# streamlit_indian_compliance_app.py import streamlit as st import json import os import requests from dotenv import load_dotenv import pandas as pd from datetime import datetime import plotly.express as px import yfinance as yf import time from typing import Optional, Dict, Any, List # Load environment variables load_dotenv() # Configuration API_KEY = os.getenv("SWARMS_API_KEY") BASE_URL = "https://api.swarms.world" # Standard headers for all requests headers = { "x-api-key": API_KEY, "Content-Type": "application/json" } class IndianDataProvider: """Handles integration with Indian financial data APIs""" def __init__(self): pass def get_nse_stock_data(self, symbol: str) -> Dict[str, Any]: """Get NSE stock data using Yahoo Finance with direct metric extraction""" try: # Create ticker with NSE suffix ticker = yf.Ticker(f"{symbol}.NS") info = ticker.info financials = ticker.financials balance_sheet = ticker.balance_sheet cash_flow = ticker.cashflow # Extract key metrics directly from info current_price = info.get("currentPrice") total_revenue = info.get("totalRevenue") net_income = info.get("netIncomeToCommon") total_debt = info.get("totalDebt") return { 'success': True, 'info': info, 'financials': financials.to_dict() if not financials.empty else {}, 'balance_sheet': balance_sheet.to_dict() if not balance_sheet.empty else {}, 'cash_flow': cash_flow.to_dict() if not cash_flow.empty else {}, 'current_price': current_price, 'total_revenue': total_revenue, 'net_income': net_income, 'total_debt': total_debt, 'price_source': 'Yahoo Finance' } except Exception as e: return {"success": False, "error": str(e)} def format_stock_data_for_analysis(self, stock_data: Dict[str, Any]) -> str: """Format NSE stock data for compliance analysis""" if not stock_data.get('success'): return f"Error fetching stock data: {stock_data.get('error', 'Unknown error')}" try: info = stock_data['info'] financials = stock_data['financials'] balance_sheet = stock_data['balance_sheet'] # Use directly extracted metrics current_price = stock_data.get('current_price') total_revenue = stock_data.get('total_revenue') net_income = stock_data.get('net_income') total_debt = stock_data.get('total_debt') # Format currency values def format_currency(value): if value is None: return "N/A" return f"₹{value:,.2f}" def get_total_liabilities(balance_sheet: dict) -> float: for timestamp, financials in balance_sheet.items(): if 'Total Liabilities Net Minority Interest' in financials: return financials['Total Liabilities Net Minority Interest'] return None def get_total_assets(balance_sheet: dict) -> float: for timestamp, financials in balance_sheet.items(): if 'Total Assets' in financials: return financials['Total Assets'] return None total_assets = get_total_assets(balance_sheet) total_liabilities = get_total_liabilities(balance_sheet) shareholders_equity = total_assets - total_liabilities if total_assets and total_liabilities else None def get_ebitda(financials: dict) -> float: for timestamp, data in financials.items(): if 'EBITDA' in data: return data['EBITDA'] return None symbol_ebitda = get_ebitda(financials) def get_gross_profit(financials: dict) -> float: for timestamp, data in financials.items(): if 'Gross Profit' in data: return data['Gross Profit'] return None gross_profit = get_gross_profit(financials) def get_operating_income(financials: dict) -> float: for timestamp, data in financials.items(): if 'Operating Income' in data: return data['Operating Income'] return None operating_income = get_operating_income(financials) def get_operating_revenue(financials: dict) -> float: for timestamp, data in financials.items(): if 'Operating Revenue' in data: return data['Operating Revenue'] return None operating_revenue = get_operating_revenue(financials) def get_total_equity(balance_sheet: dict) -> float: for timestamp, data in balance_sheet.items(): if 'Total Equity Gross Minority Interest' in data: return data['Total Equity Gross Minority Interest'] return