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import streamlit as st
from paddleocr import PaddleOCR, draw_ocr
from PIL import Image
import numpy as np
from langdetect import detect
from transformers import pipeline
import torch
# Initialize PaddleOCR for multilingual text recognition
ocr = PaddleOCR(use_angle_cls=True, lang='ar') # Using 'ar' to support Arabic scripts like Urdu
# Load summarization model
summarizer = pipeline("summarization")
def recognize_text(image_path):
image = Image.open(image_path)
img_array = np.array(image)
# OCR processing
ocr_results = ocr.ocr(img_array, cls=True)
# Extracting text from OCR results
detected_text = " ".join([line[1][0] for line in ocr_results[0]])
# Language detection and summarization
language = detect(detected_text)
summary = summarizer(detected_text, max_length=50, min_length=25, do_sample=False)[0]['summary_text']
return detected_text, language, summary
def display_ocr_results(image, ocr_results):
boxes = [line[0] for line in ocr_results[0]]
texts = [line[1][0] for line in ocr_results[0]]
scores = [line[1][1] for line in ocr_results[0]]
font_path = "/path/to/font.ttf" # Replace with a valid path to a font supporting Urdu/Arabic
return draw_ocr(np.array(image), boxes, texts, scores, font_path=font_path)
# Streamlit Interface
st.title("Multilingual OCR and Text Summarization App")
st.write("Upload an image or capture one to get OCR results and text summary")
# Image Upload or Capture
image_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
if image_file is not None:
with open("uploaded_image.png", "wb") as f:
f.write(image_file.getbuffer())
st.success("Image uploaded successfully!")
image = Image.open("uploaded_image.png")
st.image(image, caption="Uploaded Image", use_container_width=True)
# Perform OCR and display results
detected_text, language, summary = recognize_text("uploaded_image.png")
st.write("### Detected Text")
st.write(detected_text)
st.write("### Detected Language")
st.write(language)
st.write("### Text Summary")
st.write(summary)
# Display OCR visualization
visualized_image = display_ocr_results(image, ocr.ocr(np.array(image), cls=True))
st.image(visualized_image, caption="OCR Results Visualization", use_container_width=True)
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