app.py

from typing import List
import pytesseract
from PIL import Image
import gradio as gr
import re

def tesseract_ocr_with_search(filepath: str, languages: List[str], keyword: str):
    # Perform OCR on the image
    image = Image.open(filepath)
    extracted_text = pytesseract.image_to_string(image=image, lang=', '.join(languages))
    
    # If keyword is provided, highlight the occurrences
    if keyword:
        highlighted_text = re.sub(f"({re.escape(keyword)})", r"<mark>\1</mark>", extracted_text, flags=re.IGNORECASE)
    else:
        highlighted_text = extracted_text
    
    return highlighted_text

# Fetch available languages for Tesseract
language_choices = pytesseract.get_languages()

# Define Gradio Interface
demo = gr.Interface(
    fn=tesseract_ocr_with_search, 
    inputs=[
        gr.Image(type="filepath", label="Upload Image"),    # Input for image upload
        gr.CheckboxGroup(language_choices, type="value", value=['eng'], label='Language'),  # Language selection
        gr.Textbox(placeholder="Enter keyword to search", label="Keyword Search")  # Keyword input
        ],
    outputs=gr.HTML(),  # Use HTML output to allow text highlighting
    title=title,
    description=description,
    article=article,
    examples=examples,
)

if __name__ == '__main__':
    demo.launch()
    print("Finished running")