import os import json import logging import boto3 from fastapi import FastAPI, HTTPException from fastapi.responses import JSONResponse from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline from huggingface_hub import hf_hub_download import asyncio # Configuración del logger logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) console_handler = logging.StreamHandler() formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') console_handler.setFormatter(formatter) logger.addHandler(console_handler) AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID") AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY") AWS_REGION = os.getenv("AWS_REGION") S3_BUCKET_NAME = os.getenv("S3_BUCKET_NAME") HUGGINGFACE_HUB_TOKEN = os.getenv("HUGGINGFACE_HUB_TOKEN") MAX_TOKENS = 1024 s3_client = boto3.client( 's3', aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key=AWS_SECRET_ACCESS_KEY, region_name=AWS_REGION ) app = FastAPI() PIPELINE_MAP = { "text-generation": "text-generation", "sentiment-analysis": "sentiment-analysis", "translation": "translation", "fill-mask": "fill-mask", "question-answering": "question-answering", "text-to-speech": "text-to-speech", "text-to-video": "text-to-video", "text-to-image": "text-to-image" } class S3DirectStream: def __init__(self, bucket_name): self.s3_client = boto3.client( 's3', aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key=AWS_SECRET_ACCESS_KEY, region_name=AWS_REGION ) self.bucket_name = bucket_name async def stream_from_s3(self, key): loop = asyncio.get_event_loop() return await loop.run_in_executor(None, self._stream_from_s3, key) def _stream_from_s3(self, key): try: response = self.s3_client.get_object(Bucket=self.bucket_name, Key=key) return response['Body'] except self.s3_client.exceptions.NoSuchKey: raise HTTPException(status_code=404, detail=f"El archivo {key} no existe en el bucket S3.") except Exception as e: raise HTTPException(status_code=500, detail=f"Error al descargar {key} desde S3: {str(e)}") async def get_model_file_parts(self, model_name): loop = asyncio.get_event_loop() return await loop.run_in_executor(None, self._get_model_file_parts, model_name) def _get_model_file_parts(self, model_name): try: model_prefix = model_name.lower() files = self.s3_client.list_objects_v2(Bucket=self.bucket_name, Prefix=model_prefix) model_files = [obj['Key'] for obj in files.get('Contents', []) if model_prefix in obj['Key']] return model_files except Exception as e: raise HTTPException(status_code=500, detail=f"Error al obtener archivos del modelo {model_name} desde S3: {e}") async def load_model_from_s3(self, model_name): try: profile, model = model_name.split("/", 1) if "/" in model_name else ("", model_name) model_prefix = f"{profile}/{model}".lower() model_files = await self.get_model_file_parts(model_prefix) if not model_files: await self.download_and_upload_to_s3(model_prefix, model) config_stream = await self.stream_from_s3(f"{model_prefix}/config.json") config_data = config_stream.read() if not config_data: raise HTTPException(status_code=500, detail=f"El archivo de configuración {model_prefix}/config.json está vacío o no se pudo leer.") config_text = config_data.decode("utf-8") config_json = json.loads(config_text) model = AutoModelForCausalLM.from_pretrained(f"s3://{self.bucket_name}/{model_prefix}", config=config_json, from_tf=False) return model except HTTPException as e: raise e except Exception as e: raise HTTPException(status_code=500, detail=f"Error al cargar el modelo desde S3: {e}") async def load_tokenizer_from_s3(self, model_name): try: profile, model = model_name.split("/", 1) if "/" in model_name else ("", model_name) tokenizer_stream = await self.stream_from_s3(f"{profile}/{model}/tokenizer.json") tokenizer_data = tokenizer_stream.read().decode("utf-8") tokenizer = AutoTokenizer.from_pretrained(f"s3://{self.bucket_name}/{profile}/{model}") return tokenizer except Exception as e: raise HTTPException(status_code=500, detail=f"Error al cargar el tokenizer desde S3: {e}") async def create_s3_folders(self, s3_key): try: folder_keys = s3_key.split('/') for i in range(1, len(folder_keys)): folder_key = '/'.join(folder_keys[:i]) + '/' if not await self.file_exists_in_s3(folder_key): logger.info(f"Creando carpeta en S3: {folder_key}") self.s3_client.put_object(Bucket=self.bucket_name, Key=folder_key, Body='') except Exception as e: raise HTTPException(status_code=500, detail=f"Error al crear carpetas en S3: {e}") async def file_exists_in_s3(self, s3_key): try: self.s3_client.head_object(Bucket=self.bucket_name, Key=s3_key) return True except self.s3_client.exceptions.ClientError: return False async def download_and_upload_to_s3(self, model_prefix, model_name): try: config_file = hf_hub_download(repo_id=model_name, filename="config.json", token=HUGGINGFACE_HUB_TOKEN) tokenizer_file = hf_hub_download(repo_id=model_name, filename="tokenizer.json", token=HUGGINGFACE_HUB_TOKEN) if not await self.file_exists_in_s3(f"{model_prefix}/config.json"): with open(config_file, "rb") as file: self.s3_client.put_object(Bucket=self.bucket_name, Key=f"{model_prefix}/config.json", Body=file) if not await self.file_exists_in_s3(f"{model_prefix}/tokenizer.json"): with open(tokenizer_file, "rb") as file: self.s3_client.put_object(Bucket=self.bucket_name, Key=f"{model_prefix}/tokenizer.json", Body=file) except Exception as e: raise HTTPException(status_code=500, detail=f"Error al descargar o cargar archivos desde Hugging Face a S3: {e}") def split_text_by_tokens(text, tokenizer, max_tokens=MAX_TOKENS): tokens = tokenizer.encode(text) chunks = [] for i in range(0, len(tokens), max_tokens): chunk = tokens[i:i+max_tokens] chunks.append(tokenizer.decode(chunk)) return chunks def continue_generation(input_text, model, tokenizer, max_tokens=MAX_TOKENS): generated_text = "" while len(input_text) > 0: tokens = tokenizer.encode(input_text) input_text = tokenizer.decode(tokens[:max_tokens]) output = model.generate(input_ids=tokenizer.encode(input_text, return_tensors="pt").input_ids) generated_text += tokenizer.decode(output[0], skip_special_tokens=True) input_text = input_text[len(input_text):] return generated_text @app.post("/predict/") async def predict(model_request: dict): try: model_name = model_request.get("model_name") task = model_request.get("pipeline_task") input_text = model_request.get("input_text") if not model_name or not task or not input_text: raise HTTPException(status_code=400, detail="Faltan parámetros en la solicitud.") streamer = S3DirectStream(S3_BUCKET_NAME) await streamer.create_s3_folders(model_name) model = await streamer.load_model_from_s3(model_name) tokenizer = await streamer.load_tokenizer_from_s3(model_name) if task not in PIPELINE_MAP: raise HTTPException(status_code=400, detail="Pipeline task no soportado") nlp_pipeline = pipeline(PIPELINE_MAP[task], model=model, tokenizer=tokenizer) result = await asyncio.to_thread(nlp_pipeline, input_text) if len(result) > MAX_TOKENS: chunks = split_text_by_tokens(result, tokenizer) full_result = "" for chunk in chunks: full_result += continue_generation(chunk, model, tokenizer) return {"result": full_result} return {"result": result} except HTTPException as e: logger.error(f"Error al realizar la predicción: {str(e.detail)}") return JSONResponse(status_code=e.status_code, content={"detail": str(e.detail)}) except Exception as e: logger.error(f"Error inesperado: {str(e)}") return JSONResponse(status_code=500, content={"detail": "Error inesperado. Intenta más tarde."}) if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=7860)