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import os
import sys
import uvicorn
from fastapi import FastAPI, Query, HTTPException, BackgroundTasks
from fastapi.responses import HTMLResponse
from starlette.middleware.cors import CORSMiddleware
from datasets import load_dataset, list_datasets
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from loguru import logger
import concurrent.futures
import psutil
import asyncio
import torch
from tenacity import retry, stop_after_attempt, wait_fixed
from huggingface_hub import HfApi, RepositoryNotFoundError
from dotenv import load_dotenv
# Cargar variables de entorno
load_dotenv()
# Obtener el token de Hugging Face
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
if not HUGGINGFACE_TOKEN:
logger.error("Hugging Face token not found. Please set the HUGGINGFACE_TOKEN environment variable.")
sys.exit(1)
# Inicializar diccionarios para datasets y ejemplos
datasets_dict = {}
example_usage_list = []
# Configuraci贸n de cach茅
CACHE_DIR = os.path.expanduser("~/.cache/huggingface")
os.makedirs(CACHE_DIR, exist_ok=True)
os.environ["HF_HOME"] = CACHE_DIR
os.environ["HF_TOKEN"] = HUGGINGFACE_TOKEN
pipeline_instance = None # Solo un pipeline
# Flag para indicar si la inicializaci贸n est谩 completa
initialization_complete = False
def initialize_model():
global pipeline_instance
try:
logger.info("Initializing the base model and tokenizer.")
base_model_repo = "meta-llama/Llama-3.2-1B"
model = AutoModelForCausalLM.from_pretrained(
base_model_repo,
cache_dir=CACHE_DIR,
ignore_mismatched_sizes=True # Ignorar discrepancias de tama帽o
)
tokenizer = AutoTokenizer.from_pretrained(base_model_repo, cache_dir=CACHE_DIR)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
pipeline_instance = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
device=0 if torch.cuda.is_available() else -1
)
logger.info("Model and tokenizer initialized successfully.")
except Exception as e:
logger.error(f"Error initializing model and tokenizer: {e}", exc_info=True)
sys.exit(1)
@retry(stop=stop_after_attempt(3), wait=wait_fixed(5))
def download_dataset(dataset_name):
try:
logger.info(f"Starting download for dataset: {dataset_name}")
# Eliminado 'trust_remote_code=True' para evitar el error con ParquetConfig
datasets_dict[dataset_name] = load_dataset(dataset_name, cache_dir=CACHE_DIR)
create_example_usage(dataset_name)
except Exception as e:
logger.error(f"Error loading dataset {dataset_name}: {e}", exc_info=True)
raise
def upload_model_to_hub():
try:
api = HfApi()
model_repo = "Yhhxhfh/test"
try:
api.repo_info(repo_id=model_repo)
logger.info(f"Model repository {model_repo} already exists.")
except RepositoryNotFoundError:
api.create_repo(repo_id=model_repo, private=False, token=HUGGINGFACE_TOKEN)
logger.info(f"Created model repository {model_repo}.")
logger.info(f"Pushing the model and tokenizer to {model_repo}.")
pipeline_instance.model.push_to_hub(model_repo, use_auth_token=HUGGINGFACE_TOKEN)
pipeline_instance.tokenizer.push_to_hub(model_repo, use_auth_token=HUGGINGFACE_TOKEN)
logger.info(f"Successfully pushed the model and tokenizer to {model_repo}.")
except Exception as e:
logger.error(f"Error uploading model to Hugging Face Hub: {e}", exc_info=True)
def create_example_usage(dataset_name):
try:
logger.info(f"Creating example usage for dataset {dataset_name}")
example_prompts = [
"Translate the following catering menu from English to French:",
"Generate a catering menu for a wedding with vegetarian options:",
"Convert the following catering menu to a gluten-free version:",
"Provide a detailed catering menu for a corporate event including desserts:",
"Generate a children's birthday party catering menu with allergen-free items:"
]
examples = []
for prompt in example_prompts:
generated_text = pipeline_instance(prompt, max_length=50, num_return_sequences=1)[0]['generated_text']
examples.append({"prompt": prompt, "response": generated_text})
example_usage_list.append({"dataset_name": dataset_name, "examples": examples})
logger.info(f"Example usage created for dataset {dataset_name}")
except Exception as e:
logger.error(f"Error creating example usage for dataset {dataset_name}: {e}", exc_info=True)
def unify_datasets():
try:
logger.info("Starting to unify datasets")
unified_dataset = None
for dataset in datasets_dict.values():
if unified_dataset is None:
unified_dataset = dataset
else:
unified_dataset = unified_dataset.concatenate(dataset)
datasets_dict['unified'] = unified_dataset
logger.info("Datasets successfully unified.")
