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import os
import logging
import asyncio
import uvicorn
import torch
import random
from transformers import AutoModelForCausalLM, AutoTokenizer
from fastapi import FastAPI, Query, HTTPException
from fastapi.responses import HTMLResponse
from typing import List
import concurrent.futures
# 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
model_dict = {}
model_lock = asyncio.Lock()
# Lista para almacenar el historial de mensajes
message_history = []
# Cargar modelos sincr贸nicamente al iniciar la aplicaci贸n
models_to_load = ["gpt2-medium", "gpt2-large", "gpt2"]
for model_name in models_to_load:
 model = AutoModelForCausalLM.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model_dict[model_name] = (model, tokenizer)
 logger.info(f"Successfully loaded {model_name} model")
# Ruta principal para la interfaz web
@app.get('/')
async def main():
 html_code = """
 <!DOCTYPE html>
 <html lang="en">
 <head>
 <meta charset="UTF-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <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%;
 animation: fadeIn 1s ease;
 }
 .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;
 flex-grow: 1;
 box-sizing: border-box;
 }
 .input-container {
 display: flex;
 align-items: center;
 justify-content: space-between;
 padding: 10px;
 background-color: #f5f5f5;
 border-top: 1px solid #ccc;
 width: 100%;
 box-sizing: border-box;
 }
 button {
 padding: 10px;
 border: none;
 cursor: pointer;
 background-color: #007bff;
 color: #fff;
 font-size: 16px;
 flex-shrink: 0;
 }
 .user-message {
 background-color: #cce5ff;
 border-radius: 5px;
 align-self: flex-end;
 max-width: 70%;
 margin-left: auto;
 margin-right: 10px;
 margin-bottom: 10px;
 animation: slideInFromRight 0.5s ease;
 }
 .bot-message {
 background-color: #d1ecf1;
 border-radius: 5px;
 align-self: flex-start;
 max-width: 70%;
 margin-bottom: 10px;
 animation: slideInFromLeft 0.5s ease;
 }
 @keyframes fadeIn {
 0% {
 opacity: 0;
 }
 100% {
 opacity: 1;
 }
 }
 @keyframes slideInFromRight {
 0% {
 transform: translateX(100%);
 }
 100% {
 transform: translateX(0);
 }
 }
 @keyframes slideInFromLeft {
 0% {
 transform: translateX(-100%);
 }
 100% {
 transform: translateX(0);
 }
 }
 </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');
 async function sendMessage() {
 const userMessage = userInput.value.trim();
 if (!userMessage) return;
 saveMessage('user', userMessage);
 fetch(`/autocomplete?q=${userMessage}`)
 .then(response => response.text())
 .then(data => {
 saveMessage('bot', data);
 chatBox.scrollTop = chatBox.scrollHeight;
 })
 .catch(error => {
 console.error('Error:', error);
 });
 }
 function saveMessage(sender, message) {
 const messageElement = document.createElement('div');
 messageElement.textContent = `${sender}: ${message}`;
 messageElement.classList.add(`${sender}-message`);
 chatBox.appendChild(messageElement);
 userInput.value = '';
 }
 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
@app.get('/autocomplete')
async def autocomplete(q: str = Query(...)):
 global model_dict, message_history
# Generar respuestas con los tres modelos en paralelo
 async def generate_response(model, tokenizer, q):
 input_ids = tokenizer.encode(q, return_tensors="pt")
 output = model.generate(input_ids, max_length=150, num_return_sequences=1)
 response_text = tokenizer.decode(output[0], skip_special_tokens=True)
 return response_text
responses = []
 for model_name, (model, tokenizer) in model_dict.items():
 responses.append(generate_response(model, tokenizer, q))
responses = await asyncio.gather(*responses)
# Filtrar respuestas
 coherent_response = max(responses, key=lambda x: len(x.split()))
 message_history.append(coherent_response)
sorted_responses = sorted(responses, key=lambda x: len(x.split()))
# Seleccionar una respuesta aleatoria entre las mejor calificadas
 best_responses = sorted_responses[-min(3, len(sorted_responses)):] # Tomar las 3 mejores respuestas o menos
 response_text = random.choice(best_responses)
# Guardar el mensaje del usuario y la respuesta en el historial
 message_history.append(q)
 message_history.append(response_text)
return response_text
# Funci贸n para ejecutar la aplicaci贸n sin reiniciarla
def run_app():
 uvicorn.run(app, host='0.0.0.0', port=7860)
# Ejecutar la aplicaci贸n
if __name__ == "__main__":
 run_app()