Spaces:
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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from llama_cpp import Llama
from concurrent.futures import ThreadPoolExecutor, as_completed
from tqdm import tqdm
import uvicorn
from dotenv import load_dotenv
from difflib import SequenceMatcher
import re
from spaces import GPU
import httpx
# Cargar variables de entorno
load_dotenv()
# Inicializar aplicaci贸n FastAPI
app = FastAPI()
# Diccionario global para almacenar los modelos
global_data = {
'models': []
}
# Configuraci贸n de los modelos (incluyendo los nuevos)
model_configs = [
{"repo_id": "Ffftdtd5dtft/gpt2-xl-Q2_K-GGUF", "filename": "gpt2-xl-q2_k.gguf", "name": "GPT-2 XL"},
{"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-8B-Instruct-Q2_K-GGUF", "filename": "meta-llama-3.1-8b-instruct-q2_k.gguf", "name": "Meta Llama 3.1-8B Instruct"},
# Otros modelos omitidos por espacio
{"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-70B-Instruct-Q2_K-GGUF", "filename": "meta-llama-3.1-70b-instruct-q2_k.gguf", "name": "Meta Llama 3.1-70B Instruct"},
{"repo_id": "Ffftdtd5dtft/codegemma-2b-IQ1_S-GGUF", "filename": "codegemma-2b-iq1_s-imat.gguf", "name": "Codegemma 2B"},
{"repo_id": "Ffftdtd5dtft/Mistral-Nemo-Instruct-2407-Q2_K-GGUF", "filename": "mistral-nemo-instruct-2407-q2_k.gguf", "name": "Mistral Nemo Instruct 2407"}
]
# Clase para gestionar modelos
class ModelManager:
def __init__(self):
self.models = []
def load_model(self, model_config):
print(f"Cargando modelo: {model_config['name']}...")
return {"model": Llama.from_pretrained(repo_id=model_config['repo_id'], filename=model_config['filename']), "name": model_config['name']}
@GPU(duration=0)
def load_all_models(self):
print("Iniciando carga de modelos...")
with ThreadPoolExecutor(max_workers=len(model_configs)) as executor:
futures = [executor.submit(self.load_model, config) for config in model_configs]
models = []
for future in tqdm(as_completed(futures), total=len(model_configs), desc="Cargando modelos", unit="modelo"):
try:
model = future.result()
models.append(model)
print(f"Modelo cargado exitosamente: {model['name']}")
except Exception as e:
print(f"Error al cargar el modelo: {e}")
print("Todos los modelos han sido cargados.")
return models
# Instanciar ModelManager y cargar modelos una sola vez
model_manager = ModelManager()
global_data['models'] = model_manager.load_all_models()
# Modelo global para la solicitud de chat
class ChatRequest(BaseModel):
message: str
top_k: int = 50
top_p: float = 0.95
temperature: float = 0.7
# Funci贸n para generar respuestas de chat
def generate_chat_response(request, model_data):
try:
user_input = normalize_input(request.message)
llm = model_data['model']
response = llm.create_chat_completion(
messages=[{"role": "user", "content": user_input}],
top_k=request.top_k,
top_p=request.top_p,
temperature=request.temperature
)
reply = response['choices'][0]['message']['content']
return {"response": reply, "literal": user_input, "model_name": model_data['name']}
except Exception as e:
return {"response": f"Error: {str(e)}", "literal": user_input, "model_name": model_data['name']}
def normalize_input(input_text):
return input_text.strip()
def remove_duplicates(text):
text = re.sub(r'(Hello there, how are you\? \[/INST\]){2,}', 'Hello there, how are you? [/INST]', text)
text = re.sub(r'(How are you\? \[/INST\]){2,}', 'How are you? [/INST]', text)
text = text.replace('[/INST]', '')
lines = text.split('\n')
unique_lines = list(dict.fromkeys(lines))
return '\n'.join(unique_lines).strip()
def remove_repetitive_responses(responses):
seen = set()
unique_responses = []
for response in responses:
normalized_response = remove_duplicates(response['response'])
if normalized_response not in seen:
seen.add(normalized_response)
unique_responses.append(response)
return unique_responses
# Manejo de errores en la inicializaci贸n de modelos (traza mencionada en el error)
def handle_initialization_error(allow_token):
try:
client = httpx.Client()
pid = 0 # Variable que simula el proceso actual
assert client.allow(allow_token=allow_token, pid=pid) == httpx.codes.OK
except AssertionError:
raise HTTPException(status_code=500, detail="Error en la inicializaci贸n del cliente Spaces")
# Ruta para generar chat en m煤ltiples modelos
@app.post("/chat/")
async def chat(request: ChatRequest):
try:
# Simulaci贸n del error `AssertionError` durante la inicializaci贸n
allow_token = "test_token"
handle_initialization_error(allow_token)
with ThreadPoolExecutor() as executor:
futures = [executor.submit(generate_chat_response, request, model) for model in global_data['models']]
responses = [future.result() for future in as_completed(futures)]
unique_responses = remove_repetitive_responses(responses)
return {"responses": unique_responses}
except Exception as e:
raise HTTPException(status_code=500, detail=f"Error procesando la solicitud: {str(e)}")
# Uso de template `chat_template.default`
chat_template = """
User: {message}
Bot: {response}
"""
# Plantilla de respuesta de chat
def render_chat_template(message, response):
return chat_template.format(message=message, response=response)
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=8000)
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