app.py

import torch
import torchvision.transforms as T
from PIL import Image
from huggingface_hub import hf_hub_download
from transformers import VisionEncoderDecoderModel
from fastapi import FastAPI, File, UploadFile
from fastapi.responses import HTMLResponse
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates


import warnings
from contextlib import contextmanager
from transformers import MBartTokenizer, ViTImageProcessor, XLMRobertaTokenizer
from transformers import ProcessorMixin


class CustomOCRProcessor(ProcessorMixin):
    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "AutoImageProcessor"
    tokenizer_class = "AutoTokenizer"

    def __init__(self, image_processor=None, tokenizer=None, **kwargs):
        if "feature_extractor" in kwargs:
            warnings.warn(
                "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                " instead.",
                FutureWarning,
            )
            feature_extractor = kwargs.pop("feature_extractor")

        image_processor = image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
            raise ValueError("You need to specify an `image_processor`.")
        if tokenizer is None:
            raise ValueError("You need to specify a `tokenizer`.")

        super().__init__(image_processor, tokenizer)
        self.current_processor = self.image_processor
        self._in_target_context_manager = False

    def __call__(self, *args, **kwargs):
        # For backward compatibility
        if self._in_target_context_manager:
            return self.current_processor(*args, **kwargs)

        images = kwargs.pop("images", None)
        text = kwargs.pop("text", None)
        if len(args) > 0:
            images = args[0]
            args = args[1:]

        if images is None and text is None:
            raise ValueError("You need to specify either an `images` or `text` input to process.")

        if images is not None:
            inputs = self.image_processor(images, *args, **kwargs)
        if text is not None:
            encodings = self.tokenizer(text, **kwargs)

        if text is None:
            return inputs
        elif images is None:
            return encodings
        else:
            inputs["labels"] = encodings["input_ids"]
            return inputs

    def batch_decode(self, *args, **kwargs):
        return self.tokenizer.batch_decode(*args, **kwargs)

    def decode(self, *args, **kwargs):
        return self.tokenizer.decode(*args, **kwargs)


image_processor = ViTImageProcessor.from_pretrained(
    'microsoft/swin-base-patch4-window12-384-in22k'
)
tokenizer = MBartTokenizer.from_pretrained(
    'facebook/mbart-large-50'
)
processortext2 = CustomOCRProcessor(image_processor,tokenizer)


app = FastAPI()
app.mount("/static", StaticFiles(directory="static"), name="static")
templates = Jinja2Templates(directory="templates")

# Download and load the model
model2 = VisionEncoderDecoderModel.from_pretrained("musadac/vilanocr-single-urdu",use_auth_token=True).to(device)


@app.get("/", response_class=HTMLResponse)
async def root():
    return templates.TemplateResponse("index.html", {"request": None})

@app.post("/upload/", response_class=HTMLResponse)
async def upload_image(image: UploadFile = File(...)):
    # Preprocess image
    img = Image.open(image.file).convert("RGB")
    pixel_values = processortext(img.convert("RGB"), return_tensors="pt").pixel_values
    
    # Run the model
    with torch.no_grad():
        generated_ids = model2.generate(img_tensor)
    
    # Extract OCR result
    result = processortext.batch_decode(generated_ids, skip_special_tokens=True)[0]

    return {"result": result}