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from typing import Dict, Any |
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from fastapi import FastAPI, File, UploadFile |
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from fastapi.responses import StreamingResponse |
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from PIL import Image |
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import torch |
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from transformers import AutoModelForCausalLM, AutoProcessor |
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from transformers.image_utils import to_numpy_array, PILImageResampling, ChannelDimension |
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from transformers.image_transforms import resize, to_channel_dimension_format |
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import json |
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import io |
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app = FastAPI() |
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class EndpointHandler: |
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def __init__(self, model_path: str): |
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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self.processor = AutoProcessor.from_pretrained(model_path) |
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self.model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.bfloat16).to(self.device) |
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self.image_seq_len = self.model.config.perceiver_config.resampler_n_latents |
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self.bos_token = self.processor.tokenizer.bos_token |
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self.bad_words_ids = self.processor.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids |
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def convert_to_rgb(self, image: Image.Image) -> Image.Image: |
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if image.mode == "RGB": |
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return image |
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image_rgba = image.convert("RGBA") |
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background = Image.new("RGBA", image_rgba.size, (255, 255, 255)) |
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alpha_composite = Image.alpha_composite(background, image_rgba) |
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alpha_composite = alpha_composite.convert("RGB") |
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return alpha_composite |
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def custom_transform(self, image: Image.Image) -> torch.Tensor: |
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image = self.convert_to_rgb(image) |
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image = to_numpy_array(image) |
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image = resize(image, (960, 960), resample=PILImageResampling.BILINEAR) |
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image = self.processor.image_processor.rescale(image, scale=1 / 255) |
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image = self.processor.image_processor.normalize( |
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image, |
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mean=self.processor.image_processor.image_mean, |
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std=self.processor.image_processor.image_std |
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) |
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image = to_channel_dimension_format(image, ChannelDimension.FIRST) |
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return torch.tensor(image) |
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async def generate_responses(self, image: Image.Image): |
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try: |
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inputs = self.processor.tokenizer( |
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f"{self.bos_token}<fake_token_around_image>{'<image>' * self.image_seq_len}<fake_token_around_image>", |
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return_tensors="pt", |
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add_special_tokens=False, |
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) |
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inputs["pixel_values"] = self.processor.image_processor([image], transform=self.custom_transform) |
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inputs = {k: v.to(self.device) for k, v in inputs.items()} |
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generated_ids = self.model.generate(**inputs, bad_words_ids=self.bad_words_ids, max_length=2048, early_stopping=True) |
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generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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yield json.dumps({"label": generated_text, "score": 1.0}) + '\n' |
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except torch.cuda.CudaError as e: |
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yield json.dumps({"error": f"CUDA error: {e}"}) + '\n' |
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except Exception as e: |
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yield json.dumps({"error": f"Unexpected error: {e}"}) + '\n' |
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handler = EndpointHandler(model_path="Jaykintecblic/Html") |
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@app.post("/") |
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async def handle_request(file: UploadFile = File(...)): |
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image = Image.open(io.BytesIO(await file.read())) |
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return StreamingResponse(handler.generate_responses(image), media_type="application/json") |
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if __name__ == "__main__": |
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import uvicorn |
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uvicorn.run(app, host="0.0.0.0", port=8080) |
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