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 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) reader = easyocr.Reader(['en', 'hi'], gpu=False) 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()