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| # import gradio as gr | |
| # import tensorflow as tf | |
| # import requests | |
| # import os | |
| # import numpy as np | |
| # import pandas as pd | |
| # import huggingface_hub | |
| # from huggingface_hub import Repository | |
| # from datetime import datetime | |
| # import scipy.ndimage.interpolation as inter | |
| # import datasets | |
| # from datasets import load_dataset, Image | |
| # from PIL import Image | |
| # from paddleocr import PaddleOCR | |
| # from doctr.io import DocumentFile | |
| # os.environ["CUDA_VISIBLE_DEVICES"] = "0" # Use GPU 0, adjust if needed | |
| # os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true" | |
| # from doctr.models import ocr_predictor | |
| # model = ocr_predictor(det_arch='db_resnet50', reco_arch='crnn_vgg16_bn', pretrained=True) | |
| # """ | |
| # Perform OCR with doctr | |
| # """ | |
| # def ocr_with_doctr(file): | |
| # text_output = '' | |
| # # Load the document | |
| # doc = DocumentFile.from_pdf(file) | |
| # # Perform OCR | |
| # result = ocr_model(doc) | |
| # # Extract text from OCR result | |
| # for page in result.pages: | |
| # for block in page.blocks: | |
| # for line in block.lines: | |
| # text_output += " ".join([word.value for word in line.words]) + "\n" | |
| # return text_output | |
| # """ | |
| # Paddle OCR | |
| # """ | |
| # def ocr_with_paddle(img): | |
| # finaltext = '' | |
| # ocr = PaddleOCR(lang='en', use_angle_cls=True, use_gpu=True) | |
| # # img_path = 'exp.jpeg' | |
| # result = ocr.ocr(img) | |
| # for i in range(len(result[0])): | |
| # text = result[0][i][1][0] | |
| # finaltext += ' '+ text | |
| # return finaltext | |
| # def generate_ocr(Method, file): | |
| # text_output = '' | |
| # if isinstance(file, bytes): # Handle file uploaded as bytes | |
| # file = io.BytesIO(file) | |
| # if file.name.endswith('.pdf'): | |
| # # Perform OCR on the PDF using doctr | |
| # text_output = ocr_with_doctr(file) | |
| # else: | |
| # # Handle image file | |
| # img_np = np.array(Image.open(file)) | |
| # text_output = generate_text_from_image(Method, img_np) | |
| # return text_output | |
| # def generate_text_from_image(Method, img): | |
| # text_output = '' | |
| # if Method == 'PaddleOCR': | |
| # text_output = ocr_with_paddle(img) | |
| # return text_output | |
| # import gradio as gr | |
| # image_or_pdf = gr.File(label="Upload an image or PDF") | |
| # method = gr.Radio(["PaddleOCR"], value="PaddleOCR") | |
| # output = gr.Textbox(label="Output") | |
| # demo = gr.Interface( | |
| # generate_ocr, | |
| # [method, image_or_pdf], | |
| # output, | |
| # title="Optical Character Recognition", | |
| # css=".gradio-container {background-color: lightgray} #radio_div {background-color: #FFD8B4; font-size: 40px;}", | |
| # article="""<p style='text-align: center;'>Feel free to give us your thoughts on this demo and please contact us at | |
| # <a href="mailto:[email protected]" target="_blank">[email protected]</a> | |
| # <p style='text-align: center;'>Developed by: <a href="https://www.pragnakalp.com" target="_blank">Pragnakalp Techlabs</a></p>""" | |
| # ) | |
| # demo.launch(share=True) | |
| import paddle | |
| print("PaddlePaddle Version:", paddle.__version__) | |
| print("Is GPU available:", paddle.is_compiled_with_cuda()) | |
| import tensorflow as tf | |
| print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU'))) | |
| import tensorflow as tf | |
| print(tf.test.is_built_with_cuda()) | |