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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()
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