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""" |
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#App: NLP App with Streamlit |
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Description |
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This is a Natural Language Processing(NLP) base Application that is useful for |
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Document/Text Summarization from Bangla images and English Images/PDF files. |
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""" |
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import os |
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os.environ["TOKENIZERS_PARALLELISM"] = "false" |
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import streamlit as st |
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st.set_page_config(page_title="Summarization Tool", layout="wide", initial_sidebar_state="expanded") |
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st.title("Bangla and English Summarizer: Upload Images/Pdf or input texts to summarize!") |
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import torch |
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import docx2txt |
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from PIL import Image |
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from PyPDF2 import PdfFileReader |
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from pdf2image import convert_from_bytes |
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import pdfplumber |
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import pdf2image |
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import requests |
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import cv2 |
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import numpy as np |
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import pytesseract |
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import line_cor |
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import altair as alt |
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from PIL import Image |
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API_URL0 = "https://api-inference.huggingface.co/models/csebuetnlp/mT5_multilingual_XLSum" |
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headers0 = {"Authorization": "Bearer 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"} |
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API_URL1 = "https://api-inference.huggingface.co/models/Michael-Vptn/text-summarization-t5-base" |
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headers1 = {"Authorization": "Bearer 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"} |
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API_URL2 = "https://api-inference.huggingface.co/models/gpt2" |
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headers2 = {"Authorization": "Bearer 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"} |
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def read_pdf(file): |
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pdfReader = PdfFileReader(file) |
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count = pdfReader.numPages |
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all_page_text = " " |
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for i in range(count): |
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page = pdfReader.getPage(i) |
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all_page_text += page.extractText()+" " |
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return all_page_text |
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def engsum(output): |
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def query(payload): |
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response = requests.post(API_URL1, headers=headers1, json=payload) |
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return response.json() |
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out = query({ |
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"inputs": output, |
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"min_length":300 |
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}) |
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if isinstance(out, list) and out[0].get("generated_text"): |
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text_output = out[0]["generated_text"] |
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st.success(text_output) |
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def bansum(text): |
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def query(payload): |
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response = requests.post(API_URL0, headers=headers0, json=payload) |
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return response.json() |
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out = query({"inputs": text, "min_length":300}) |
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if isinstance(out, list) and out[0].get("summary_text"): |
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text_output = out[0]["summary_text"] |
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st.success(text_output) |
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if "photo" not in st.session_state: |
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st.session_state["photo"]="not done" |
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c2, c3 = st.columns([2.5,1]) |
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def change_photo_state(): |
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st.session_state["photo"]="done" |
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message = st.text_input("Type your text here!") |
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camera_photo = c2.camera_input("Capture a photo to summarize: ", on_change=change_photo_state) |
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uploaded_photo = c3.file_uploader("Upload your Images/PDF",type=['jpg','png','jpeg','pdf'], on_change=change_photo_state) |
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if st.session_state["photo"]=="done" or message: |
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if uploaded_photo and uploaded_photo.type=='application/pdf': |
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tet = read_pdf(uploaded_photo) |
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values = st.slider('Select a approximate number of lines to see and summarize',value=[0, len(tet)//(7*100)]) |
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text = tet[values[0]*7*10:values[1]*10*100] if values[0]!=len(tet)//(10*100) else tet[len(tet)//(10*100):] |
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if st.button("English Pdf Summarize"): |
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st.subheader("Selected text for summarize: ") |
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st.success(text) |
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st.subheader("Summarized Text: ") |
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engsum(text) |
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elif uploaded_photo and uploaded_photo.type !='application/pdf': |
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text=None |
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img = Image.open(uploaded_photo) |
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img = img.save("img.png") |
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img = cv2.imread("img.png") |
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st.text("Select the summarization type:") |
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if st.button("BENGALI"): |
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text = pytesseract.image_to_string(img, lang="ben") |
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st.subheader("সারাংশ/সারমর্ম") |
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bansum(text) |
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if st.button("ENGLISH"): |
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text=pytesseract.image_to_string(img) |
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st.subheader("Summarized Text") |
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engsum(text) |
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elif camera_photo: |
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text=None |
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img = Image.open(camera_photo) |
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img = img.save("img.png") |
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img = cv2.imread("img.png") |
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st.text("Select the summarization type:") |
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if st.button("Bangla"): |
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text = pytesseract.image_to_string(img, lang="ben") |
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st.subheader("সারাংশ/সারমর্ম") |
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bansum(text) |
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if st.button("English"): |
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text=pytesseract.image_to_string(img) |
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st.subheader("Summarized Text") |
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engsum(text) |
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else: |
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text=None |
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text = message |
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if st.button("Bangla"): |
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bansum(text) |
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if st.button("English"): |
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engsum(text) |
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