src/OCR.py

import numpy as np
import pandas as pd
import easyocr
import streamlit as st
from PIL import Image
import cv2
from utils.qa import chain
from langchain.memory import ConversationBufferWindowMemory
from langchain_community.chat_message_histories import StreamlitChatMessageHistory

reader = easyocr.Reader(['en', 'hi'], gpu=False)
def I_OCR():
    # Function to display the OCR image with bounding boxes and text
    def display_ocr_image(img, results):
        img_np = np.array(img)
        for detection in results:
            top_left = tuple([int(val) for val in detection[0][0]])
            bottom_right = tuple([int(val) for val in detection[0][2]])
            text = detection[1]
            font = cv2.FONT_HERSHEY_COMPLEX
            cv2.rectangle(img_np, top_left, bottom_right, (0, 255, 0), 5)
            cv2.putText(img_np, text, top_left, font, 1, (125, 29, 241), 2, cv2.LINE_AA)
        st.image(img_np, channels="BGR", use_column_width=True)

    # Function to extract text from DataFrame column
    def extracted_text(col):
        return " , ".join(img_df[col])

    # Function to initialize session state
    def initialize_session_state():
        if "messages" not in st.session_state:
            st.session_state.messages = [
                {"role": "assistant", "content": "Hi! How may I assist you today?"}
            ]

    # Function to get answer from QA model
    def get_answer(query):
        response = chain.invoke(query)
        return response["result"]

    # Streamlit app
    st.title("Question in image")

    file = st.file_uploader(label= "Upload Image Here (png/jpg/jpeg) : ", type=['png', 'jpg', 'jpeg'])

    if file is not None:
        image = Image.open(file)
        st.image(image)
        
        results = reader.readtext(np.array(image))

        img_df = pd.DataFrame(results, columns=['bbox', 'Predicted Text', 'Prediction Confidence'])

        text_combined = extracted_text(col='Predicted Text')
        st.write("Text Generated :- ", text_combined)

        display_ocr_image(image, results)

    else:
        st.warning("!! Please Upload your image !!")

    initialize_session_state()

    memory_storage = StreamlitChatMessageHistory(key="chat_messages")
    memory = ConversationBufferWindowMemory(memory_key="chat_history", human_prefix="User", chat_memory=memory_storage, k=3)

    for i, msg in enumerate(memory_storage.messages):
        name = "user" if i % 2 == 0 else "assistant"
        st.chat_message(name).markdown(msg.content)

    if user_input := st.chat_input("User Input"):
        with st.chat_message("user"):
            st.markdown(user_input)

        with st.spinner("Generating Response..."):
            with st.chat_message("assistant"):
                response = get_answer(user_input)
                answer = response
                st.markdown(answer)

    #if st.sidebar.button("Clear Chat History"):
    #    memory_storage.clear()

# Run the OCR function
#OCR()