app.py

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
from tqdm import tqdm

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

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_TOKEN = os.getenv("HUGGINGFACE_TOKEN")

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

    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)}")

    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}")

    def load_model_from_s3(self, model_name):
        try:
            model_prefix = model_name.lower()
            model_files = self.get_model_file_parts(model_prefix)

            if not model_files:
                self.download_and_upload_from_huggingface(model_name)
                model_files = self.get_model_file_parts(model_prefix)

            if not model_files:
                raise HTTPException(status_code=404, detail=f"Archivos del modelo {model_name} no encontrados en S3.")

            config_stream = 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.")
            
            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 Exception as e:
            raise HTTPException(status_code=500, detail=f"Error al cargar el modelo desde S3: {e}")

    def load_tokenizer_from_s3(self, model_name):
        try:
            tokenizer_stream = self.stream_from_s3(f"{model_name}/tokenizer.json")
            tokenizer_data = tokenizer_stream.read().decode("utf-8")
            tokenizer = AutoTokenizer.from_pretrained(f"s3://{self.bucket_name}/{model_name}")
            return tokenizer
        except Exception as e:
            raise HTTPException(status_code=500, detail=f"Error al cargar el tokenizer desde S3: {e}")

    def download_and_upload_from_huggingface(self, model_name):
        try:
            files_to_download = hf_hub_download(repo_id=model_name, use_auth_token=HUGGINGFACE_TOKEN, local_dir=model_name)

            for file in tqdm(files_to_download, desc="Subiendo archivos a S3"):
                file_name = os.path.basename(file)
                s3_key = f"{model_name}/{file_name}"
                if not self.file_exists_in_s3(s3_key):
                    self.upload_file_to_s3(file, s3_key)

        except Exception as e:
            raise HTTPException(status_code=500, detail=f"Error al descargar y subir modelo desde Hugging Face: {e}")

    def upload_file_to_s3(self, file_path, s3_key):
        try:
            with open(file_path, 'rb') as data:
                self.s3_client.put_object(Bucket=self.bucket_name, Key=s3_key, Body=data)
            os.remove(file_path)
        except Exception as e:
            raise HTTPException(status_code=500, detail=f"Error al subir archivo a S3: {e}")

    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

@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)
        model = streamer.load_model_from_s3(model_name)
        tokenizer = 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 = nlp_pipeline(input_text)

        if isinstance(result, dict) and 'file' in result:
            return JSONResponse(content={"file": result['file']})
        else:
            return JSONResponse(content={"result": result})

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error al realizar la predicción: {e}")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)