app.py

import torch
import onnx
import onnxruntime as rt
from torchvision import transforms as T
from PIL import Image
from tokenizer_base import Tokenizer
import pathlib
import os
import gradio as gr
from huggingface_hub import Repository



model_file = "captcha.onnx"
img_size = (32,128)
charset = r"0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~"
tokenizer_base = Tokenizer(charset)

def get_transform(img_size):
        transforms = []
        transforms.extend([
            T.Resize(img_size, T.InterpolationMode.BICUBIC),
            T.ToTensor(),
            T.Normalize(0.5, 0.5)
        ])
        return T.Compose(transforms)

def to_numpy(tensor):
    return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()

def initialize_model(model_file):
    transform = get_transform(img_size)
    # Onnx model loading
    onnx_model = onnx.load(model_file)
    onnx.checker.check_model(onnx_model)
    ort_session = rt.InferenceSession(model_file)
    return transform,ort_session 

def get_text(img_org):
    # img_org = Image.open(image_path)
    # Preprocess. Model expects a batch of images with shape: (B, C, H, W)
    x = transform(img_org.convert('RGB')).unsqueeze(0)

    # compute ONNX Runtime output prediction
    ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(x)}
    logits = ort_session.run(None, ort_inputs)[0]
    probs = torch.tensor(logits).softmax(-1)
    preds, probs = tokenizer_base.decode(probs)
    preds = preds[0]
    print(preds)
    return preds

transform,ort_session = initialize_model(model_file=model_file)

def get_text(image):
    # Ваша функция для обработки изображения
    return "Текст с изображения"

# Создание интерфейса
with gr.Blocks() as demo:
    image_input = gr.Image(type="pil")
    text_output = gr.Textbox()
    
    # Кнопка для обработки изображения
    submit_button = gr.Button("Распознать текст")
    
    # Связываем функцию с кнопкой
    submit_button.click(fn=get_text, inputs=image_input, outputs=text_output)

# Запуск с включенной поддержкой очереди
demo.queue().launch()

# if __name__ == "__main__":
#     image_path = "8000.png"
#     preds,probs = get_text(image_path)
#     print(preds[0])