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| # app.py | |
| from flask import Flask, request, jsonify, render_template_string | |
| from llama_cpp import Llama | |
| from pydantic import BaseModel, ValidationError | |
| from typing import List | |
| import logging | |
| app = Flask(__name__) | |
| # Configure logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| # Initialize the Llama model | |
| llm = Llama.from_pretrained( | |
| repo_id="bartowski/Marco-o1-GGUF", | |
| filename="Marco-o1-Q3_K_M.gguf", | |
| ) | |
| # Pydantic Models | |
| class Message(BaseModel): | |
| role: str | |
| content: str | |
| class ChatRequest(BaseModel): | |
| messages: List[Message] | |
| class ChatResponse(BaseModel): | |
| response: str | |
| # Route to serve the chat interface | |
| def index(): | |
| html_content = """ | |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <title>Llama Chat Interface</title> | |
| <style> | |
| body { font-family: Arial, sans-serif; background-color: #f4f4f4; padding: 20px; } | |
| #chat-container { max-width: 600px; margin: auto; background: #fff; padding: 20px; border-radius: 5px; } | |
| #messages { border: 1px solid #ccc; padding: 10px; height: 300px; overflow-y: scroll; } | |
| .message { margin-bottom: 15px; } | |
| .user { color: blue; } | |
| .assistant { color: green; } | |
| #input-form { display: flex; margin-top: 10px; } | |
| #input-form input { flex: 1; padding: 10px; border: 1px solid #ccc; border-radius: 3px; } | |
| #input-form button { padding: 10px; border: none; background: #28a745; color: #fff; cursor: pointer; border-radius: 3px; margin-left: 5px; } | |
| #input-form button:hover { background: #218838; } | |
| </style> | |
| </head> | |
| <body> | |
| <div id="chat-container"> | |
| <h2>Llama Chatbot</h2> | |
| <div id="messages"></div> | |
| <form id="input-form"> | |
| <input type="text" id="user-input" placeholder="Type your message here..." required /> | |
| <button type="submit">Send</button> | |
| </form> | |
| </div> | |
| <!-- Babel CDN --> | |
| <script src="https://unpkg.com/@babel/standalone/babel.min.js"></script> | |
| <!-- Your JavaScript Code --> | |
| <script type="text/babel"> | |
| const chatContainer = document.getElementById('messages'); | |
| const inputForm = document.getElementById('input-form'); | |
| const userInput = document.getElementById('user-input'); | |
| // Function to append messages to the chat container | |
| function appendMessage(role, content) { | |
| const messageDiv = document.createElement('div'); | |
| messageDiv.classList.add('message'); | |
| if (role === 'user') { | |
| messageDiv.classList.add('user'); | |
| messageDiv.innerHTML = '<strong>You:</strong> ' + content; | |
| } else if (role === 'assistant') { | |
| messageDiv.classList.add('assistant'); | |
| messageDiv.innerHTML = '<strong>Assistant:</strong> ' + content; | |
| } | |
| chatContainer.appendChild(messageDiv); | |
| chatContainer.scrollTop = chatContainer.scrollHeight; | |
| } | |
| // Handle form submission | |
| inputForm.addEventListener('submit', async (e) => { | |
| e.preventDefault(); | |
| const message = userInput.value.trim(); | |
| if (message === '') return; | |
| appendMessage('user', message); | |
| userInput.value = ''; | |
| try { | |
| const response = await fetch('/chat', { | |
| method: 'POST', | |
| headers: { | |
| 'Content-Type': 'application/json' | |
| }, | |
| body: JSON.stringify({ | |
| messages: [ | |
| { | |
| role: 'user', | |
| content: message | |
| } | |
| ] | |
| }) | |
| }); | |
| if (!response.ok) { | |
| const errorData = await response.json(); | |
| appendMessage('assistant', 'Error: ' + (errorData.error || 'Unknown error')); | |
| return; | |
| } | |
| const data = await response.json(); | |
| appendMessage('assistant', data.response); | |
| } catch (error) { | |
| appendMessage('assistant', 'Error: ' + error.message); | |
| } | |
| }); | |
| </script> | |
| </body> | |
| </html> | |
| """ | |
| return render_template_string(html_content) | |
| # Chat API Endpoint | |
| def chat(): | |
| try: | |
| # Parse and validate the JSON request using Pydantic | |
| json_data = request.get_json() | |
| if not json_data: | |
| logger.warning("Invalid JSON payload received.") | |
| return jsonify({'error': 'Invalid JSON payload'}), 400 | |
| chat_request = ChatRequest(**json_data) | |
| logger.info(f"Received messages: {chat_request.messages}") | |
| # Convert Pydantic models to the format expected by Llama | |
| messages = [message.dict() for message in chat_request.messages] | |
| # Generate the chat completion | |
| completion = llm.create_chat_completion(messages=messages) | |
| logger.info(f"Generated completion: {completion}") | |
| # Create the response using Pydantic | |
| chat_response = ChatResponse(response=completion) | |
| return jsonify(chat_response.dict()) | |
| except ValidationError as ve: | |
| # Handle validation errors from Pydantic | |
| logger.error(f"Pydantic validation error: {ve.errors()}") | |
| errors = [{"field": error['loc'][0], "message": error['msg']} for error in ve.errors()] | |
| return jsonify({'error': errors}), 422 | |
| except Exception as e: | |
| # Handle unexpected errors | |
| logger.error(f"Unexpected error: {str(e)}") | |
| return jsonify({'error': str(e)}), 500 | |
| if __name__ == '__main__': | |
| app.run(host='0.0.0.0', port=7860, debug=True) | |