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import logging
import time
from pathlib import Path
import contextlib
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
)
import gradio as gr
import nltk
import torch
from pdf2text import *
_here = Path(__file__).parent
nltk.download("stopwords") # TODO=find where this requirement originates from
def load_uploaded_file(file_obj, temp_dir: Path = None):
"""
load_uploaded_file - process an uploaded file
Args:
file_obj (POTENTIALLY list): Gradio file object inside a list
Returns:
str, the uploaded file contents
"""
# check if mysterious file object is a list
if isinstance(file_obj, list):
file_obj = file_obj[0]
file_path = Path(file_obj.name)
if temp_dir is None:
_temp_dir = _here / "temp"
_temp_dir.mkdir(exist_ok=True)
try:
pdf_bytes_obj = open(file_path, "rb").read()
temp_path = temp_dir / file_path.name if temp_dir else file_path
# save to PDF file
with open(temp_path, "wb") as f:
f.write(pdf_bytes_obj)
logging.info(f"Saved uploaded file to {temp_path}")
return str(temp_path.resolve())
except Exception as e:
logging.error(f"Trying to load file with path {file_path}, error: {e}")
print(f"Trying to load file with path {file_path}, error: {e}")
return None
def convert_PDF(pdf_obj, language: str = "en"):
"""
convert_PDF - convert a PDF file to text
Args:
pdf_bytes_obj (bytes): PDF file contents
language (str, optional): Language to use for OCR. Defaults to "en".
Returns:
str, the PDF file contents as text
"""
global ocr_model
st = time.perf_counter()
if isinstance(pdf_obj, list):
pdf_obj = pdf_obj[0]
file_path = Path(pdf_obj.name)
conversion_stats = convert_PDF_to_Text(
file_path,
ocr_model=ocr_model,
max_pages=20,
)
converted_txt = conversion_stats["converted_text"]
num_pages = conversion_stats["num_pages"]
# if alt_lang: # TODO: fix this
rt = round((time.perf_counter() - st) / 60, 2)
print(f"Runtime: {rt} minutes")
html = ""
html += f"<p>Runtime: {rt} minutes on CPU for {num_pages} pages</p>"
return converted_txt, html
if __name__ == "__main__":
logging.info("Starting app")
use_GPU = torch.cuda.is_available()
logging.info(f"Using GPU status: {use_GPU}")
logging.info("Loading OCR model")
with contextlib.redirect_stdout(None):
ocr_model = ocr_predictor(
"db_resnet50",
"crnn_mobilenet_v3_large",
pretrained=True,
assume_straight_pages=True,
)
# define pdf bytes as None
pdf_obj = _here / "example_file.pdf"
pdf_obj = str(pdf_obj.resolve())
_temp_dir = _here / "temp"
_temp_dir.mkdir(exist_ok=True)
logging.info("starting demo")
demo = gr.Blocks()
with demo:
gr.Markdown("# PDF to Text")
gr.Markdown("A basic demo of pdf-to-text conversion using OCR from the [doctr](https://mindee.github.io/doctr/index.html) package")
gr.Markdown("---")
with gr.Column():
gr.Markdown("## Load Inputs")
gr.Markdown("Upload your own file:")
gr.Markdown("_If no file is uploaded, a sample PDF will be used_")
pdf_obj = gr.Textbox(
lines=1,
label="VM file path",
placeholder="When the file is uploaded, the path will appear here",
value=pdf_obj,
)
with gr.Row():
uploaded_file = gr.File(
label="Upload a PDF file",
file_count="single",
type="file",
value= _here / "example_file.pdf",
)
load_file_button = gr.Button("Load Uploaded File")
gr.Markdown("---")
with gr.Column():
gr.Markdown("## Convert PDF to Text")
convert_button = gr.Button("Convert PDF!")
out_placeholder = gr.HTML("<p><em>Output will appear below:</em></p>")
gr.Markdown("### Output")
OCR_text = gr.Textbox(
label="OCR Result", placeholder="The OCR text will appear here"
)
load_file_button.click(
fn=load_uploaded_file, inputs=uploaded_file, outputs=[pdf_obj]
)
convert_button.click(
fn=convert_PDF, inputs=[pdf_obj], outputs=[OCR_text, out_placeholder]
)
demo.launch(enable_queue=True)
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