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| import streamlit as st | |
| from PIL import Image | |
| from ocr_utils import extract_text | |
| import numpy as np | |
| # Streamlit application title | |
| st.title("OCR and Keyword Search Application") | |
| st.write("Upload an image containing Hindi and English text to extract and search within the text.") | |
| # File uploader for image | |
| uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file is not None: | |
| # Open the uploaded image using PIL | |
| image = Image.open(uploaded_file) | |
| st.image(image, caption='Uploaded Image', use_column_width=True) | |
| # Convert the image to a NumPy array | |
| image_np = np.array(image) | |
| # Perform OCR on the uploaded image using the utility function | |
| full_text = extract_text(image_np) | |
| # Display the extracted text | |
| st.subheader("Extracted Text") | |
| st.write(full_text) | |
| # Text input for keyword search | |
| keyword = st.text_input("Enter Keyword to Search") | |
| # Highlight the keyword in the extracted text | |
| if keyword: | |
| highlighted_text = full_text.replace( | |
| keyword, f"<mark>{keyword}</mark>") | |
| st.subheader("Highlighted Search Results") | |
| st.markdown(highlighted_text, unsafe_allow_html=True) | |
| else: | |
| st.subheader("Highlighted Search Results") | |
| st.write("No keyword entered for highlighting.") | |