Delete app.py

app.py

@@ -1,147 +0,0 @@
-import re
-import streamlit as st  # Importing required libraries
-from transformers import AutoModel, AutoTokenizer
-import io
-#import logging
-from PIL import Image
-
-# Configure logging for error handling
-#logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
-
-# Helper function for logging and displaying errors
-def handle_error(error_message):
-    #logging.error(error_message)
-    st.error(f"An error occurred: {error_message}")
-
-# Cache the model and tokenizer to avoid reloading on every run
-@st.cache_resource
-def load_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=151643)
-    model.eval()
-    return model, tokenizer
-
-# OCR function using the cached model
-def extract_text(image_bytes):
-    try:
-        # Load the cached model and tokenizer
-        model, tokenizer = load_model()
-
-        # Open the image from bytes in memory and convert to PNG for the model
-        image = Image.open(io.BytesIO(image_bytes))
-        image.save("temp_image.png", format="PNG")
-
-        # Extract text using the cached model
-        res = model.chat(tokenizer, "temp_image.png", ocr_type='ocr')
-        return res
-
-    except Exception as e:
-        handle_error(f"Error during OCR extraction: {str(e)}")
-        return None
-
-# Function to search for the keyword in the extracted text and highlight it in red
-def search_keyword(extracted_text, keyword):
-    # Using regex for case-insensitive and whole-word matching
-    keyword = re.escape(keyword)  # Escape any special characters in the keyword
-    regex_pattern = rf'\b({keyword})\b'  # Match the whole word
-
-    # Count occurrences
-    occurrences = len(re.findall(regex_pattern, extracted_text, flags=re.IGNORECASE))
-
-    # Highlight the keyword in red using HTML
-    highlighted_text = re.sub(regex_pattern, r"<span style='color:red'><b>\1</b></span>", extracted_text, flags=re.IGNORECASE)
-
-    return highlighted_text, occurrences
-
-# Cache the image and OCR results
-@st.cache_data
-def cache_image_ocr(image_bytes):
-    return extract_text(image_bytes)
-
-# Main function for setting up the Streamlit app
-def app():
-    st.set_page_config(
-        page_title="OCR Tool",
-        layout="wide",
-        page_icon=":chart_with_upwards_trend:"
-    )
-    
-    st.header("Optical Character Recognition for English and Hindi Texts")
-    st.write("Upload an image below for OCR:")
-
-    # Initialize session state to store extracted text
-    if 'extracted_text' not in st.session_state:
-        st.session_state.extracted_text = None
-
-    # Create a two-column layout
-    col1, col2 = st.columns([1, 1])  # Equal width columns
-
-    with col1:
-        st.subheader("Upload and OCR Extraction")
-        # File uploader with exception handling
-        uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"], accept_multiple_files=False)
-        
-        if uploaded_file is not None:
-            # Displaying uploaded image
-            st.image(uploaded_file, caption='Uploaded Image', use_column_width=True)
-
-            # Convert uploaded file to bytes
-            image_bytes = uploaded_file.read()
-
-            # Use cache to store the OCR results
-            if st.session_state.extracted_text is None:
-                with st.spinner("Extracting the text..."):
-                    # Cache the OCR result
-                    extracted_text = cache_image_ocr(image_bytes)
-
-                    if extracted_text:
-                        st.success("Text extraction completed!", icon="🎉")
-                        
-                        # Store the extracted text in session state so it doesn't re-run
-                        st.session_state.extracted_text = extracted_text
-                        
-                        st.write("Extracted Text:")
-                        st.write(extracted_text)
-
-                    else:
-                        st.error("Failed to extract text. Please try with a different image.")
-
-            else:
-                # If text is already in session state, just display it
-                st.write("Extracted Text:")
-                st.write(st.session_state.extracted_text)
-
-        else:
-            # Clear extracted text when the image is removed
-            st.session_state.extracted_text = None
-            st.info("Please upload an image file to proceed.")
-
-    # Keyword search functionality (only after text is extracted)
-    with col2:
-        st.subheader("Keyword Search")
-        
-        if st.session_state.extracted_text:
-            keyword = st.text_input("Enter keyword to search")
-
-            if keyword:
-                with st.spinner(f"Searching for '{keyword}'..."):
-                    highlighted_text, occurrences = search_keyword(st.session_state.extracted_text, keyword)
-
-                    if occurrences > 0:
-                        st.success(f"Found {occurrences} occurrences of the keyword '{keyword}'!")
-                        # Display the text with red-colored highlights
-                        st.markdown(highlighted_text, unsafe_allow_html=True)
-                    else:
-                        st.warning(f"No occurrences of the keyword '{keyword}' were found.")
-        else:
-            st.info("Please upload an image and extract text first.")
-
-# Main function to launch the app
-def main():
-    try:
-        app()
-    except Exception as main_error:
-        handle_error(f"Unexpected error in the main function: {str(main_error)}")
-
-if __name__ == "__main__":
-    main()