None total_equity = get_total_equity(balance_sheet) debt_to_equity = (total_debt / total_equity) if total_debt and total_equity else None formatted_data = f""" COMPANY INFORMATION: Company Name: {info.get('longName', 'N/A')} Symbol: {info.get('symbol', 'N/A')} Sector: {info.get('sector', 'N/A')} Industry: {info.get('industry', 'N/A')} Market Cap: {format_currency(info.get('marketCap'))} Employees: {info.get('fullTimeEmployees', 'N/A')} CURRENT PRICE: {format_currency(current_price)} (Source: Yahoo Finance) FINANCIAL HIGHLIGHTS: 52 Week High: {format_currency(info.get('fiftyTwoWeekHigh'))} 52 Week Low: {format_currency(info.get('fiftyTwoWeekLow'))} P/E Ratio: {info.get('trailingPE', 'N/A')} Book Value: {format_currency(info.get('bookValue'))} Dividend Yield: {info.get('dividendYield', 'N/A')}% FINANCIAL STATEMENTS: Revenue (Latest): {format_currency(total_revenue)} Net Income (Latest): {format_currency(net_income)} Total Assets (Latest): {format_currency(total_assets)} Total Debt (Latest): {format_currency(total_debt)} Total Equity (Latest): {format_currency(total_equity)} Debt to Equity Ratio: {(f"{debt_to_equity:.2f}" if debt_to_equity else "N/A")} Total Liabilities: {format_currency(total_liabilities)} Shareholders Equity: {format_currency(shareholders_equity)} EBITDA: {(format_currency(symbol_ebitda) if symbol_ebitda else "N/A")} Gross Profit: {format_currency(gross_profit)} Operating Income: {format_currency(operating_income)} Operating Revenue: {format_currency(operating_revenue)} BUSINESS SUMMARY: {info.get('longBusinessSummary', 'N/A')} GOVERNANCE: Board Members: {len(info.get('companyOfficers', []))} officers listed Audit Risk: {info.get('auditRisk', 'N/A')} Board Risk: {info.get('boardRisk', 'N/A')} Compensation Risk: {info.get('compensationRisk', 'N/A')} Shareholder Rights Risk: {info.get('shareHolderRightsRisk', 'N/A')} Overall Risk: {info.get('overallRisk', 'N/A')} """ return formatted_data.strip() except Exception as e: return f"Error formatting stock data: {str(e)}" def run_swarm(swarm_config): """Execute a swarm with the provided configuration.""" try: response = requests.post( f"{BASE_URL}/v1/swarm/completions", headers=headers, json=swarm_config ) return response.json() except Exception as e: return {"error": str(e)} def create_indian_compliance_swarm(financial_data, company_info): """Create a swarm for Indian financial compliance assistance.""" DOCUMENTATION_ANALYZER_PROMPT = """ You are a financial documentation specialist with expertise in Indian financial reporting standards and regulations. Your role is to analyze financial statements and disclosures for compliance with Indian requirements. Your tasks include: 1. Reviewing financial statements for completeness under Indian Accounting Standards (Ind AS) or AS 2. Analyzing annual reports and board reports for mandatory disclosures 3. Checking compliance with Companies Act 2013 disclosure requirements 4. Verifying CSR reporting and ESG disclosures as per Indian regulations 5. Ensuring proper disclosure of related party transactions under Indian law 6. Reviewing audit reports and internal financial control assessments 7. Checking compliance with SEBI disclosure norms (for listed companies) 8. Analyzing tax provisions and deferred tax disclosures under Indian tax laws Focus on Indian regulatory framework and provide findings specific to Indian compliance requirements. """ ACCOUNTING_STANDARDS_PROMPT = """ You are an expert in Indian Accounting Standards (Ind AS) and legacy Accounting Standards (AS) with deep knowledge of Indian GAAP requirements. Your responsibility is to ensure financial statements comply with applicable Indian accounting frameworks. Your tasks include: 1. Analyzing compliance with applicable Ind AS or AS standards 2. Reviewing revenue recognition under Ind AS 115 or AS 9 3. Checking financial instrument accounting under Ind AS 109 or AS 30/31/32 4. Evaluating lease accounting under Ind AS 116 or AS 19 