app.py

from transformers import AutoTokenizer
from transformers import AutoModelForSeq2SeqLM
import streamlit as st
import fitz  # PyMuPDF
from docx import Document
import re
import nltk
from presidio_analyzer import AnalyzerEngine, PatternRecognizer, RecognizerResult, Pattern
nltk.download('punkt')


def sentence_tokenize(text):
    sentences = nltk.sent_tokenize(text)
    return sentences

model_dir_large = 'edithram23/Redaction_Personal_info_v1'
tokenizer_large = AutoTokenizer.from_pretrained(model_dir_large)
model_large = AutoModelForSeq2SeqLM.from_pretrained(model_dir_large)

# model_dir_small = 'edithram23/Redaction'
# tokenizer_small = AutoTokenizer.from_pretrained(model_dir_small)
# model_small = AutoModelForSeq2SeqLM.from_pretrained(model_dir_small)

# def small(text, model=model_small, tokenizer=tokenizer_small):
#     inputs = ["Mask Generation: " + text.lower() + '.']
#     inputs = tokenizer(inputs, max_length=256, truncation=True, return_tensors="pt")
#     output = model.generate(**inputs, num_beams=8, do_sample=True, max_length=len(text))
#     decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
#     predicted_title = decoded_output.strip()
#     pattern = r'\[.*?\]'
#     redacted_text = re.sub(pattern, '[redacted]', predicted_title)
#     return redacted_text

# Initialize the analyzer engine
analyzer = AnalyzerEngine()

# Define a custom address recognizer using a regex pattern
address_pattern = Pattern(name="address", regex=r"\d+\s\w+\s(?:street|st|road|rd|avenue|ave|lane|ln|drive|dr|blvd|boulevard)\s*\w*", score=0.5)
address_recognizer = PatternRecognizer(supported_entity="ADDRESS", patterns=[address_pattern])

# Add the custom address recognizer to the analyzer
analyzer.registry.add_recognizer(address_recognizer)
analyzer.get_recognizers
# Define a function to extract entities
def extract_entities(text):
    entities = {
        "NAME": [],
        "PHONE_NUMBER": [],
        "EMAIL": [],
        "ADDRESS": [],
        "LOCATION": [],
        "IN_AADHAAR": [],
    }
    output = []

    # Analyze the text for PII
    results = analyzer.analyze(text=text, language='en')

    for result in results:
        if result.entity_type == "PERSON":
            entities["NAME"].append(text[result.start:result.end])
            output+=[text[result.start:result.end]]
        elif result.entity_type == "PHONE_NUMBER":
            entities["PHONE_NUMBER"].append(text[result.start:result.end])
            output+=[text[result.start:result.end]]
        elif result.entity_type == "EMAIL_ADDRESS":
            entities["EMAIL"].append(text[result.start:result.end])
            output+=[text[result.start:result.end]]
        elif result.entity_type == "ADDRESS":
            entities["ADDRESS"].append(text[result.start:result.end])
            output+=[text[result.start:result.end]]
        elif result.entity_type == 'LOCATION':
          entities['LOCATION'].append(text[result.start:result.end])
          output+=[text[result.start:result.end]]
        elif result.entity_type == 'IN_AADHAAR':
          entities['IN_PAN'].append(text[result.start:result.end])
          output+=[text[result.start:result.end]]

    return entities,output
    
def mask_generation(text, model=model_large, tokenizer=tokenizer_large):
    if len(text) < 90:
        text = text + '.'
        # return small(text)
    inputs = ["Mask Generation: " + text.lower() + '.']
    inputs = tokenizer(inputs, max_length=512, truncation=True, return_tensors="pt")
    output = model.generate(**inputs, num_beams=8, do_sample=True, max_length=len(text))
    decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
    predicted_title = decoded_output.strip()
    pattern = r'\[.*?\]'
    redacted_text = re.sub(pattern, '[redacted]', predicted_title)
    return redacted_text

def redact_text(page, text):
    text_instances = page.search_for(text)
    for inst in text_instances:
        page.add_redact_annot(inst, fill=(0, 0, 0))
    page.apply_redactions()

def read_pdf(file):
    pdf_document = fitz.open(stream=file.read(), filetype="pdf")
    text = ""
    for page_num in range(len(pdf_document)):
        page = pdf_document.load_page(page_num)
        text += page.get_text()
    return text, pdf_document

def read_docx(file):
    doc = Document(file)
    text = "\n".join([para.text for para in doc.paragraphs])
    return text

def read_txt(file):
    text = file.read().decode("utf-8")
    return text

def process_file(file):
    if file.type == "application/pdf":
        return read_pdf(file)
    elif file.type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document":
        return read_docx(file), None
    elif file.type == "text/plain":
        return read_txt(file), None
    else:
        return "Unsupported file type.", None

st.title("Redaction")
uploaded_file = st.file_uploader("Upload a file", type=["pdf", "docx", "txt"])

if uploaded_file is not None:
    file_contents, pdf_document = process_file(uploaded_file)
    if pdf_document:
        redacted_text = []
        for pg in pdf_document:
              text = pg.get_text('text')
              sentences = sentence_tokenize(text)
              for sent in sentences:
                entities,words_out = extract_entities(sent)
                avai_red = pg.search_for(sent)
                new=[]
                for w in words_out:
                  
                  new+=w.split('\n')
                words_out = [i for i in new if len(i)>2]
                print(words_out)
                for i in avai_red:
                        b = pg.get_text("text", clip=i)
                        # result = [item for item in output if item in b]  # Get elements of 'a' that are in 'b'
                        for j in words_out:
                            new_n = pg.search_for(j, clip=i)
                            for all in new_n:
                              pg.add_redact_annot(all,fill=(0, 0, 0))
              pg.apply_redactions()
            
        output_pdf = "output_redacted.pdf"
        pdf_document.save(output_pdf)

        with open(output_pdf, "rb") as file:
            st.download_button(
                label="Download Processed PDF",
                data=file,
                file_name="processed_file.pdf",
                mime="application/pdf",
            )
    else:
        token = sentence_tokenize(file_contents)
        final = ''
        for i in range(0, len(token)):
            final += mask_generation(token[i]) + '\n'
        processed_text = final
        st.text_area("OUTPUT", processed_text, height=400)
        st.download_button(
            label="Download Processed File",
            data=processed_text,
            file_name="processed_file.txt",
            mime="text/plain",
        )