Create app.py

app.py

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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"The uploaded file saved  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",
+    max_pages=20,
+):
+    """
+    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
+    """
+    # clear local text cache
+    rm_local_text_files()
+    global ocr_model
+    st = time.perf_counter()
+    if isinstance(pdf_obj, list):
+        pdf_obj = pdf_obj[0]
+    file_path = Path(pdf_obj.name)
+    if not file_path.suffix == ".pdf":
+        logging.error(f"File {file_path} is not a PDF file")
+
+        html_error = f"""
+        <div style="color: red; font-size: 20px; font-weight: bold;">
+        File {file_path} is not a PDF file. Please upload a PDF file.
+        </div>
+        """
+        return "File is not a PDF file", html_error, None
+
+    conversion_stats = convert_PDF_to_Text(
+        file_path,
+        ocr_model=ocr_model,
+        max_pages=max_pages,
+    )
+    converted_txt = conversion_stats["converted_text"]
+    num_pages = conversion_stats["num_pages"]
+    was_truncated = conversion_stats["truncated"]
+    # if alt_lang: # TODO: fix this
+
+    rt = round((time.perf_counter() - st) / 60, 2)
+    print(f"Runtime: {rt} minutes")
+    html = ""
+    if was_truncated:
+        html += f"<p>WARNING - PDF was truncated to {max_pages} pages</p>"
+    html += f"<p>Runtime: {rt} minutes on CPU for {num_pages} pages</p>"
+
+    _output_name = f"RESULT_{file_path.stem}_OCR.txt"
+    with open(_output_name, "w", encoding="utf-8", errors="ignore") as f:
+        f.write(converted_txt)
+
+    return converted_txt, html, _output_name
+
+
+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 / "exampler.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 for end-to-end text detection and recognition where the input will be in pdf format and the result is text conversion using OCR from the [doctr](https://mindee.github.io/doctr/index.html) package"
+        )
+        gr.Markdown("---")