# 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:letstalk@pragnakalp.com" target="_blank">letstalk@pragnakalp.com</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())