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import gradio as gr
from transformers import AutoProcessor, AutoModelForCausalLM
from pdf2image import convert_from_path
import base64
import io
from PIL import Image
# Load the OCR model and processor from Hugging Face
processor = AutoProcessor.from_pretrained("allenai/olmOCR-7B-0225-preview")
model = AutoModelForCausalLM.from_pretrained("allenai/olmOCR-7B-0225-preview")
def process_pdf(pdf_file):
"""
Process the uploaded PDF file, extract text from each page, and generate HTML
to display each page's image and text with copy buttons.
"""
# Check if a PDF file was uploaded
if pdf_file is None:
return "<p>Please upload a PDF file.</p>"
# Convert PDF to images
try:
pages = convert_from_path(pdf_file.name)
except Exception as e:
return f"<p>Error converting PDF to images: {str(e)}</p>"
# Start building the HTML output
html = '<div><button onclick="copyAll()" style="margin-bottom: 10px;">Copy All</button></div><div id="pages">'
# Process each page
for i, page in enumerate(pages):
# Convert the page image to base64 for embedding in HTML
buffered = io.BytesIO()
page.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue()).decode()
img_data = f"data:image/png;base64,{img_str}"
# Extract text from the page using the OCR model
try:
inputs = processor(text="Extract the text from this image.", images=page, return_tensors="pt")
outputs = model.generate(**inputs)
text = processor.decode(outputs[0], skip_special_tokens=True)
except Exception as e:
text = f"Error extracting text: {str(e)}"
# Generate HTML for this page's section
textarea_id = f"text{i+1}"
html += f'''
<div class="page" style="margin-bottom: 20px; border-bottom: 1px solid #ccc; padding-bottom: 20px;">
<h3>Page {i+1}</h3>
<div style="display: flex; align-items: flex-start;">
<img src="{img_data}" alt="Page {i+1}" style="max-width: 300px; margin-right: 20px;">
<div style="flex-grow: 1;">
<textarea id="{textarea_id}" rows="10" style="width: 100%;">{text}</textarea>
<button onclick="copyText('{textarea_id}')" style="margin-top: 5px;">Copy</button>
</div>
</div>
</div>
'''
# Close the pages div and add JavaScript for copy functionality
html += '</div>'
html += '''
<script>
function copyText(id) {
var text = document.getElementById(id);
text.select();
document.execCommand("copy");
}
function copyAll() {
var texts = document.querySelectorAll("#pages textarea");
var allText = Array.from(texts).map(t => t.value).join("\\n\\n");
navigator.clipboard.writeText(allText);
}
</script>
'''
return html
# Define the Gradio interface
with gr.Blocks(title="PDF Text Extractor") as demo:
gr.Markdown("# PDF Text Extractor")
gr.Markdown("Upload a PDF file and click 'Extract Text' to see each page's image and extracted text.")
with gr.Row():
pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
submit_btn = gr.Button("Extract Text")
output_html = gr.HTML()
submit_btn.click(fn=process_pdf, inputs=pdf_input, outputs=output_html)
# Launch the interface
demo.launch() |