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| import gradio as gr | |
| from transformers import TrOCRProcessor, VisionEncoderDecoderModel | |
| import requests | |
| from PIL import Image | |
| import numpy as np | |
| import cv2 | |
| processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") | |
| model = VisionEncoderDecoderModel.from_pretrained("aico/TrOCR-MNIST") | |
| def process_image(image): | |
| #print(np.shape(image)) | |
| #print(image) | |
| #rint(image.astype('uint8')) | |
| #cv2.imwrite("image.png",image.astype('uint8'),(28, 28)) | |
| img = Image.fromarray(image.astype('uint8')).convert("RGB") | |
| #img = Image.open("image.png").convert("RGB") | |
| print(img) | |
| # prepare image | |
| pixel_values = processor(img, return_tensors="pt").pixel_values | |
| # generate (no beam search) | |
| generated_ids = model.generate(pixel_values) | |
| # decode | |
| generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| return generated_text | |
| title = "Interactive demo: Single Digits MNIST" | |
| description = "Aico - University Utrecht" | |
| iface = gr.Interface(fn=process_image, | |
| inputs="sketchpad", | |
| outputs="label", | |
| title = title, | |
| description = description) | |
| iface.launch(debug=True) |