Upload app.py

app.py

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+import streamlit as st
+from PIL import Image
+import pytesseract
+import re
+
+def highlight_text(text, keyword):
+    escaped_key = re.escape(keyword)
+    highlighted_text = re.sub(f'({escaped_key})', r'<mark>\1</mark>', text, flags = re.IGNORECASE)
+    return highlighted_text
+
+st.title('OCR Document Search Web App')
+st.divider()
+
+'''
+def got_ocr(image_path):
+    from transformers import AutoModel, AutoTokenizer
+    tokenizer = AutoTokenizer.from_pretrained("stepfun-ai/GOT-OCR2_0", trust_remote_code=True)
+    model = AutoModel.from_pretrained("stepfun-ai/GOT-OCR2_0", trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
+    model=model.eval().cuda()
+    image = Image.open(image_path)
+    res = model.chat(tokenizer, image, ocr_type='ocr')
+    return res
+'''
+
+uploaded_img = st.file_uploader('Upload an image', type=['jpg', 'jpeg', 'png'])
+
+if uploaded_img is not None:
+    image = Image.open(uploaded_img)
+
+    st.image(image, caption='Uploaded image', use_column_width=True)
+
+    extracted_text = pytesseract.image_to_string(image, lang='eng+hin')
+
+    st.subheader('Extracted text')
+    st.divider()
+
+    st.text(extracted_text)
+
+    st.divider()
+
+    search_query = st.text_input('Enter a keyword to search in the extracted text - ')
+
+    if search_query:
+
+        highlighted_text = highlight_text(extracted_text, search_query)
+
+        st.subheader('Text with Highlighted Keyword')
+        st.markdown(highlighted_text, unsafe_allow_html=True)