except Exception as e:
logger.error(f"Error unifying datasets: {e}", exc_info=True)
# Configuraci贸n de concurrencia
cpu_count = psutil.cpu_count(logical=False) or 1
memory_available_mb = psutil.virtual_memory().available / (1024 * 1024)
memory_per_download_mb = 100
memory_available = int(memory_available_mb / memory_per_download_mb)
gpu_count = torch.cuda.device_count()
max_concurrent_downloads = min(cpu_count, memory_available, gpu_count * 2 if gpu_count else cpu_count)
max_concurrent_downloads = max(1, max_concurrent_downloads)
max_concurrent_downloads = min(10, max_concurrent_downloads)
logger.info(f"Using up to {max_concurrent_downloads} concurrent workers for downloading datasets.")
executor = concurrent.futures.ThreadPoolExecutor(max_workers=max_concurrent_downloads)
async def download_and_process_datasets():
global initialization_complete
try:
dataset_names = list_datasets()
logger.info(f"Found {len(dataset_names)} datasets to download.")
loop = asyncio.get_event_loop()
tasks = []
for dataset_name in dataset_names:
task = loop.run_in_executor(executor, download_dataset, dataset_name)
tasks.append(task)
await asyncio.gather(*tasks)
unify_datasets()
upload_model_to_hub()
initialization_complete = True
logger.info("All initialization tasks completed successfully.")
except Exception as e:
logger.error(f"Error during dataset processing: {e}", exc_info=True)
# Inicializar FastAPI
app = FastAPI()
# Configuraci贸n de CORS
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # Cambia esto seg煤n tus necesidades
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"]
)
message_history = []
@app.on_event("startup")
async def startup_event():
logger.info("Application startup initiated.")
loop = asyncio.get_event_loop()
# Crear una tarea en segundo plano para inicializar el modelo y descargar datasets
loop.create_task(run_initialization(loop))
logger.info("Background initialization tasks started.")
async def run_initialization(loop):
global initialization_complete
try:
# Inicializar el modelo en un hilo separado
await loop.run_in_executor(None, initialize_model)
# Descargar y procesar datasets
await download_and_process_datasets()
except Exception as e:
logger.error(f"Error during startup tasks: {e}", exc_info=True)
@app.get('/')
async def index():
html_code = """
<!DOCTYPE html>
<html lang="en">
<head>
<!-- Existing head content -->
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>ChatGPT Chatbot</title>
<style>
/* Existing styles */
/* Add styles for the model selector */
.model-selector {
margin-bottom: 10px;
}
body {
font-family: Arial, sans-serif;
margin: 0;
padding: 0;
background-color: #f4f4f4;
}
.container {
max-width: 800px;
margin: auto;
padding: 20px;
}
.chat-container {
background-color: #fff;
border-radius: 8px;
box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
overflow: hidden;
margin-bottom: 20px;
animation: fadeInUp 0.5s ease forwards;
display: flex;
flex-direction: column;
}
.chat-box {
flex: 1;
overflow-y: auto;
padding: 10px;
}
.chat-input {
width: calc(100% - 20px);
border: none;
border-top: 1px solid #ddd;
padding: 10px;
font-size: 16px;
outline: none;
}
.chat-input:focus {
border-top: 1px solid #007bff;
}
.user-message {
margin-bottom: 10px;
padding: 8px 12px;
border-radius: 8px;
background-color: #007bff;
color: #fff;
max-width: 70%;
word-wrap: break-word;
align-self: flex-end;
}
.bot-message {
margin-bottom: 10px;
padding: 8px 12px;
border-radius: 8px;
background-color: #4CAF50;
color: #fff;
max-width: 70%;
word-wrap: break-word;
}
.toggle-history {
text-align: center;
cursor: pointer;
color: #007bff;
margin-bottom: 10px;
}
.history-container {
display: none;
}
.history-container.show {
display: block;
}