5. Reviewing impairment assessments under Ind AS 36 or AS 28 6. Analyzing consolidation requirements under Ind AS 110/111 or AS 21/23/27 7. Checking fair value measurements and disclosures under Ind AS 113 8. Ensuring proper segment reporting under Ind AS 108 or AS 17 9. Reviewing first-time adoption issues for Ind AS transition Reference specific Indian accounting standards and consider MCA notifications and clarifications. """ REGULATORY_COMPLIANCE_PROMPT = """ You are a senior regulatory compliance expert specializing in Indian financial regulations and corporate law. Your expertise covers Companies Act 2013, SEBI regulations, RBI guidelines, and other Indian regulatory frameworks. Your responsibilities include: 1. Ensuring compliance with Companies Act 2013 provisions and rules 2. Verifying SEBI LODR (Listing Obligations and Disclosure Requirements) compliance 3. Checking RBI guidelines compliance (for applicable sectors) 4. Reviewing corporate governance disclosures as per Indian regulations 5. Analyzing CSR compliance and reporting under Section 135 of Companies Act 6. Verifying board composition and audit committee requirements 7. Checking compliance with insider trading regulations (SEBI PIT) 8. Reviewing related party transaction approvals and disclosures 9. Ensuring proper filing requirements with MCA and SEBI 10. Analyzing compliance with sectoral regulations (banking, insurance, etc.) Focus on Indian regulatory environment and recent updates to regulations and compliance requirements. """ swarm_config = { "name": "Indian Financial Compliance Assistant", "description": "A specialized swarm for Indian financial regulatory compliance", "agents": [ { "agent_name": "Indian Documentation Analyzer", "description": "Reviews financial statements for Indian compliance requirements", "system_prompt": DOCUMENTATION_ANALYZER_PROMPT, "model_name": "gpt-4o", "role": "worker", "max_loops": 1, "max_tokens": 4096, "temperature": 0.5, "auto_generate_prompt": False, }, { "agent_name": "Indian Accounting Standards Expert", "description": "Evaluates compliance with Ind AS/AS requirements", "system_prompt": ACCOUNTING_STANDARDS_PROMPT, "model_name": "gpt-4o", "role": "worker", "max_loops": 1, "max_tokens": 4096, "temperature": 0.5, "auto_generate_prompt": False, }, { "agent_name": "Indian Regulatory Compliance Specialist", "description": "Assesses adherence to Indian regulatory frameworks", "system_prompt": REGULATORY_COMPLIANCE_PROMPT, "model_name": "gpt-4o", "role": "worker", "max_loops": 1, "max_tokens": 4096, "temperature": 0.5, "auto_generate_prompt": False, } ], "max_loops": 1, "swarm_type": "SequentialWorkflow", "task": f""" Analyze the following financial information for an Indian company ({company_info}) and provide a comprehensive compliance assessment according to Indian regulations: {financial_data} For your compliance evaluation, provide: 1. Assessment of compliance with Indian Accounting Standards (Ind AS/AS) 2. Analysis of Companies Act 2013 compliance requirements 3. Review of SEBI regulations compliance (if applicable) 4. Evaluation of corporate governance and disclosure requirements 5. Assessment of CSR and ESG reporting compliance 6. Identification of potential compliance risks specific to Indian regulations 7. Recommendations for improving compliance with Indian standards Focus specifically on Indian regulatory framework and recent regulatory updates. """ } return run_swarm(swarm_config) def create_comprehensive_csv_data(data_source, company_info, accounting_standards, regulatory_frameworks, result): """Create comprehensive CSV data with all analysis information""" # Extract company information company_parts = company_info.split(" in the ") company_type = company_parts[0] if len(company_parts) > 0 else "N/A" industry_sector = company_parts[1].split(", classified as")[0] if len(company_parts) > 1 else "N/A" company_size = company_parts[1].split("classified as ")[1].split(", for")[0] if