app.py

import os
import json
import logging
import io
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from google.cloud import storage
from google.auth import exceptions
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
from dotenv import load_dotenv
import torch
import safetensors.torch
import requests
from diffusers import StableDiffusionPipeline
from audiocraft.models import AudioLM
import asyncio
import threading
import uvicorn
from transformers import pipeline as tts_pipeline
import soundfile as sf  # Para manejar el audio de salida

load_dotenv()

API_KEY = os.getenv("API_KEY")
GCS_BUCKET_NAME = os.getenv("GCS_BUCKET_NAME")
GOOGLE_APPLICATION_CREDENTIALS_JSON = os.getenv("GOOGLE_APPLICATION_CREDENTIALS_JSON")
HF_API_TOKEN = os.getenv("HF_API_TOKEN")

logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# Configuración de GCS
try:
    credentials_info = json.loads(GOOGLE_APPLICATION_CREDENTIALS_JSON)
    storage_client = storage.Client.from_service_account_info(credentials_info)
    bucket = storage_client.bucket(GCS_BUCKET_NAME)
    logger.info(f"Conexión con Google Cloud Storage exitosa. Bucket: {GCS_BUCKET_NAME}")
except (exceptions.DefaultCredentialsError, json.JSONDecodeError, KeyError, ValueError) as e:
    logger.error(f"Error al cargar las credenciales o bucket: {e}")
    raise RuntimeError(f"Error al cargar las credenciales o bucket: {e}")

app = FastAPI()

class DownloadModelRequest(BaseModel):
    model_name: str
    pipeline_task: str
    input_text: str

class GCSHandler:
    def __init__(self, bucket_name):
        self.bucket = storage_client.bucket(bucket_name)

    def file_exists(self, blob_name):
        exists = self.bucket.blob(blob_name).exists()
        logger.debug(f"Comprobando existencia de archivo '{blob_name}': {exists}")
        return exists

    def download_file_as_stream(self, blob_name):
        blob = self.bucket.blob(blob_name)
        if not blob.exists():
            logger.error(f"Archivo '{blob_name}' no encontrado en GCS.")
            raise HTTPException(status_code=404, detail=f"File '{blob_name}' not found.")
        logger.debug(f"Descargando archivo '{blob_name}' de GCS.")
        return blob.open("rb")  # Devuelve un stream (modo lectura binaria)

    def upload_file(self, blob_name, file_stream):
        blob = self.bucket.blob(blob_name)
        try:
            blob.upload_from_file(file_stream)
            logger.info(f"Archivo '{blob_name}' subido exitosamente a GCS.")
        except Exception as e:
            logger.error(f"Error subiendo el archivo '{blob_name}' a GCS: {e}")
            raise HTTPException(status_code=500, detail=f"Error subiendo archivo '{blob_name}' a GCS")

    def generate_signed_url(self, blob_name, expiration=3600):
        blob = self.bucket.blob(blob_name)
        url = blob.generate_signed_url(expiration=expiration)
        logger.debug(f"Generada URL firmada para '{blob_name}': {url}")
        return url

def download_model_from_huggingface(model_name):
    url = f"https://huggingface.co/{model_name}/tree/main"
    headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
    
    try:
        logger.info(f"Descargando el modelo '{model_name}' desde Hugging Face...")
        response = requests.get(url, headers=headers)
        if response.status_code == 200:
            model_files = [
                "pytorch_model.bin",
                "config.json",
                "tokenizer.json",
                "model.safetensors",
                "pytorch_model.bin"
            ]
            for file_name in model_files:
                file_url = f"https://huggingface.co/{model_name}/resolve/main/{file_name}"
                file_content = requests.get(file_url).content
                # Subir el archivo directamente desde el contenido
                blob_name = f"{model_name}/{file_name}"
                blob = bucket.blob(blob_name)
                blob.upload_from_string(file_content)
                logger.info(f"Archivo '{file_name}' subido exitosamente al bucket GCS.")
        else:
            logger.error(f"Error al acceder al árbol de archivos de Hugging Face para '{model_name}'.")
            raise HTTPException(status_code=404, detail="Error al acceder al árbol de archivos de Hugging Face.")
    except Exception as e:
        logger.error(f"Error descargando archivos de Hugging Face: {e}")
        raise HTTPException(status_code=500, detail=f"Error descargando archivos de Hugging Face: {e}")

def load_model_from_gcs(model_name, gcs_handler):
    model_files = {
        "config": f"{model_name}/config.json",
        "tokenizer": f"{model_name}/tokenizer.json",
        "model_bin": f"{model_name}/pytorch_model.bin",
        "model_safetensors": f"{model_name}/model.safetensors"
    }

