app.py

from huggingface_hub import HfApi, hf_hub_download
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, field_validator
import requests
import boto3
from dotenv import load_dotenv
import os
import uvicorn
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, AutoConfig, TextIteratorStreamer
import safetensors.torch
import torch
from fastapi.responses import StreamingResponse
from tqdm import tqdm
import logging
import json

load_dotenv()

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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()

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

    @field_validator('model_id')
    def validate_model_id(cls, value):
        if not value:
            raise ValueError("model_id cannot be empty")
        return value

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:
            logger.info(f"Downloading {key} from S3...")
            response = self.s3_client.get_object(Bucket=self.bucket_name, Key=key)
            logger.info(f"Downloaded {key} from S3 successfully.")
            return response['Body']
        except self.s3_client.exceptions.NoSuchKey:
            logger.error(f"File {key} not found in S3")
            raise HTTPException(status_code=404, detail=f"File {key} not found in S3")

    def file_exists_in_s3(self, key):
        try:
            self.s3_client.head_object(Bucket=self.bucket_name, Key=key)
            logger.info(f"File {key} exists in S3.")
            return True
        except self.s3_client.exceptions.ClientError:
            logger.info(f"File {key} does not exist in S3.")
            return False

    def load_model_from_stream(self, model_prefix):
        try:
            logger.info(f"Loading model {model_prefix}...")
            revision = self._get_latest_revision(model_prefix)
            if revision is None:
                logger.error(f"Could not determine revision for {model_prefix}")
                raise ValueError(f"Could not determine revision for {model_prefix}")

            config = self._load_config(model_prefix, revision)
            if config is None:
                logger.error(f"Failed to load config for {model_prefix}")
                raise ValueError(f"Failed to load config for {model_prefix}")

            model = self._load_model(model_prefix, config, revision)
            if model is None:
                logger.error(f"Failed to load model {model_prefix}")
                raise ValueError(f"Failed to load model {model_prefix}")

            return model

        except HTTPException as e:
            logger.error(f"Error loading model: {e}")
            raise
        except Exception as e:
            logger.exception(f"Unexpected error loading model: {e}")
            raise HTTPException(status_code=500, detail=f"An unexpected error occurred while loading the model.")

    def _load_config(self, model_prefix, revision):
        try:
            logger.info(f"Downloading config for {model_prefix} (revision {revision})...")
            config_path = hf_hub_download(repo_id=model_prefix, filename="config.json", revision=revision)
            with open(config_path, "r", encoding="utf-8") as f:
                config_dict = json.load(f)
            return AutoConfig.from_pretrained(model_prefix, **config_dict)
        except Exception as e:
            logger.error(f"Error loading config: {e}")
            return None

    def _load_model(self, model_prefix, config, revision):
        try:
            logger.info(f"Downloading model files for {model_prefix} (revision {revision})...")
            model_files = self._get_model_files(model_prefix, revision)
            if not model_files:
                logger.error(f"No model files found for {model_prefix}")
                return None

            state_dict = {}
            for model_file in model_files:
                logger.info(f"Downloading model file: {model_file}")
                file_path = hf_hub_download(repo_id=model_prefix, filename=model_file, revision=revision)
                with open(file_path, "rb") as f:
                    if model_file.endswith(".safetensors"):
                        shard_state = safetensors.torch.load_file(file_path)
                    elif model_file.endswith(".bin"):
                        shard_state = torch.load(f, map_location="cpu")
                    else:
                        logger.error(f"Unsupported model file type: {model_file}")
                        raise ValueError(f"Unsupported model file type: {model_file}")
                    state_dict.update(shard_state)

            model = AutoModelForCausalLM.from_config(config)
            model.load_state_dict(state_dict)
            return model

        except Exception as e:
            logger.exception(f"Error loading model: {e}")
            return None

    def load_tokenizer_from_stream(self, model_prefix):
        try:
            logger.info(f"Loading tokenizer for {model_prefix}...")
            revision = self._get_latest_revision(model_prefix)
            if revision is None:
                logger.error(f"Could not determine revision for {model_prefix}")
                raise ValueError(f"Could not determine revision for {model_prefix}")

            tokenizer = self._load_tokenizer(model_prefix, revision)
            if tokenizer is None:
                logger.error(f"Failed to load tokenizer for {model_prefix}")
                raise ValueError(f"Failed to load tokenizer for {model_prefix}")
            return tokenizer
        except HTTPException as e:
            logger.error(f"Error loading tokenizer: {e}")
            return None
        except Exception as e:
            logger.exception(f"Unexpected error loading tokenizer: {e}")
            raise HTTPException(status_code=500, detail=f"An unexpected error occurred while loading the tokenizer.")

