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import streamlit as st
from ocr_cpu import extract_text_got # The updated OCR function
import json
# --- UI Styling ---
st.set_page_config(page_title="DualTextOCRFusion",
layout="centered", page_icon="🔍")
st.markdown(
"""
<style>
.reportview-container {
background: #f4f4f4;
}
.sidebar .sidebar-content {
background: #e0e0e0;
}
h1 {
color: #007BFF;
}
.upload-btn {
background-color: #007BFF;
color: white;
padding: 10px;
border-radius: 5px;
text-align: center;
}
</style>
""", unsafe_allow_html=True
)
# --- Title ---
st.title("🔍 DualTextOCRFusion")
st.write("Upload an image with **Hindi** and **English** text to extract and search for keywords.")
# --- Image Upload Section ---
uploaded_file = st.file_uploader(
"Choose an image file", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
st.image(uploaded_file, caption='Uploaded Image', use_column_width=True)
# Extract text from the image using the selected OCR function (GOT)
with st.spinner("Extracting text using the model..."):
try:
extracted_text = extract_text_got(
uploaded_file) # Pass uploaded_file directly
if not extracted_text.strip():
st.warning("No text extracted from the image.")
except Exception as e:
st.error(f"Error during text extraction: {str(e)}")
extracted_text = ""
# Display extracted text
st.subheader("Extracted Text")
st.text_area("Text", extracted_text, height=250)
# Save extracted text for search
if extracted_text:
with open("extracted_text.json", "w") as json_file:
json.dump({"text": extracted_text}, json_file)
# --- Keyword Search ---
st.subheader("Search for Keywords")
keyword = st.text_input(
"Enter a keyword to search in the extracted text")
if keyword:
if keyword.lower() in extracted_text.lower():
st.success(f"Keyword **'{keyword}'** found in the text!")
else:
st.error(f"Keyword **'{keyword}'** not found.")
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