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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())
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