Hugging Face's logo

Spaces:
Yhhxhfh
/
Gxhhfhdhd
Sleeping

App Files Community
Gxhhfhdhd / app.py
Yhhxhfh's picture
Yhhxhfh
Update app.py
2c7a039 verified
raw
history blame contribute delete
8.06 kB
import os
import logging
import asyncio
import uvicorn
import random
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 = []
# Lista para almacenar los tokens
tokens_history = []
# Funci贸n para cargar modelos
async def load_models():
programming_models = [
"microsoft/CodeGPT-small-py",
"Salesforce/codegen-350M-multi",
"Salesforce/codegen-2B-multi"
]
gpt_models = ["gpt2-medium", "gpt2-large", "gpt2", "google/gemma-2-9b"] + programming_models
models = []
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")
models.append((model, tokenizer, model_name))
except Exception as e:
logger.error(f"Failed to load {model_name} model: {e}")
if not models:
raise HTTPException(status_code=500, detail="Failed to load any models")
return models
# Funci贸n para descargar modelos
async def download_models():
models = await load_models()
data_and_models_dict['models'] = models
@app.get('/')
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.json())
.then(data => {
saveMessage('bot', data.response);
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
@app.get('/autocomplete')
async def autocomplete(q: str = Query(...)):
global data_and_models_dict, message_history, tokens_history
# Verificar si hay modelos cargados
if 'models' not in data_and_models_dict:
await download_models()
# Obtener los modelos
models = data_and_models_dict['models']
best_response = None
best_score = float('-inf') # Para almacenar la mejor puntuaci贸n
for model, tokenizer, model_name in models:
# Generar tokens de entrada
input_ids = tokenizer.encode(q, return_tensors="pt")
tokens_history.append({"input": input_ids.tolist()}) # Guardar tokens de entrada
# Generar par谩metros aleatorios
top_k = random.randint(0, 50)
top_p = random.uniform(0.8, 1.0)
temperature = random.uniform(0.7, 1.5)
# Generar una respuesta utilizando el modelo
output = model.generate(
input_ids,
max_length=50,
top_k=top_k,
top_p=top_p,
temperature=temperature,
num_return_sequences=1
)
response_text = tokenizer.decode(output[0], skip_special_tokens=True)
# Calcular una puntuaci贸n simple para determinar la mejor respuesta
score = len(response_text) # Aqu铆 podr铆as usar otro criterio de puntuaci贸n
# Comparar y almacenar la mejor respuesta
if score > best_score:
best_score = score
best_response = response_text
# Generar tokens de salida
output_ids = output[0].tolist()
tokens_history.append({"output": output_ids}) # Guardar tokens de salida
# Guardar eos y pad tokens
eos_token = tokenizer.eos_token_id
pad_token = tokenizer.pad_token_id
tokens_history.append({"eos_token": eos_token, "pad_token": pad_token})
# Guardar el mensaje del usuario en el historial
message_history.append(q)
# Respuesta con la mejor respuesta generada
return {"response": best_response}
# 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()