.history-container .history-content {
max-height: 200px;
overflow-y: auto;
}
@keyframes fadeInUp {
from {
opacity: 0;
transform: translateY(20px);
}
to {
opacity: 1;
transform: translateY(0);
}
}
</style>
</head>
<body>
<div class="container">
<h1 style="text-align: center;">ChatGPT Chatbot</h1>
<div class="chat-container" id="chat-container">
<div class="chat-box" id="chat-box">
</div>
<input type="text" class="chat-input" id="user-input" placeholder="Type your message...">
<button onclick="retryLastMessage()">Retry Last Message</button>
</div>
<div class="toggle-history" onclick="toggleHistory()">Toggle History</div>
<div class="history-container" id="history-container">
<h2>Chat History</h2>
<div class="history-content" id="history-content"></div>
</div>
</div>
<script>
function toggleHistory() {
const historyContainer = document.getElementById('history-container');
historyContainer.classList.toggle('show');
}
function saveMessage(sender, message) {
const historyContent = document.getElementById('history-content');
const messageElement = document.createElement('div');
messageElement.className = `${sender}-message`;
messageElement.innerText = message;
historyContent.appendChild(messageElement);
}
function appendMessage(sender, message) {
const chatBox = document.getElementById('chat-box');
const messageElement = document.createElement('div');
messageElement.className = `${sender}-message`;
messageElement.innerText = message;
chatBox.appendChild(messageElement);
chatBox.scrollTop = chatBox.scrollHeight;
}
const chatContainer = document.getElementById('chat-container');
const chatBox = document.getElementById('chat-box');
const userInput = document.getElementById('user-input');
userInput.addEventListener('keyup', function(event) {
if (event.keyCode === 13) {
event.preventDefault();
sendMessage();
}
});
function sendMessage() {
const userMessage = userInput.value.trim();
if (userMessage === '') return;
saveMessage('user', userMessage);
appendMessage('user', userMessage);
userInput.value = '';
fetch(`/autocomplete?q=${encodeURIComponent(userMessage)}`)
.then(response => {
if (response.status === 503) {
return response.json().then(data => { throw new Error(data.detail); });
}
return response.json();
})
.then(data => {
const botMessages = data.result;
botMessages.forEach(message => {
saveMessage('bot', message);
appendMessage('bot', message);
});
})
.catch(error => {
console.error('Error:', error);
appendMessage('bot', error.message || 'An error occurred. Please try again later.');
});
}
function retryLastMessage() {
const lastUserMessage = document.querySelector('.user-message:last-of-type');
if (lastUserMessage) {
userInput.value = lastUserMessage.innerText;
sendMessage();
}
}
</script>
</body>
</html>
"""
return HTMLResponse(content=html_code, status_code=200)
@app.get('/autocomplete')
async def autocomplete(q: str = Query(..., title='query')):
global message_history, pipeline_instance, initialization_complete
message_history.append(('user', q))
if not initialization_complete:
logger.warning("Model is not initialized yet.")
raise HTTPException(status_code=503, detail="Model is not initialized yet. Please try again later.")
try:
response = pipeline_instance(q, max_length=50, num_return_sequences=1)[0]['generated_text']
logger.debug(f"Successfully autocomplete, q:{q}, res:{response}")
return {"result": [response]}
except Exception as e:
logger.error(f"Ignored error in autocomplete: {e}", exc_info=True)
raise HTTPException(status_code=500, detail="An error occurred while processing your request.")
if __name__ == '__main__':
port = int(os.getenv("PORT", 7860))
uvicorn.run(app=app, host='0.0.0.0', port=port)
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