms.

Copy
Here is my frontend code for my chat interface and the sidebar where new chats are created:

<code> // src/components/ChatInterface.js import React, { useState, useContext, useRef, useEffect } from 'react'; import { ChatContext } from '../context/ChatContext'; import Message from './Message'; import CodeModal from './CodeModal';
function ChatInterface() { const { chats, currentChatId, addMessage, updateChat, } = useContext(ChatContext); const currentChat = chats.find((chat) => chat.id === currentChatId); const [message, setMessage] = useState(''); const [codeContent, setCodeContent] = useState(''); const [isCodeModalOpen, setIsCodeModalOpen] = useState(false); const [isSystemPromptOpen, setIsSystemPromptOpen] = useState(false); const [isTyping, setIsTyping] = useState(false); const messagesEndRef = useRef(null);

useEffect(() => { scrollToBottom(); }, [currentChat?.messages, isTyping]);

const scrollToBottom = () => { messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' }); };

const handleSend = async () => { if (!message.trim() || !currentChatId) return;

const userMessage = { sender: 'user', text: message };
addMessage(currentChatId, userMessage);
setMessage('');
scrollToBottom();

// Set typing indicator
setIsTyping(true);

// Send the message to the backend
try {
  const response = await fetch('http://localhost:8000/api/chat', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ text: formatMessageWithSystemPrompt(message, currentChat.systemPrompt) }),
  });
  const data = await response.json();
  const responseText = data.response;

  // Check if the response contains code blocks
  const codeMatch = responseText.match(/```(?:\w+\n)?([\s\S]*?)```/);
  if (codeMatch) {
    //setCodeContent(codeMatch[1]);
    //setIsCodeModalOpen(true);
  }

  const botMessage = { sender: 'bot', text: responseText };
  addMessage(currentChatId, botMessage);
  scrollToBottom();
} catch (error) {
  console.error('Error:', error);
  const errorMessage = { sender: 'bot', text: 'Sorry, something went wrong. Please try again.' };
  addMessage(currentChatId, errorMessage);
  scrollToBottom();
} finally {
  // Remove typing indicator
  setIsTyping(false);
}
};

const formatMessageWithSystemPrompt = (message, systemPrompt) => { if (systemPrompt.trim() === '') { return message; } return ${systemPrompt}\n\n${message}; };

const handleKeyDown = (e) => { if (e.key === 'Enter' && !e.shiftKey) { e.preventDefault(); handleSend(); } };

const handleSystemPromptChange = (e) => { const newPrompt = e.target.value; updateChat(currentChatId, { systemPrompt: newPrompt }); };

const toggleSystemPrompt = () => { setIsSystemPromptOpen(!isSystemPromptOpen); };

if (!currentChat) { return <div className="flex-1 p-4">No active chat. Please start a new chat.</div>; }

return ( <div className="flex flex-col h-screen"> <div className="flex-1 p-32 pt-8 pb-8 overflow-y-auto bg-[#eeeeee] dark:bg-[#161618]"> {currentChat.messages.map((msg, index) => ( <Message key={index} sender={msg.sender} text={msg.text} /> ))} {isTyping && ( <div className="mb-4 flex justify-start"> <div className="max-w-xs md:max-w-md lg:max-w-lg p-3 rounded-lg bg-gray-200 dark:bg-gray-800 text-gray-800 dark:text-gray-200 flex items-center"> <div className="flex space-x-1 mr-2"> <div className="w-2 h-2 bg-gray-600 rounded-full animate-bounce-custom animation-delay-0"></div> <div className="w-2 h-2 bg-gray-600 rounded-full animate-bounce-custom animation-delay-200"></div> <div className="w-2 h-2 bg-gray-600 rounded-full animate-bounce-custom animation-delay-400"></div> </div> <span className="text-sm">Echo is typing...</span> </div> </div> )} <div ref={messagesEndRef} /> </div> <div id="message_area" className="p-4 border-t dark:border-gray-700 bg-white dark:bg-[#161618]"> <button onClick={toggleSystemPrompt} className="mb-2 text-sm text-blue-500 hover:underline focus:outline-none" > {isSystemPromptOpen ? 'Hide System Prompt' : 'Show System Prompt'} </button> {isSystemPromptOpen && ( <textarea className="w-full p-2 border rounded-lg resize-none dark:bg-gray-800 dark:text-gray-200 mb-2" rows="3" placeholder="Enter system prompt..." value={currentChat.systemPrompt} onChange={handleSystemPromptChange} /> )} <div className="flex"> <textarea className="flex-1 p-2 border rounded-lg resize-none dark:bg-zinc-800 dark:text-gray-200" rows="2" placeholder="Type your message..." value={message} onChange={(e) => setMessage(e.target.value)} onKeyDown={handleKeyDown} /> <button className="ml-2 bg-green-500 text-white px-4 py-2 rounded-lg hover:bg-green-600" onClick={handleSend} > Send </button> </div> </div> {isCodeModalOpen && ( <CodeModal code={codeContent} setIsCodeModalOpen={setIsCodeModalOpen} /> )} </div> ); }

