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import gradio as gr
import pdf_to_image
import image_to_text
from ml_engine.model_functions import is_it_title
def process_pdf(pdf):
# Ensure we get the correct path to the uploaded file
pdf_path = pdf.name # `pdf` is now a NamedString/TempFile with a `.name` attribute
pdf_pages_images = pdf_to_image.pdfToImg2(pdf_path)
pages = []
curr_pg = ""
for img in pdf_pages_images:
text = image_to_text.img2string(img)
for line in text.split("\n"):
if(len(line) == 0): continue
if(is_it_title(line)):
# print(f"TITLE FOUND: {line}") #Debug statement
if(len(curr_pg) != 0):
pages.append(curr_pg)
curr_pg = ""
curr_pg = (curr_pg + line + "\n")
pages.append(curr_pg)
# print(pages)
return pages # Returning a list of strings
# Gradio interface using latest syntax
with gr.Blocks() as demo:
gr.Markdown("# PDF to Pages Processor")
gr.Markdown("Upload a PDF and get a list of extracted pages as output.")
# pdf_input = gr.File(label="Upload a PDF", file_types=[".pdf"])
pdf_input = gr.File(label="Upload a PDF")
output = gr.JSON(label="Extracted Pages")
submit_button = gr.Button("Process PDF")
# Define interaction
submit_button.click(fn=process_pdf, inputs=pdf_input, outputs=output)
if __name__ == "__main__":
demo.launch()
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