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# from fastapi import FastAPI, File, UploadFile
# import numpy as np
# from PIL import Image
# from paddleocr import PaddleOCR
# from doctr.io import DocumentFile
# from doctr.models import ocr_predictor
# import io
# import os
# app = FastAPI()
# # # Load the doctr OCR model
# # os.environ['USE_TORCH'] = 'YES'
# # os.environ['USE_TF'] = 'NO'
# model = ocr_predictor(det_arch='db_resnet50', reco_arch='crnn_vgg16_bn', pretrained=True)
# 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
# def ocr_with_paddle(img):
# finaltext = ''
# ocr = PaddleOCR(lang='en', use_angle_cls=True)
# result = ocr.ocr(img)
# for i in range(len(result[0])):
# text = result[0][i][1][0]
# finaltext += ' ' + text
# return finaltext
# def generate_text_from_image(img):
# text_output = ''
# text_output = ocr_with_paddle(img)
# return text_output
# @app.post("/ocr/")
# async def perform_ocr(file: UploadFile = File(...)):
# file_bytes = await file.read()
# if file.filename.endswith('.pdf'):
# text_output = ocr_with_doctr(io.BytesIO(file_bytes))
# else:
# img = np.array(Image.open(io.BytesIO(file_bytes)))
# text_output = generate_text_from_image(img)
# return {"ocr_text": text_output}
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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