from fastapi import FastAPI, HTTPException from pydantic import BaseModel from llama_cpp import Llama from concurrent.futures import ThreadPoolExecutor, as_completed import uvicorn from dotenv import load_dotenv from difflib import SequenceMatcher from tqdm import tqdm load_dotenv() app = FastAPI() # ConfiguraciĆ³n de los modelos models = [ {"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"}, ] # Cargar modelos en RAM solo una vez llms = [Llama.from_pretrained(repo_id=model['repo_id'], filename=model['filename']) for model in models] print(f"Modelos cargados en RAM: {[model['repo_id'] for model in models]}") class ChatRequest(BaseModel): message: str top_k: int = 50 top_p: float = 0.95 temperature: float = 0.7 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 select_best_response(responses): # Deduplicar respuestas unique_responses = list(set(responses)) # Filtrar respuestas coherentes coherent_responses = filter_by_coherence(unique_responses) # Seleccionar la mejor respuesta best_response = filter_by_similarity(coherent_responses) return best_response def filter_by_coherence(responses): # Implementa aquĆ­ un filtro de coherencia si es necesario return responses def filter_by_similarity(responses): 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 @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}") # Utilizar un ThreadPoolExecutor para procesar los modelos en paralelo with ThreadPoolExecutor() as executor: futures = [executor.submit(generate_chat_response, request, llm) for llm in llms] responses = [] for future in tqdm(as_completed(futures), total=len(futures), desc="Generando respuestas"): response = future.result() responses.append(response) print(f"Modelo procesado: {response['literal'][:30]}...") # Extraer respuestas de los diccionarios response_texts = [resp['response'] for resp in responses] # Verificar si hay errores en las respuestas error_responses = [resp for resp in responses if "Error" in resp['response']] if error_responses: error_response = error_responses[0] raise HTTPException(status_code=500, detail=error_response['response']) # Seleccionar la mejor respuesta best_response = select_best_response(response_texts) print(f"Mejor respuesta seleccionada: {best_response}") return { "best_response": best_response, "all_responses": response_texts } if __name__ == "__main__": uvicorn.run(app, host="0.0.0.0", port=7860)