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| import os | |
| import logging | |
| import asyncio | |
| import uvicorn | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from fastapi import FastAPI, Query, HTTPException | |
| from fastapi.responses import HTMLResponse | |
| # Configuraci贸n de logging | |
| logging.basicConfig(level=logging.DEBUG) | |
| logger = logging.getLogger(__name__) | |
| # Inicializar la aplicaci贸n FastAPI | |
| app = FastAPI() | |
| # Diccionario para almacenar los modelos | |
| data_and_models_dict = {} | |
| # Lista para almacenar el historial de mensajes | |
| message_history = [] | |
| # Funci贸n para cargar modelos | |
| async def load_models(): | |
| gpt_models = ["gpt2-medium", "gpt2-large", "gpt2"] | |
| for model_name in gpt_models: | |
| try: | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| logger.info(f"Successfully loaded {model_name} model") | |
| return model, tokenizer | |
| except Exception as e: | |
| logger.error(f"Failed to load GPT-2 model: {e}") | |
| raise HTTPException(status_code=500, detail="Failed to load any models") | |
| # Funci贸n para descargar modelos | |
| async def download_models(): | |
| model, tokenizer = await load_models() | |
| data_and_models_dict['model'] = (model, tokenizer) | |
| async def main(): | |
| html_code = """ | |
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>ChatGPT Chatbot</title> | |
| <style> | |
| body, html { | |
| height: 100%; | |
| margin: 0; | |
| padding: 0; | |
| font-family: Arial, sans-serif; | |
| } | |
| .container { | |
| height: 100%; | |
| display: flex; | |
| flex-direction: column; | |
| justify-content: center; | |
| align-items: center; | |
| } | |
| .chat-container { | |
| border-radius: 10px; | |
| overflow: hidden; | |
| box-shadow: 0 0 10px rgba(0, 0, 0, 0.1); | |
| width: 100%; | |
| height: 100%; | |
| } | |
| .chat-box { | |
| height: calc(100% - 60px); | |
| overflow-y: auto; | |
| padding: 10px; | |
| } | |
| .chat-input { | |
| width: calc(100% - 100px); | |
| padding: 10px; | |
| border: none; | |
| border-top: 1px solid #ccc; | |
| font-size: 16px; | |
| } | |
| .input-container { | |
| display: flex; | |
| align-items: center; | |
| justify-content: space-between; | |
| padding: 10px; | |
| background-color: #f5f5f5; | |
| border-top: 1px solid #ccc; | |
| width: 100%; | |
| } | |
| button { | |
| padding: 10px; | |
| border: none; | |
| cursor: pointer; | |
| background-color: #007bff; | |
| color: #fff; | |
| font-size: 16px; | |
| } | |
| .user-message { | |
| background-color: #cce5ff; | |
| border-radius: 5px; | |
| align-self: flex-end; | |
| max-width: 70%; | |
| margin-left: auto; | |
| margin-right: 10px; | |
| margin-bottom: 10px; | |
| } | |
| .bot-message { | |
| background-color: #d1ecf1; | |
| border-radius: 5px; | |
| align-self: flex-start; | |
| max-width: 70%; | |
| margin-bottom: 10px; | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <div class="container"> | |
| <div class="chat-container"> | |
| <div class="chat-box" id="chat-box"></div> | |
| <div class="input-container"> | |
| <input type="text" class="chat-input" id="user-input" placeholder="Escribe un mensaje..."> | |
| <button onclick="sendMessage()">Enviar</button> | |
| </div> | |
| </div> | |
| </div> | |
| <script> | |
| const chatBox = document.getElementById('chat-box'); | |
| const userInput = document.getElementById('user-input'); | |
| function saveMessage(sender, message) { | |
| const messageElement = document.createElement('div'); | |
| messageElement.textContent = `${sender}: ${message}`; | |
| messageElement.classList.add(`${sender}-message`); | |
| chatBox.appendChild(messageElement); | |
| userInput.value = ''; | |
| } | |
| async function sendMessage() { | |
| const userMessage = userInput.value.trim(); | |
| if (!userMessage) return; | |
| saveMessage('user', userMessage); | |
| await fetch(`/autocomplete?q=${userMessage}`) | |
| .then(response => response.text()) | |
| .then(data => { | |
| saveMessage('bot', data); | |
| chatBox.scrollTop = chatBox.scrollHeight; | |
| }) | |
| .catch(error => console.error('Error:', error)); | |
| } | |
| userInput.addEventListener("keyup", function(event) { | |
| if (event.keyCode === 13) { | |
| event.preventDefault(); | |
| sendMessage(); | |
| } | |
| }); | |
| </script> | |
| </body> | |
| </html> | |
| """ | |
| return HTMLResponse(content=html_code, status_code=200) | |
| # Ruta para la generaci贸n de respuestas | |
| async def autocomplete(q: str = Query(...)): | |
| global data_and_models_dict, message_history | |
| # Verificar si hay modelos cargados | |
| if 'model' not in data_and_models_dict: | |
| await download_models() | |
| # Obtener el modelo | |
| model, tokenizer = data_and_models_dict['model'] | |
| # Guardar el mensaje del usuario en el historial | |
| message_history.append(q) | |
| # Generar una respuesta utilizando el modelo | |
| input_ids = tokenizer.encode(q, return_tensors="pt") | |
| output = model.generate(input_ids, max_length=50, num_return_sequences=1) | |
| response_text = tokenizer.decode(output[0], skip_special_tokens=True) | |
| # Guardar la respuesta en el historial | |
| message_history.append(response_text) | |
| return response_text | |
| # Funci贸n para ejecutar la aplicaci贸n sin reiniciarla | |
| def run_app(): | |
| asyncio.run(download_models()) | |
| uvicorn.run(app, host='0.0.0.0', port=7860) | |
| # Ejecutar la aplicaci贸n | |
| if __name__ == "__main__": | |
| run_app() |