len(company_parts) > 1 else "N/A" financial_year = company_parts[1].split("for ")[-1] if len(company_parts) > 1 else "N/A" # Create comprehensive data structure comprehensive_data = [] # 1. Basic Information comprehensive_data.append({ 'Category': 'Basic Information', 'Field': 'Analysis Timestamp', 'Value': datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'Details': 'Time when analysis was performed', 'Priority': '', 'Timeline': '', 'Regulation': '' }) comprehensive_data.append({ 'Category': 'Basic Information', 'Field': 'Data Source', 'Value': data_source, 'Details': 'Source of financial data used for analysis', 'Priority': '', 'Timeline': '', 'Regulation': '' }) comprehensive_data.append({ 'Category': 'Basic Information', 'Field': 'Company Type', 'Value': company_type, 'Details': 'Legal structure of the company', 'Priority': '', 'Timeline': '', 'Regulation': '' }) comprehensive_data.append({ 'Category': 'Basic Information', 'Field': 'Industry Sector', 'Value': industry_sector, 'Details': 'Primary business sector', 'Priority': '', 'Timeline': '', 'Regulation': '' }) comprehensive_data.append({ 'Category': 'Basic Information', 'Field': 'Company Size', 'Value': company_size, 'Details': 'Classification based on turnover and capital', 'Priority': '', 'Timeline': '', 'Regulation': '' }) comprehensive_data.append({ 'Category': 'Basic Information', 'Field': 'Financial Year', 'Value': financial_year, 'Details': 'Reporting period under analysis', 'Priority': '', 'Timeline': '', 'Regulation': '' }) comprehensive_data.append({ 'Category': 'Basic Information', 'Field': 'Accounting Standards', 'Value': accounting_standards, 'Details': 'Applicable accounting framework', 'Priority': '', 'Timeline': '', 'Regulation': '' }) comprehensive_data.append({ 'Category': 'Basic Information', 'Field': 'Regulatory Frameworks', 'Value': ', '.join(regulatory_frameworks), 'Details': 'Applicable regulatory requirements', 'Priority': '', 'Timeline': '', 'Regulation': '' }) # 2. AI Analysis Results (if available) if isinstance(result, dict) and 'response' in result: comprehensive_data.append({ 'Category': 'AI Analysis Results', 'Field': 'Full Analysis Response', 'Value': 'AI Generated Compliance Analysis', 'Details': result['response'][:1000] + '...' if len(result['response']) > 1000 else result['response'], 'Priority': '', 'Timeline': '', 'Regulation': '' }) return pd.DataFrame(comprehensive_data) def main(): st.set_page_config( page_title="Indian Financial Compliance System", page_icon="🇮đŸ‡ŗ", layout="wide", initial_sidebar_state="expanded" ) # Initialize data provider if 'data_provider' not in st.session_state: st.session_state.data_provider = IndianDataProvider() # Header st.title("🇮đŸ‡ŗ Indian Financial Compliance & Regulatory System") st.markdown("AI-Powered Multi-Agent Financial Compliance Analysis for Indian Companies") # Sidebar Configuration with st.sidebar: st.header("Configuration") # API Key Check if not API_KEY: st.error("SWARMS_API_KEY not found in environment variables") st.info("Please set your API key in the .env file") return else: st.success("API Key configured") st.divider() # Data Source Selection st.subheader("📊 Data Source") data_source = st.selectbox( "Choose Data Source", [ "Manual Input", "NSE Listed Company (Live Data)" ], help="Select how you want to input financial data" ) financial_data = "" company_info_auto = "" # Handle different data sources if data_source == "NSE Listed Company (Live Data)": st.subheader("🔍 NSE Stock Data") # Popular Indian stocks for quick selection popular_stocks = [ 'ADANIENT', 'ADANIPORTS', 'APOLLOHOSP', 'ASIANPAINT', 'AXISBANK', 'BAJAJ-AUTO', 'BAJFINANCE', 'BAJAJFINSV', 'BEL', 'BHARTIARTL', 'CIPL', 'COALINDIA', 'DRREDDY', 'EICHERMOT', 'GRASIM', 'HCLTECH', 'HDFCBANK', 'HDFCLIFE', 'HEROMOTOCO', 'HINDALCO', 'HINDUNILVR', 'ICICIBANK', 'INDUSINDBK', 'INFY', 'ITC', 'JIOFIN', 'JSWSTEEL', 'KOTAKBANK', 'LT', 'M&M', 'MARUTI', 'NESTLEIND', 'NTPC', 'ONGC', 'POWERGRID', 'RELIANCE', 