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app.py

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+import streamlit as st
+from transformers import AutoModel, AutoTokenizer
+import torch
+from PIL import Image
+import tempfile
+import easyocr
+import re
+
+
+# Load the pre-trained English OCR model and tokenizer 
+@st.cache_resource
+def load_english_model():
+    tokenizer = AutoTokenizer.from_pretrained('srimanth-d/GOT_CPU', trust_remote_code=True)
+    model = AutoModel.from_pretrained('srimanth-d/GOT_CPU', trust_remote_code=True, low_cpu_mem_usage=True, use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
+    model = model.eval()
+    return tokenizer, model
+
+# Load the multilingual OCR model (EasyOCR) for both Hindi and English
+@st.cache_resource
+def load_multilingual_model():
+    reader = easyocr.Reader(['en', 'hi'])  
+    return reader
+
+st.title('OCR Web App')
+
+if 'ocr_result' not in st.session_state:
+    st.session_state['ocr_result'] = ""
+
+# Function to highlight the search word in the OCR results
+def highlight_text(text, search_query):
+    if not search_query:
+        return text
+    highlighted = re.sub(f"({re.escape(search_query)})", r'<mark>\1</mark>', text, flags=re.IGNORECASE)
+    return highlighted
+
+# Create a two-column layout
+left_column, right_column = st.columns([1, 2])
+
+with left_column:
+    language = st.selectbox("Select Language", ["English", "Hindi + English"])
+
+    predict_button = st.button('Predict')
+
+    # Search functionality after results
+    search_query = st.text_input("Search in results")
+    search_button = st.button('Search')
+
+    # Display search results by highlighting the searched word
+    if search_button and st.session_state['ocr_result']:
+        if search_query.lower() in st.session_state['ocr_result'].lower():
+            st.success(f"'{search_query}' found in the OCR results!")
+        else:
+            st.error(f"'{search_query}' not found in the OCR results.")
+
+with right_column:
+    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
+
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        img_format = image.format if image.format is not None else "JPEG"
+        with tempfile.NamedTemporaryFile(delete=False, suffix=f".{img_format.lower()}") as tmp_file:
+            img_path = tmp_file.name
+            image.save(img_path, format=img_format)
+
+        # Perform OCR when the Predict button is clicked
+        if predict_button:
+            if language == "English":
+                tokenizer, model = load_english_model()
+                with st.spinner('Processing English OCR...'):
+                    res = model.chat(tokenizer, img_path, ocr_type='ocr')
+                st.session_state['ocr_result'] = res  
+                st.write("OCR Result (English):")
+                st.write(st.session_state['ocr_result'], unsafe_allow_html=True)
+
+            elif language == "Hindi + English":
+                reader = load_multilingual_model()
+                with st.spinner('Processing Hindi + English OCR...'):
+                    result = reader.readtext(img_path, detail=0)  
+                st.session_state['ocr_result'] = " ".join(result)  
+                st.write("OCR Result (Hindi + English):")
+                st.write(st.session_state['ocr_result'], unsafe_allow_html=True)
+
+    if st.session_state['ocr_result']:
+        highlighted_result = highlight_text(st.session_state['ocr_result'], search_query)
+        st.write("Highlighted OCR Result:")
+        st.markdown(highlighted_result, unsafe_allow_html=True)

requirements.txt

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+streamlit==1.25.0
+transformers==4.37.2
+torch==2.0.1
+torchvision==0.15.2
+Pillow
+easyocr==1.7.0
+tiktoken
+verovio
+accelerate==0.28.0