    def _load_tokenizer(self, model_prefix, revision):
        try:
            logger.info(f"Downloading tokenizer for {model_prefix} (revision {revision})...")
            tokenizer_path = hf_hub_download(repo_id=model_prefix, filename="tokenizer.json", revision=revision)
            return AutoTokenizer.from_pretrained(tokenizer_path)
        except Exception as e:
            logger.error(f"Error loading tokenizer: {e}")
            return None


    def _get_model_files(self, model_prefix, revision="main"):
        try:
            api = HfApi()
            model_files = api.list_repo_files(model_prefix, revision=revision)
            model_files = [file.rfilename for file in model_files if file.rfilename.endswith(('.bin', '.safetensors'))]
            return model_files
        except Exception as e:
            logger.error(f"Error retrieving model files from Hugging Face: {e}")
            raise HTTPException(status_code=500, detail=f"Error retrieving model files from Hugging Face") from e

    def download_and_upload_to_s3_url(self, url, s3_key):
        logger.info(f"Downloading from {url}...")
        with requests.get(url, stream=True) as response:
            if response.status_code == 200:
                total_size_in_bytes = int(response.headers.get('content-length', 0))
                block_size = 1024
                progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
                logger.info(f"Uploading to S3: {s3_key}...")
                self.s3_client.upload_fileobj(response.raw, self.bucket_name, s3_key, Callback=lambda bytes_transferred: progress_bar.update(bytes_transferred))
                progress_bar.close()
                logger.info(f"Uploaded {s3_key} to S3 successfully.")
            elif response.status_code == 404:
                logger.error(f"File not found at {url}")
                raise HTTPException(status_code=404, detail=f"Error downloading file from {url}. File not found.")
            else:
                logger.error(f"Error downloading from {url}: Status code {response.status_code}")
                raise HTTPException(status_code=500, detail=f"Error downloading file from {url}")

    def _get_latest_revision(self, model_prefix):
        try:
            api = HfApi()
            model_info = api.model_info(model_prefix)
            if hasattr(model_info, 'revision'):
                revision = model_info.revision
                if revision:
                    return revision
                else:
                    logger.warning(f"No revision found for {model_prefix}, using 'main'")
                    return "main"
            else:
                logger.warning(f"ModelInfo object for {model_prefix} does not have a 'revision' attribute, using 'main'")
                return "main"
        except Exception as e:
            logger.error(f"Error getting latest revision for {model_prefix}: {e}")
            return None


@app.post("/predict/")
async def predict(model_request: DownloadModelRequest):
    try:
        logger.info(f"Received request: Model={model_request.model_id}, Task={model_request.pipeline_task}, Input={model_request.input_text}")
        model_id = model_request.model_id
        task = model_request.pipeline_task
        input_text = model_request.input_text

        streamer = S3DirectStream(S3_BUCKET_NAME)
        logger.info("Loading model and tokenizer...")
        model = streamer.load_model_from_stream(model_id)

        if model is None:
            logger.error(f"Failed to load model {model_id}")
            raise HTTPException(status_code=500, detail=f"Failed to load model {model_id}")

        tokenizer = streamer.load_tokenizer_from_stream(model_id)
        logger.info("Model and tokenizer loaded.")

        if task not in ["text-generation", "sentiment-analysis", "translation", "fill-mask", "question-answering",  "summarization", "zero-shot-classification"]:
            raise HTTPException(status_code=400, detail="Unsupported pipeline task")

        if task == "text-generation":
            logger.info("Starting text generation...")
            text_streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
            inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
            generation_kwargs = dict(inputs, streamer=text_streamer)
            model.generate(**generation_kwargs)
            logger.info("Text generation finished.")
            return StreamingResponse(iter([tokenizer.decode(token) for token in text_streamer]), media_type="text/event-stream")
        else:
            logger.info(f"Starting pipeline task: {task}...")
            nlp_pipeline = pipeline(task, model=model, tokenizer=tokenizer, device_map="auto", trust_remote_code=True)
            outputs = nlp_pipeline(input_text)
            logger.info(f"Pipeline task {task} finished.")
            return {"result": outputs}

    except Exception as e:
        logger.exception(f"Error processing request: {e}")
        raise HTTPException(status_code=500, detail=f"Error processing request: {str(e)}")

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