'SBILIFE', 'SHRIRAMFIN', 'SBIN', 'SUNPHARMA', 'TATACONSUM', 'TCS', 'TATAMOTORS', 'TATASTEEL', 'TECHM', 'TITAN', 'TRENT', 'ULTRACEMCO', 'WIPRO' ] col1, col2 = st.columns(2) with col1: stock_symbol = st.selectbox( "Popular NSE Stocks", [""] + popular_stocks, help="Select from popular stocks" ) with col2: custom_symbol = st.text_input( "Enter Custom Symbol", placeholder="e.g., WIPRO", help="Enter NSE stock symbol" ) # Use custom symbol if provided, otherwise use selected symbol_to_fetch = custom_symbol.upper() if custom_symbol else stock_symbol if symbol_to_fetch and st.button("🚀 Fetch Live NSE Data", type="primary"): with st.spinner(f"Fetching live data for {symbol_to_fetch}..."): stock_data = st.session_state.data_provider.get_nse_stock_data(symbol_to_fetch) if stock_data.get('success'): st.success(f"✅ Successfully fetched data for {symbol_to_fetch}") financial_data = st.session_state.data_provider.format_stock_data_for_analysis(stock_data) company_info_auto = f"NSE Listed Company - {stock_data['info'].get('longName', symbol_to_fetch)} in {stock_data['info'].get('sector', 'Unknown')} sector" # Display quick preview with directly extracted metrics with st.expander("📋 Data Preview"): col_prev1, col_prev2 = st.columns(2) with col_prev1: st.metric("Market Cap", f"₹{stock_data['info'].get('marketCap', 0):,}") st.metric("Current Price", f"₹{stock_data.get('current_price', 0):,.2f}") with col_prev2: st.metric("Revenue", f"₹{stock_data.get('total_revenue', 0):,.2f}") st.metric("Net Income", f"₹{stock_data.get('net_income', 0):,.2f}") # Show data source info st.info(f"📊 Data Source: {stock_data.get('price_source', 'Unknown')}") else: st.error(f"❌ Failed to fetch data: {stock_data.get('error', 'Unknown error')}") st.divider() # Company Information (Auto-filled or Manual) st.subheader("đŸĸ Company Information") if company_info_auto: st.info(f"Auto-detected: {company_info_auto}") company_info = company_info_auto else: company_type = st.selectbox( "Company Type", [ "Public Limited Company", "Private Limited Company", "Listed Company", "Unlisted Public Company", "Small Company", "One Person Company (OPC)", "Section 8 Company (Non-Profit)", "Government Company", "Foreign Company" ] ) industry = st.selectbox( "Industry Sector", [ "Information Technology", "Banking & Financial Services", "Pharmaceuticals", "Automobile", "Textiles", "Steel & Metal", "Oil & Gas", "Telecommunications", "Real Estate", "Infrastructure", "Consumer Goods", "Healthcare", "Education", "Agriculture", "Other" ] ) company_size = st.selectbox( "Company Size", [ "Small Company (Turnover ≤ ₹20 Cr, Paid-up Capital ≤ ₹2 Cr)", "Medium Company", "Large Company", "Listed Company", "Multinational Company" ] ) financial_year = st.selectbox( "Financial Year", ["FY 2024-25", "FY 2023-24", "FY 2022-23", "FY 2021-22"] ) company_info = f"{company_type} in the {industry} sector, classified as {company_size}, for {financial_year}" # Applicable Standards st.subheader("📋 Standards & Regulations") accounting_standards = st.selectbox( "Accounting Standards", [ "Indian Accounting Standards (Ind AS)", "Accounting Standards (AS) - Old GAAP", "Both Ind AS and AS (Transition period)" ] ) regulatory_frameworks = st.multiselect( "Applicable Regulations", [ "Companies Act 2013", "SEBI LODR Regulations", "SEBI ICDR Regulations", "RBI Guidelines", "FEMA Regulations", "Income Tax Act 1961", "GST Regulations", "RERA (Real Estate)", "Insurance Regulatory Act", "Banking Regulation Act" ], default=["Companies Act 2013"] ) # Main Content Area col1, col2 = st.columns([2, 1]) with col1: st.subheader("📄 Financial Data Input") if data_source == "Manual Input" or not financial_data: # Manual input tabs tab1, tab2 = st.tabs(["✍ī¸ Text Input", "📁 File Upload"]) with tab1: financial_data = st.text_area( "Enter Financial Statements and Related Information", value=financial_data, height=400, placeholder="""Enter your Indian financial data here, such as: â€ĸ Balance Sheet as per Schedule