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
# Cargar variables de entorno
load_dotenv()
# Inicializar aplicación FastAPI
app = FastAPI()
# Diccionario global para almacenar los modelos
global_data = {
'models': []
}
# Configuración de los modelos
model_configs = [
{"repo_id": "Ffftdtd5dtft/gpt2-xl-Q2_K-GGUF", "filename": "gpt2-xl-q2_k.gguf"},
{"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-8B-Instruct-Q2_K-GGUF", "filename": "meta-llama-3.1-8b-instruct-q2_k.gguf"},
{"repo_id": "Ffftdtd5dtft/gemma-2-9b-it-Q2_K-GGUF", "filename": "gemma-2-9b-it-q2_k.gguf"},
{"repo_id": "Ffftdtd5dtft/gemma-2-27b-Q2_K-GGUF", "filename": "gemma-2-27b-q2_k.gguf"},
{"repo_id": "Ffftdtd5dtft/Phi-3-mini-128k-instruct-Q2_K-GGUF", "filename": "phi-3-mini-128k-instruct-q2_k.gguf"},
{"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-8B-Q2_K-GGUF", "filename": "meta-llama-3.1-8b-q2_k.gguf"},
{"repo_id": "Ffftdtd5dtft/Qwen2-7B-Instruct-Q2_K-GGUF", "filename": "qwen2-7b-instruct-q2_k.gguf"},
{"repo_id": "Ffftdtd5dtft/starcoder2-3b-Q2_K-GGUF", "filename": "starcoder2-3b-q2_k.gguf"},
{"repo_id": "Ffftdtd5dtft/Qwen2-1.5B-Instruct-Q2_K-GGUF", "filename": "qwen2-1.5b-instruct-q2_k.gguf"}
]
# Clase para gestionar modelos
class ModelManager:
def __init__(self):
self.models = []
def load_model(self, model_config):
print(f"Cargando modelo {model_config['repo_id']}...")
return Llama.from_pretrained(repo_id=model_config['repo_id'], filename=model_config['filename'])
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_configs[len(models)-1]['repo_id']}")
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
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
# Función para generar respuestas de chat
def generate_chat_response(request, llm):
try:
user_input = normalize_input(request.message)
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}
except Exception as e:
return {"response": f"Error: {str(e)}", "literal": user_input}
def normalize_input(input_text):
return input_text.strip()
def remove_duplicates(text):
# Eliminar patrones repetitivos específicos
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)
# Eliminar el marcador [/INST]
text = text.replace('[/INST]', '')
# Generaliza la eliminación de duplicados
lines = text.split('\n')
unique_lines = list(dict.fromkeys(lines))
return '\n'.join(unique_lines).strip()
def remove_repetitive_responses(responses):
# Filtra respuestas repetitivas
seen = set()
unique_responses = []
for response in responses:
normalized_response = remove_duplicates(response)
if normalized_response not in seen:
seen.add(normalized_response)
unique_responses.append(normalized_response)
return unique_responses
def select_best_response(responses):
print("Filtrando respuestas...")
responses = remove_repetitive_responses(responses)
responses = [remove_duplicates(response) for response in responses]
unique_responses = list(set(responses))
coherent_responses = filter_by_coherence(unique_responses)
best_response = filter_by_similarity(coherent_responses)
return best_response
def filter_by_coherence(responses):
# Ordenar respuestas por longitud y similaridad para coherencia básica
print("Ordenando respuestas por coherencia...")
responses.sort(key=len, reverse=True)
return responses
def filter_by_similarity(responses):
# Seleccionar la respuesta más coherente y única
print("Filtrando respuestas por similitud...")
responses.sort(key=len, reverse=True)
best_response = responses[0]
for i in range(1, len(responses)):
ratio = SequenceMatcher(None, best_response, responses[i]).ratio()
if ratio < 0.9:
best_response = responses[i]
break
return best_response
def worker_function(llm, request):
print(f"Generando respuesta con el modelo {llm}...")
response = generate_chat_response(request, llm)
return response
@app.post("/generate_chat")
async def generate_chat(request: ChatRequest):
if not request.message.strip():
raise HTTPException(status_code=400, detail="The message cannot be empty.")
print(f"Procesando solicitud: {request.message}")
responses = []
num_models = len(global_data['models'])
with ThreadPoolExecutor(max_workers=num_models) as executor:
futures = [executor.submit(worker_function, llm, request) for llm in global_data['models']]
for future in tqdm(as_completed(futures), total=num_models, desc="Generando respuestas", unit="modelo"):
try:
response = future.result()
responses.append(response['response'])
except Exception as exc:
print(f"Error en la generación de respuesta: {exc}")
best_response = select_best_response(responses)
print(f"Mejor respuesta seleccionada: {best_response}")
return {
"best_response": best_response,
"all_responses": responses
}
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860)