app.py

import os
import torch
from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse, StreamingResponse
from pydantic import BaseModel, field_validator
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, GenerationConfig, StoppingCriteriaList
import boto3
import uvicorn
from io import BytesIO
from transformers import pipeline

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")

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()

SPECIAL_TOKENS = {
    "bos_token": "<|startoftext|>",
    "eos_token": "<|endoftext|>",
    "pad_token": "[PAD]",
    "unk_token": "[UNK]",
}

class GenerateRequest(BaseModel):
    model_name: str
    input_text: str = ""
    task_type: str
    temperature: float = 1.0
    max_new_tokens: int = 10
    top_p: float = 1.0
    top_k: int = 50
    repetition_penalty: float = 1.1
    num_return_sequences: int = 1
    do_sample: bool = True
    stop_sequences: list[str] = []
    no_repeat_ngram_size: int = 2

    @field_validator("model_name")
    def model_name_cannot_be_empty(cls, v):
        if not v:
            raise ValueError("model_name cannot be empty.")
        return v

    @field_validator("task_type")
    def task_type_must_be_valid(cls, v):
        valid_types = ["text-to-text", "text-to-image", "text-to-speech", "text-to-video"]
        if v not in valid_types:
            raise ValueError(f"task_type must be one of: {valid_types}")
        return v

    @field_validator("max_new_tokens")
    def max_new_tokens_must_be_within_limit(cls, v):
        if v > 500:
            raise ValueError("max_new_tokens cannot be greater than 500.")
        return v

class S3ModelLoader:
    def __init__(self, bucket_name, s3_client):
        self.bucket_name = bucket_name
        self.s3_client = s3_client

    def _get_s3_uri(self, model_name):
        return f"s3://{self.bucket_name}/{model_name.replace('/', '-')}"

    async def load_model_and_tokenizer(self, model_name):
        s3_uri = self._get_s3_uri(model_name)
        try:
            config = AutoConfig.from_pretrained(s3_uri, local_files_only=True)
            model = AutoModelForCausalLM.from_pretrained(s3_uri, config=config, local_files_only=True)
            tokenizer = AutoTokenizer.from_pretrained(s3_uri, config=config, local_files_only=True)
            tokenizer.add_special_tokens(SPECIAL_TOKENS)
            model.resize_token_embeddings(len(tokenizer))
            if tokenizer.pad_token_id is None:
                tokenizer.pad_token_id = tokenizer.eos_token_id
            return model, tokenizer
        except EnvironmentError:
            try:
                config = AutoConfig.from_pretrained(model_name)
                tokenizer = AutoTokenizer.from_pretrained(model_name, config=config)
                tokenizer.add_special_tokens(SPECIAL_TOKENS)
                model = AutoModelForCausalLM.from_pretrained(model_name, config=config)
                model.resize_token_embeddings(len(tokenizer))
                if tokenizer.pad_token_id is None:
                    tokenizer.pad_token_id = tokenizer.eos_token_id
                model.save_pretrained(s3_uri)
                tokenizer.save_pretrained(s3_uri)
                return model, tokenizer
            except Exception as e:
                raise HTTPException(status_code=500, detail=f"Error loading model: {e}")

model_loader = S3ModelLoader(S3_BUCKET_NAME, s3_client)

@app.post("/generate")
async def generate(request: GenerateRequest):
    try:
        model_name = request.model_name
        input_text = request.input_text
        task_type = request.task_type
        temperature = request.temperature
        max_new_tokens = request.max_new_tokens
        top_p = request.top_p
        top_k = request.top_k
        repetition_penalty = request.repetition_penalty
        num_return_sequences = request.num_return_sequences
        do_sample = request.do_sample
        stop_sequences = request.stop_sequences
        no_repeat_ngram_size = request.no_repeat_ngram_size

        model, tokenizer = await model_loader.load_model_and_tokenizer(model_name)
        device = "cuda" if torch.cuda.is_available() else "cpu"
        model.to(device)

        generation_config = GenerationConfig(
            temperature=temperature,
            max_new_tokens=max_new_tokens,
            top_p=top_p,
            top_k=top_k,
            repetition_penalty=repetition_penalty,
            do_sample=do_sample,
            num_return_sequences=num_return_sequences,
            no_repeat_ngram_size=no_repeat_ngram_size,
            pad_token_id=tokenizer.pad_token_id
        )

        generated_text = generate_text(model, tokenizer, input_text, generation_config, stop_sequences, device)
        return JSONResponse({"text": generated_text})

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")

def generate_text(model, tokenizer, input_text, generation_config, stop_sequences, device):
    max_model_length = model.config.max_position_embeddings
    encoded_input = tokenizer(input_text, return_tensors="pt", max_length=max_model_length, truncation=True).to(device)

    stopping_criteria = StoppingCriteriaList()

    class CustomStoppingCriteria(StoppingCriteriaList):
        def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
            decoded_output = tokenizer.decode(input_ids[0], skip_special_tokens=True)
            for stop in stop_sequences:
                if decoded_output.endswith(stop):
                    return True
            return False

    stopping_criteria.append(CustomStoppingCriteria())

    outputs = model.generate(
        encoded_input.input_ids,
        generation_config=generation_config,
        stopping_criteria=stopping_criteria,
        pad_token_id=generation_config.pad_token_id
    )

    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return generated_text

@app.post("/generate-image")
async def generate_image(request: GenerateRequest):
    try:
        validated_body = request
        device = "cuda" if torch.cuda.is_available() else "cpu"

        image_generator = pipeline("text-to-image", model=validated_body.model_name, device=device)
        image = image_generator(validated_body.input_text)[0]

        img_byte_arr = BytesIO()
        image.save(img_byte_arr, format="PNG")
        img_byte_arr.seek(0)

        return StreamingResponse(img_byte_arr, media_type="image/png")

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")

@app.post("/generate-text-to-speech")
async def generate_text_to_speech(request: GenerateRequest):
    try:
        validated_body = request
        device = "cuda" if torch.cuda.is_available() else "cpu"

        audio_generator = pipeline("text-to-speech", model=validated_body.model_name, device=device)
        audio = audio_generator(validated_body.input_text)[0]

        audio_byte_arr = BytesIO()
        # It is expected that the audio is saved as wav.
        # Saving like this will not always work. Please check how your
        # audio_generator model is working.
        audio_generator.save_audio(audio_byte_arr, audio)
        audio_byte_arr.seek(0)

        return StreamingResponse(audio_byte_arr, media_type="audio/wav")

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")

@app.post("/generate-video")
async def generate_video(request: GenerateRequest):
    try:
        validated_body = request
        device = "cuda" if torch.cuda.is_available() else "cpu"
        video_generator = pipeline("text-to-video", model=validated_body.model_name, device=device)
        video = video_generator(validated_body.input_text)[0]

        video_byte_arr = BytesIO()
        # Same as above. Please check how your video model is returning the
        # videos and save them accordingly.
        # It is expected that the video is saved as MP4
        video_generator.save_video(video_byte_arr, video)
        video_byte_arr.seek(0)

        return StreamingResponse(video_byte_arr, media_type="video/mp4")

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")

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