III of Companies Act 2013 â€ĸ Statement of Profit & Loss â€ĸ Cash Flow Statement â€ĸ Statement of Changes in Equity â€ĸ Notes to Financial Statements â€ĸ Board's Report â€ĸ Management Discussion & Analysis (MD&A) â€ĸ Corporate Governance Report â€ĸ CSR Report â€ĸ Auditor's Report â€ĸ Internal Financial Control Report â€ĸ Related Party Transactions â€ĸ Segment Reporting (if applicable) â€ĸ Subsidiary/Associate company details""" ) with tab2: uploaded_file = st.file_uploader( "Upload Financial Document", type=['txt'], help="Upload annual reports, financial statements, or compliance documents" ) if uploaded_file is not None: if uploaded_file.type == "text/plain": financial_data = str(uploaded_file.read(), "utf-8") st.success(f"✅ File uploaded: {uploaded_file.name}") else: # Display auto-fetched data with option to edit with st.expander("📊 Auto-Fetched Financial Data (Click to view/edit)", expanded=False): financial_data = st.text_area( "Financial Data (Auto-fetched - you can edit if needed)", value=financial_data, height=300 ) st.info(f"✅ Using data from: {data_source}") with col2: st.subheader("📊 Analysis Dashboard") # Quick metrics if 'compliance_result' in st.session_state: st.metric("Compliance Status", "Analyzed", "✓") st.metric("Data Source", data_source) st.metric("Analysis Count", len(st.session_state.get('analysis_history', []))) else: st.info("Analysis metrics will appear after running compliance check") # Data source info st.subheader("ℹī¸ Current Configuration") st.write(f"**Data Source:** {data_source}") st.write(f"**Accounting Standards:** {accounting_standards}") st.write(f"**Regulations:** {', '.join(regulatory_frameworks[:2])}{'...' if len(regulatory_frameworks) > 2 else ''}") # Analysis History st.subheader("📚 Recent Analyses") if 'analysis_history' not in st.session_state: st.session_state.analysis_history = [] if st.session_state.analysis_history: for i, analysis in enumerate(st.session_state.analysis_history[-3:]): with st.expander(f"Analysis {len(st.session_state.analysis_history) - i}"): st.text(f"Date: {analysis['timestamp']}") st.text(f"Source: {analysis['data_source']}") st.text(f"Company: {analysis['company_info'][:50]}...") st.text(f"Status: {analysis['status']}") else: st.info("No previous analyses") # Analysis Controls st.divider() col_run, col_options = st.columns([1, 2]) with col_run: run_analysis = st.button( "🚀 Run Indian Compliance Analysis", type="primary", use_container_width=True, disabled=not bool(financial_data.strip()) ) with col_options: with st.expander("⚙ī¸ Advanced Options"): col_adv1, col_adv2 = st.columns(2) with col_adv1: focus_areas = st.multiselect( "Focus Areas", [ "Revenue Recognition (Ind AS 115)", "Related Party Transactions", "Corporate Social Responsibility", "Board Governance", "Audit Committee Compliance", "Segment Reporting", "Consolidation Requirements" ] ) with col_adv2: analysis_depth = st.selectbox( "Analysis Depth", ["Standard", "Detailed", "Comprehensive"] ) # Run Analysis if run_analysis: if not financial_data.strip(): st.error("⚠ī¸ Please provide financial data to analyze") return with st.spinner("🔄 Running multi-agent Indian compliance analysis..."): # Progress tracking progress_bar = st.progress(0) status_text = st.empty() status_text.text("🤖 Initializing Indian compliance agents...") progress_bar.progress(20) time.sleep(1) status_text.text("📋 Analyzing documentation compliance...") progress_bar.progress(40) time.sleep(1) status_text.text("📊 Evaluating Indian accounting standards...") progress_bar.progress(60) time.sleep(1) status_text.text("⚖ī¸ Checking regulatory compliance...") progress_bar.progress(80) time.sleep(1) # Run the analysis result = create_indian_compliance_swarm(financial_data, company_info) progress_bar.progress(100) status_text.text("✅ Analysis complete!") time.sleep(1) # Clear progress indicators progress_bar.empty() status_text.empty() # Store results st.session_state.compliance_result = result # Add to history