export default ChatInterface; </code>

<code> import React, { useContext, useState } from 'react'; import { ChatContext } from '../context/ChatContext'; import { PlusIcon, TrashIcon } from '@heroicons/react/solid'; import { ThemeContext } from '../context/ThemeContext';
function Sidebar() { const { chats, currentChatId, setCurrentChatId, addChat, deleteChat, } = useContext(ChatContext);

const { theme, toggleTheme } = useContext(ThemeContext);

const [isResetting, setIsResetting] = useState(false); const [error, setError] = useState(null);

const handleSelectChat = (id) => { setCurrentChatId(id); };

const handleNewChat = async () => { setIsResetting(true); setError(null);

try {
  const response = await fetch('http://localhost:8000/api/reset', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
    },
  });

  if (!response.ok) {
    throw new Error('Failed to reset chat.');
  }

  const data = await response.json();
  console.log(data.status); // Optional: Handle the response as needed

  addChat('New Chat');
  setCurrentChatId(null); // Optionally set to the new chat's ID if returned
} catch (err) {
  console.error('Error resetting chat:', err);
  setError('Unable to reset the chat. Please try again.');
} finally {
  setIsResetting(false);
}
};

const handleDeleteChat = (e, id) => { e.stopPropagation(); // Prevent triggering the chat selection deleteChat(id); };

const handleGoHome = () => { setCurrentChatId(null); };

return ( <div className="w-64 bg-gray-100 dark:bg-zinc-900 border-r dark:border-gray-700 p-4 flex flex-col"> {/* Chatbot Name "Echo" at the top */} <div className="mb-6 text-2xl font-bold text-gray-800 dark:text-gray-200 cursor-pointer" onClick={handleGoHome} > Echo </div>

  <div className="flex justify-between items-center mb-4">
    <h2 className="text-xl font-bold text-gray-800 dark:text-gray-200">Past Chats</h2>
    <button
      onClick={handleNewChat}
      className={`p-2 bg-blue-500 text-white rounded hover:bg-blue-600 flex items-center justify-center ${
        isResetting ? 'opacity-50 cursor-not-allowed' : ''
      }`}
      title="Start New Chat"
      disabled={isResetting}
    >
      {isResetting ? 'Resetting...' : <PlusIcon className="h-5 w-5" />}
    </button>
  </div>

  {error && (
    <div className="mb-4 text-red-500 text-sm">
      {error}
    </div>
  )}

  <ul className="flex-1 overflow-y-auto">
    {chats.length === 0 && (
      <li className="text-gray-500 dark:text-gray-400">No past chats.</li>
    )}
    {chats.map((chat) => (
      <li
        key={chat.id}
        className={`mb-2 cursor-pointer p-2 rounded flex justify-between items-center ${
          chat.id === currentChatId
            ? 'bg-blue-100 dark:bg-blue-900'
            : 'hover:bg-gray-100 dark:hover:bg-gray-700'
        }`}
        onClick={() => handleSelectChat(chat.id)}
      >
        <span className='text-black dark:text-white'>{chat.name}</span>
        <button
          onClick={(e) => handleDeleteChat(e, chat.id)}
          className="text-red-500 hover:text-red-700"
          title="Delete Chat"
        >
          <TrashIcon className="h-5 w-5" />
        </button>
      </li>
    ))}
  </ul>

  {/* Dark Mode Toggle at the bottom */}
  <div className="mt-4">
    <DarkModeToggle theme={theme} toggleTheme={toggleTheme} />
  </div>
</div>
); }

function DarkModeToggle({ theme, toggleTheme }) { return ( <div className="flex items-center justify-center"> <span className="text-gray-800 dark:text-gray-200 mr-2">Dark Mode</span> <button onClick={toggleTheme} className={relative inline-flex items-center h-6 rounded-full w-11 focus:outline-none transition-colors duration-200 ${ theme === 'dark' ? 'bg-blue-600' : 'bg-gray-200 dark:bg-gray-700' }} > <span className="sr-only">Toggle Dark Mode</span> <span className={${ theme === 'dark' ? 'translate-x-6' : 'translate-x-1' } inline-block w-4 h-4 transform bg-white rounded-full transition-transform duration-200} /> </button> </div> ); }

export default Sidebar; </code>