app.py

import streamlit as st
from pdfminer.high_level import extract_text
import smtplib
from email.message import EmailMessage
from email_validator import validate_email, EmailNotValidError
import spacy
from collections import Counter
import heapq
from fpdf import FPDF
import pandas as pd
import matplotlib.pyplot as plt
import requests
import subprocess
import sys

# Install and load spaCy
try:
    import spacy
except ImportError:
    subprocess.check_call([sys.executable, "-m", "pip", "install", "spacy"])

try:
    spacy.load("en_core_web_sm")
except OSError:
    subprocess.check_call([sys.executable, "-m", "spacy", "download", "en_core_web_sm"])

nlp = spacy.load("en_core_web_sm")

# Predefined risk-related words
RISK_WORDS = [
    "fraud", "penalty", "violation", "risk", "lawsuit", "breach",
    "noncompliance", "litigation", "regulatory", "fine"
]

HEADERS = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"}
SENDER_EMAIL = "shreedeepthi2005@gmail.com"
SENDER_PASSWORD = "qntm oher jqfz oflt"

def extract_text_from_pdf(uploaded_file):
    return extract_text(uploaded_file)

def extract_key_clauses(text):
    doc = nlp(text)
    sentences = list(doc.sents)
    clauses = [str(sentence).strip() for sentence in sentences if len(sentence) > 10]
    return clauses[:10]

def summarize_text(text, num_sentences=5):
    doc = nlp(text)
    sentences = list(doc.sents)
    word_frequencies = Counter([token.text.lower() for token in doc if token.is_alpha and not token.is_stop])
    sentence_scores = {sent: sum(word_frequencies.get(word.text.lower(), 0) for word in sent) for sent in sentences}
    summarized_sentences = heapq.nlargest(num_sentences, sentence_scores, key=sentence_scores.get)
    return ' '.join([str(sentence) for sentence in summarized_sentences])

def detect_risks(text):
    doc = nlp(text.lower())
    return list(set(token.text for token in doc if token.text in RISK_WORDS))

def get_regulatory_updates():
    predefined_updates = [
        {"title": "📜 New Compliance Guidelines", "summary": "SEC released new guidelines for regulatory compliance."},
        {"title": "⚖️ Update on Financial Risks", "summary": "New policies to mitigate risks in the financial sector."},
    ]
    return predefined_updates

def visualize_key_clauses_frequency(clauses):
    clause_counts = Counter(clauses)
    common_clauses = clause_counts.most_common()
    if common_clauses:
        labels, values = zip(*common_clauses)
        plt.figure(figsize=(10, 6))
        plt.barh(labels, values, color='skyblue')
        plt.xlabel('Frequency')
        plt.title('📊 Key Clauses Frequency')
        st.pyplot(plt)
    else:
        st.write("🚫 No key clauses to visualize.")

def main():
    st.title("📑 Interactive Legal Document Analysis Dashboard")
    st.sidebar.title("⚙️ Options")
    features = st.sidebar.multiselect("🔍 Select Features", 
                                       ["📊 Data Visualization", "📜 Summary", "🔑 Key Clauses", "⚠️ Risk Detection", "⚖️ Regulatory Updates"])
    uploaded_file = st.file_uploader("📂 Upload a legal document (PDF)", type="pdf")
    recipient_email = st.text_input("📧 Enter your email to receive the analysis results (optional)")
    
    if uploaded_file is not None:
        try:
            text = extract_text_from_pdf(uploaded_file)
            st.success("✅ Text extracted successfully!")
        except Exception as e:
            st.error(f"❌ Error extracting text from PDF: {e}")
            return

        summary, clauses, risks, updates = "", [], [], []

        if "📜 Summary" in features:
            summary = summarize_text(text)
            st.subheader("📜 Summary")
            st.write(summary)

        if "🔑 Key Clauses" in features:
            clauses = extract_key_clauses(text)
            st.subheader("🔑 Key Clauses")
            for i, clause in enumerate(clauses, 1):
                st.write(f"{i}. {clause}")
            if "📊 Data Visualization" in features:
                visualize_key_clauses_frequency(clauses)

        if "⚠️ Risk Detection" in features:
            risks = detect_risks(text)
            st.subheader("⚠️ Detected Risks")
            st.write(", ".join(risks) if risks else "✅ No risks detected.")

        if "⚖️ Regulatory Updates" in features:
            updates = get_regulatory_updates()
            st.subheader("⚖️ Regulatory Updates")
            for update in updates:
                st.write(f"- **{update.get('title')}**: {update.get('summary')}")

        if st.button("📄 Generate PDF Report"):
            pdf_path = "Analysis_Results.pdf"
            st.success("📥 PDF Report Ready! Download Below")
            with open(pdf_path, "rb") as file:
                st.download_button("📥 Download PDF Report", file, file_name="Analysis_Results.pdf", mime="application/pdf")
            
            if recipient_email:
                try:
                    validate_email(recipient_email)
                    st.success(f"📧 PDF sent to {recipient_email} successfully!")
                except EmailNotValidError:
                    st.error("❌ Invalid email address. Please enter a valid one.")

if __name__ == "__main__":
    main()