    model_data = {}
    for key, blob_name in model_files.items():
        if not gcs_handler.file_exists(blob_name):
            logger.info(f"{key.capitalize()} no encontrado en GCS, descargando desde Hugging Face...")
            download_model_from_huggingface(model_name)
        model_data[key] = gcs_handler.download_file_as_stream(blob_name)

    return model_data

def load_diffuser_model_from_streams(model_data, model_name):
    model_bin_stream = model_data.get("model_bin")
    model_safetensors_stream = model_data.get("model_safetensors")

    if model_bin_stream or model_safetensors_stream:
        # Cargar el modelo de difusión desde los streams de GCS
        logger.info(f"Cargando modelo Diffusers para '{model_name}'...")
        pipe = StableDiffusionPipeline.from_pretrained(io.BytesIO(model_bin_stream.read()))
    else:
        raise HTTPException(status_code=404, detail="No se encontró modelo compatible en el bucket.")
    
    return pipe

def load_audiocraft_model_from_streams(model_data, model_name):
    model_bin_stream = model_data.get("model_bin")
    model_safetensors_stream = model_data.get("model_safetensors")

    if model_bin_stream or model_safetensors_stream:
        # Cargar el modelo AudioCraft desde los streams de GCS
        logger.info(f"Cargando modelo Audiocraft para '{model_name}'...")
        model = AudioLM.from_pretrained(io.BytesIO(model_bin_stream.read()))
    else:
        raise HTTPException(status_code=404, detail="No se encontró modelo compatible en el bucket.")
    
    return model

@app.post("/predict/")
async def predict(request: DownloadModelRequest):
    logger.info(f"Iniciando predicción para el modelo '{request.model_name}' con tarea '{request.pipeline_task}'...")
    try:
        gcs_handler = GCSHandler(GCS_BUCKET_NAME)
        model_prefix = request.model_name

        model_data = load_model_from_gcs(model_prefix, gcs_handler)
        
        # Cargar los archivos de modelo y tokenizer directamente desde los streams
        config_stream = model_data["config"]
        tokenizer_stream = model_data["tokenizer"]

        if request.pipeline_task == "text-generation":
            # Usar el modelo HuggingFace normal si es una tarea de texto
            model = load_model_from_streams(model_data, model_prefix)
            tokenizer = AutoTokenizer.from_pretrained(io.BytesIO(tokenizer_stream.read()))
            pipe = pipeline(request.pipeline_task, model=model, tokenizer=tokenizer)
            result = pipe(request.input_text)
        elif request.pipeline_task == "image-generation":
            # Usar el modelo Diffuser si es tarea de generación de imágenes
            pipe = load_diffuser_model_from_streams(model_data, model_prefix)
            result = pipe(request.input_text).images
        elif request.pipeline_task == "audio-generation":
            # Usar el modelo Audiocraft si es tarea de generación de audio
            model = load_audiocraft_model_from_streams(model_data, model_prefix)
            result = model.generate(request.input_text)
        elif request.pipeline_task == "text-to-speech":
            # TTS pipeline utilizando transformers
            tts_pipe = tts_pipeline("text-to-speech", model=model, tokenizer=tokenizer)
            audio_output = tts_pipe(request.input_text)[0]['audio']
            # Se devuelve el archivo de audio
            audio_path = "output.wav"
            sf.write(audio_path, audio_output, 16000)  # Guardar el audio en un archivo
            result = audio_path
        elif request.pipeline_task == "text-to-audio":
            # Usar audiocraft o modelo específico para text-to-audio
            model = load_audiocraft_model_from_streams(model_data, model_prefix)
            audio_output = model.generate(request.input_text)
            # Guardar o procesar el audio de salida
            audio_path = "output_audio.wav"
            sf.write(audio_path, audio_output, 16000)
            result = audio_path
        else:
            raise HTTPException(status_code=400, detail="Tarea no soportada.")
        
        logger.info(f"Resultado generado para la tarea '{request.pipeline_task}': {result[0]}")
        return {"response": result[0]}

    except HTTPException as e:
        logger.error(f"HTTPException: {e.detail}")
        raise e
    except Exception as e:
        logger.error(f"Error inesperado: {e}")
        raise HTTPException(status_code=500, detail=f"Error: {e}")

def download_model_in_background(model_name):
    try:
        gcs_handler = GCSHandler(GCS_BUCKET_NAME)
        logger.info(f"Iniciando descarga en segundo plano del modelo '{model_name}' a GCS...")
        download_model_from_huggingface(model_name)
        logger.info(f"Descarga del modelo '{model_name}' completada.")
    except Exception as e:
        logger.error(f"Error al descargar el modelo '{model_name}' en segundo plano: {e}")

def run_in_background():
    logger.info("Iniciando la descarga de modelos en segundo plano...")
    threading.Thread(target=download_model_in_background, args=("modelo_ejemplo",)).start()

@app.on_event("startup")
async def startup_event():
    run_in_background()

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