# 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())