app.py

import os
import torch
from fastapi import FastAPI, HTTPException
from fastapi.responses import StreamingResponse
from pydantic import BaseModel, field_validator
from transformers import (
    AutoConfig,
    pipeline,
    AutoModelForSeq2SeqLM,
    AutoTokenizer,
    GenerationConfig,
    StoppingCriteriaList
)
import boto3
import uvicorn
import asyncio
from io import BytesIO
from transformers import pipeline
import json

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()

class GenerateRequest(BaseModel):
    model_name: str
    input_text: str = ""
    task_type: str
    temperature: float = 1.0
    max_new_tokens: int = 200
    stream: bool = True
    top_p: float = 1.0
    top_k: int = 50
    repetition_penalty: float = 1.0
    num_return_sequences: int = 1
    do_sample: bool = True
    chunk_delay: float = 0.0
    stop_sequences: list[str] = []

    @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

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 = AutoModelForSeq2SeqLM.from_pretrained(s3_uri, config=config, local_files_only=True)
            tokenizer = AutoTokenizer.from_pretrained(s3_uri, config=config, local_files_only=True)

            if tokenizer.eos_token_id is not None and tokenizer.pad_token_id is None:
                tokenizer.pad_token_id = config.pad_token_id or tokenizer.eos_token_id

            return model, tokenizer
        except EnvironmentError:
            try:
                config = AutoConfig.from_pretrained(model_name)
                tokenizer = AutoTokenizer.from_pretrained(model_name, config=config)
                model = AutoModelForSeq2SeqLM.from_pretrained(model_name, config=config)

                if tokenizer.eos_token_id is not None and tokenizer.pad_token_id is None:
                    tokenizer.pad_token_id = config.pad_token_id or 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
        stream = request.stream
        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
        chunk_delay = request.chunk_delay
        stop_sequences = request.stop_sequences

        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,
        )

        return StreamingResponse(
            stream_text(model, tokenizer, input_text,
                         generation_config, stop_sequences,
                         device, chunk_delay),
            media_type="text/plain"
        )
    
    except Exception as e:
        raise HTTPException(status_code=500,
                            detail=f"Internal server error: {str(e)}")


async def stream_text(model, tokenizer, input_text,
                        generation_config, stop_sequences,
                        device, chunk_delay, max_length=2048):
    encoded_input = tokenizer(input_text,
                               return_tensors="pt",
                               truncation=True,
                               max_length=max_length).to(device)
    input_length = encoded_input["input_ids"].shape[1]
    remaining_tokens = max_length - input_length

    if remaining_tokens <= 0:
        yield ""

    generation_config.max_new_tokens = min(
        remaining_tokens, generation_config.max_new_tokens
    )
    
    def find_stop(output_text, stop_sequences):
        for seq in stop_sequences:
            if seq in output_text:
                last_index = output_text.rfind(seq)
                return last_index + len(seq)

        return -1
    
    output_text = ""
    
    while True:
        outputs = model.generate(
            **encoded_input,
            do_sample=generation_config.do_sample,
            max_new_tokens=generation_config.max_new_tokens,
            temperature=generation_config.temperature,
            top_p=generation_config.top_p,
            top_k=generation_config.top_k,
            repetition_penalty=generation_config.repetition_penalty,
            num_return_sequences=generation_config.num_return_sequences,
            output_scores=True,
            return_dict_in_generate=True,
        )
    
        new_text = tokenizer.decode(outputs.sequences[0][len(encoded_input["input_ids"][0]):], skip_special_tokens=True)
        
        output_text += new_text
        
        
        
        stop_index = find_stop(output_text, stop_sequences)
        
        if stop_index != -1:
            final_output = output_text[:stop_index]
            
            
            
            chunked_output = [final_output[i:i+10] for i in range(0, len(final_output), 10)]
            
            for chunk in chunked_output:
              
                yield json.dumps({"text": chunk, "is_end": False}) + "\n"
                await asyncio.sleep(chunk_delay)
                
            yield json.dumps({"text": "", "is_end": True}) + "\n"
            break
                
        else:
            chunked_output = [new_text[i:i+10] for i in range(0, len(new_text), 10)]
            for chunk in chunked_output:
                yield json.dumps({"text": chunk, "is_end": False}) + "\n"
                await asyncio.sleep(chunk_delay)
                
        
        if len(output_text) >= generation_config.max_new_tokens:
            
            chunked_output = [output_text[i:i+10] for i in range(0, len(output_text), 10)]
            
            for chunk in chunked_output:
                yield json.dumps({"text": chunk, "is_end": False}) + "\n"
                await asyncio.sleep(chunk_delay)
            yield json.dumps({"text": "", "is_end": True}) + "\n"
            break

        encoded_input = tokenizer(output_text,
                               return_tensors="pt",
                               truncation=True,
                               max_length=max_length).to(device)

@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()
        audio.save(audio_byte_arr)
        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()
        video.save(video_byte_arr)
        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)