app.py

import streamlit as st
import pandas as pd
import numpy as np
from transformers import BertTokenizer, BertForSequenceClassification
from simple_salesforce import Salesforce
import torch
from PyPDF2 import PdfReader
import re
import seaborn as sns
import matplotlib.pyplot as plt

# Salesforce connection
def connect_to_salesforce():
    sf = Salesforce(
        username='your_username',
        password='your_password',
        security_token='your_security_token',
        domain='login'  # or 'test' for sandbox
    )
    return sf

# Extract text from PDF
def extract_text_from_pdf(pdf_file):
    reader = PdfReader(pdf_file)
    text = ""
    for page in reader.pages:
        text += page.extract_text() + "\n"
    return text

# Split text into clauses
def split_into_clauses(text):
    clauses = re.split(r'\n\s*\d+\.\s*|\n\s*[A-Z]\.\s*', text)
    clauses = [clause.strip() for clause in clauses if clause.strip()]
    return clauses

# Load BERT model and tokenizer
@st.cache_resource
def load_model():
    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
    model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=3)  # Fine-tuned for 3 risk levels
    return tokenizer, model

# Process clauses and assign risk scores
def process_clauses(clauses, tokenizer, model):
    results = []
    risk_levels = {0: 'Low', 1: 'Medium', 2: 'High'}
    
    for clause in clauses:
        inputs = tokenizer(clause, return_tensors="pt", truncation=True, padding=True, max_length=512)
        with torch.no_grad():
            outputs = model(**inputs)
        logits = outputs.logits
        risk_score = torch.softmax(logits, dim=1).numpy()[0]
        risk_level = risk_levels[np.argmax(risk_score)]
        
        results.append({
            'clause_text': clause,
            'risk_level': risk_level,
            'severity_score': float(np.max(risk_score)),
            'clause_type': infer_clause_type(clause)  # Simplified clause type inference
        })
    
    return results

# Simplified clause type inference (extend with more sophisticated logic as needed)
def infer_clause_type(clause):
    if 'liability' in clause.lower():
        return 'Liability'
    elif 'payment' in clause.lower():
        return 'Payment'
    else:
        return 'General'

# Save results to Salesforce
def save_to_salesforce(sf, results, contract_id):
    for result in results:
        sf.Contract_Risk__c.create({
            'Contract__c': contract_id,
            'Clause_Text__c': result['clause_text'][:255],  # Truncate if needed
            'Risk_Level__c': result['risk_level'],
            'Severity_Score__c': result['severity_score'],
            'Clause_Type__c': result['clause_type']
        })

# Generate heatmap
def generate_heatmap(results):
    df = pd.DataFrame(results)
    risk_scores = df['severity_score'].values
    plt.figure(figsize=(10, 2))
    sns.heatmap([risk_scores], cmap='RdYlGn_r', annot=True, fmt='.2f', cbar_kws={'label': 'Risk Severity'})
    plt.title('Contract Clause Risk Heatmap')
    plt.xlabel('Clause Index')
    plt.yticks([])
    st.pyplot(plt)

# Streamlit interface
def main():
    st.title("Contract Risk Analyzer")
    
    # File upload
    uploaded_file = st.file_uploader("Upload Contract PDF", type=["pdf"])
    contract_id = st.text_input("Enter Contract ID")
    
    if uploaded_file and contract_id:
        # Extract and process text
        text = extract_text_from_pdf(uploaded_file)
        clauses = split_into_clauses(text)
        
        # Load model and process clauses
        tokenizer, model = load_model()
        results = process_clauses(clauses, tokenizer, model)
        
        # Display results
        st.subheader("Clause Analysis Results")
        for i, result in enumerate(results, 1):
            st.write(f"**Clause {i}**")
            st.write(f"Text: {result['clause_text'][:100]}...")
            st.write(f"Clause Type: {result['clause_type']}")
            st.write(f"Risk Level: {result['risk_level']}")
            st.write(f"Severity Score: {result['severity_score']:.2f}")
            st.write("---")
        
        # Generate and display heatmap
        generate_heatmap(results)
        
        # Save to Salesforce
        if st.button("Save to Salesforce"):
            sf = connect_to_salesforce()
            save_to_salesforce(sf, results, contract_id)
            st.success("Results saved to Salesforce!")

if __name__ == "__main__":
    main()