st.session_state.analysis_history.append({ 'timestamp': datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'data_source': data_source, 'company_info': company_info, 'status': 'Completed' }) # Display Results if 'compliance_result' in st.session_state: st.divider() st.header("📊 Indian Compliance Analysis Results") result = st.session_state.compliance_result if "error" in result: st.error(f"❌ Analysis failed: {result['error']}") return # Results tabs tab1, tab2, tab3 = st.tabs([ "📋 Executive Summary", "🔍 Detailed Analysis", "⚖ī¸ Risk Assessment" ]) with tab1: st.subheader("Executive Summary") # Display actual API results st.subheader("🤖 AI Analysis Output") if isinstance(result, dict) and 'response' in result: with st.container(): st.markdown("---") st.write(result['response']) else: with st.expander("Raw API Response"): st.json(result) with tab2: st.subheader("🔍 Detailed Agent Analysis") agents = [ ("📋", "Indian Documentation Analyzer", "Documentation compliance assessment"), ("📊", "Indian Accounting Standards Expert", "Accounting standards evaluation"), ("⚖ī¸", "Indian Regulatory Compliance Specialist", "Regulatory compliance review") ] for icon, agent, description in agents: with st.expander(f"{icon} {agent} Results"): st.write(f"**Agent:** {agent}") st.write(f"**Focus:** {description}") if "Documentation" in agent: st.write("**Key Areas Reviewed:**") st.write("â€ĸ Annual report completeness assessment") st.write("â€ĸ Board report adequacy review") st.write("â€ĸ Mandatory disclosure verification") st.write("â€ĸ CSR reporting compliance") elif "Accounting" in agent: st.write("**Standards Evaluated:**") st.write("â€ĸ Ind AS/AS compliance evaluation") st.write("â€ĸ Revenue recognition analysis") st.write("â€ĸ Financial instrument accounting review") st.write("â€ĸ Consolidation requirements check") else: st.write("**Regulations Assessed:**") st.write("â€ĸ Companies Act 2013 compliance") st.write("â€ĸ SEBI regulation adherence") st.write("â€ĸ Corporate governance assessment") st.write("â€ĸ Filing requirements validation") with tab3: st.subheader("⚖ī¸ Indian Regulatory Risk Assessment") # Risk categories specific to Indian context risk_data = { 'Risk Area': [ 'Companies Act Compliance', 'Ind AS Implementation', 'SEBI Regulations', 'CSR Compliance', 'Corporate Governance', 'Tax Compliance' ], 'Risk Level': ['Medium', 'Low', 'Medium', 'High', 'Low', 'Medium'], 'Impact Score': [7, 4, 6, 9, 3, 5], 'Likelihood Score': [5, 3, 6, 8, 2, 4] } df_risk = pd.DataFrame(risk_data) # Risk matrix visualization fig = px.scatter( df_risk, x='Impact Score', y='Likelihood Score', color='Risk Level', size='Impact Score', hover_data=['Risk Area'], title="Indian Regulatory Risk Matrix", color_discrete_map={'High': '#FF6B6B', 'Medium': '#FFD93D', 'Low': '#6BCF7F'}, labels={'Impact Score': 'Impact →', 'Likelihood Score': 'Likelihood →'} ) fig.update_layout( xaxis_range=[0, 10], yaxis_range=[0, 10], height=500 ) st.plotly_chart(fig, use_container_width=True) st.subheader("📊 Risk Summary Table") st.dataframe(df_risk, use_container_width=True) # Export functionality st.divider() st.subheader("📤 Export Results") col_exp1, col_exp2 = st.columns(2) with col_exp1: # Create comprehensive CSV data comprehensive_df = create_comprehensive_csv_data( data_source, company_info, accounting_standards, regulatory_frameworks, result ) csv_data = comprehensive_df.to_csv(index=False) st.download_button( "📋 Download Analysis CSV", data=csv_data, file_name=f"compliance_analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv", mime="text/csv" ) # Show preview of CSV data with st.expander("📊 CSV Data Preview"): st.dataframe(comprehensive_df, use_container_width=True, height=300) with col_exp2: json_result = json.dumps(result, indent=2) st.download_button( "💾 Download JSON", data=json_result, file_name=f"indian_compliance_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json", mime="application/json" ) if __name__ == "__main__": main()