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| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import json | |
| import chromadb | |
| from chromadb.config import Settings | |
| from datetime import datetime | |
| class FastFinancialAnalyzer: | |
| def __init__(self): | |
| # Initialize ChromaDB | |
| self.client = chromadb.Client(Settings(anonymized_telemetry=False)) | |
| # Create financial metrics collection | |
| self.collection = self.client.create_collection( | |
| name="financial_metrics_" + datetime.now().strftime("%Y%m%d_%H%M%S") | |
| ) | |
| # Initialize ratio benchmarks | |
| self.initialize_ratio_benchmarks() | |
| def initialize_ratio_benchmarks(self): | |
| """Initialize benchmark ratios for comparison""" | |
| self.benchmarks = { | |
| "liquidity_ratios": { | |
| "current_ratio": {"good": 2.0, "warning": 1.0}, | |
| "quick_ratio": {"good": 1.0, "warning": 0.5} | |
| }, | |
| "profitability_ratios": { | |
| "gross_margin": {"good": 0.4, "warning": 0.2}, | |
| "net_margin": {"good": 0.1, "warning": 0.05} | |
| }, | |
| "efficiency_ratios": { | |
| "inventory_turnover": {"good": 4, "warning": 2}, | |
| "asset_turnover": {"good": 0.5, "warning": 0.25} | |
| } | |
| } | |
| def calculate_ratios(self, balance_sheet_df, income_stmt_df): | |
| """Calculate key financial ratios""" | |
| try: | |
| ratios = {} | |
| # Clean numeric data | |
| for df in [balance_sheet_df, income_stmt_df]: | |
| for col in df.select_dtypes(include=['object']).columns: | |
| df[col] = pd.to_numeric(df[col].astype(str).str.replace(r'[^\d.-]', ''), errors='coerce') | |
| # Calculate ratios for each year | |
| years = [col for col in balance_sheet_df.columns if str(col).isdigit()] | |
| for year in years: | |
| ratios[year] = { | |
| "liquidity": { | |
| "current_ratio": balance_sheet_df.loc[balance_sheet_df['Account'] == 'Total_Current_Assets', year].values[0] / | |
| balance_sheet_df.loc[balance_sheet_df['Account'] == 'Total_Current_Liabilities', year].values[0], | |
| "quick_ratio": (balance_sheet_df.loc[balance_sheet_df['Account'] == 'Total_Current_Assets', year].values[0] - | |
| balance_sheet_df.loc[balance_sheet_df['Account'] == 'Inventory', year].values[0]) / | |
| balance_sheet_df.loc[balance_sheet_df['Account'] == 'Total_Current_Liabilities', year].values[0] | |
| }, | |
| "profitability": { | |
| "gross_margin": income_stmt_df.loc[income_stmt_df['Revenue Items'] == 'Gross Profit', year].values[0] / | |
| income_stmt_df.loc[income_stmt_df['Revenue Items'] == 'Total Net Revenue', year].values[0], | |
| "net_margin": income_stmt_df.loc[income_stmt_df['Revenue Items'] == 'Net Earnings', year].values[0] / | |
| income_stmt_df.loc[income_stmt_df['Revenue Items'] == 'Total Net Revenue', year].values[0] | |
| }, | |
| "growth": { | |
| "revenue_growth": None if year == years[0] else | |
| (income_stmt_df.loc[income_stmt_df['Revenue Items'] == 'Total Net Revenue', year].values[0] - | |
| income_stmt_df.loc[income_stmt_df['Revenue Items'] == 'Total Net Revenue', str(int(year)-1)].values[0]) / | |
| income_stmt_df.loc[income_stmt_df['Revenue Items'] == 'Total Net Revenue', str(int(year)-1)].values[0] * 100 | |
| } | |
| } | |
| return ratios | |
| except Exception as e: | |
| return f"Error calculating ratios: {str(e)}" | |
| def analyze_trends(self, ratios): | |
| """Analyze financial trends""" | |
| trends = { | |
| "liquidity": self.analyze_ratio_trend("current_ratio", ratios), | |
| "profitability": self.analyze_ratio_trend("net_margin", ratios), | |
| "growth": self.analyze_revenue_growth(ratios) | |
| } | |
| return trends | |
| def analyze_ratio_trend(self, ratio_name, ratios): | |
| """Analyze trend for a specific ratio""" | |
| values = [] | |
| years = sorted(ratios.keys()) | |
| for year in years: | |
| if ratio_name in ratios[year].get("liquidity", {}): | |
| values.append(ratios[year]["liquidity"][ratio_name]) | |
| elif ratio_name in ratios[year].get("profitability", {}): | |
| values.append(ratios[year]["profitability"][ratio_name]) | |
| if not values: | |
| return "No data available" | |
| trend = np.polyfit(range(len(values)), values, 1)[0] | |
| if trend > 0.05: | |
| return "Strong upward trend" | |
| elif trend > 0: | |
| return "Slight upward trend" | |
| elif trend > -0.05: | |
| return "Stable" | |
| else: | |
| return "Downward trend" | |
| def analyze_revenue_growth(self, ratios): | |
| """Analyze revenue growth trend""" | |
| growth_rates = [] | |
| years = sorted(ratios.keys())[1:] # Skip first year as it won't have growth rate | |
| for year in years: | |
| if ratios[year]["growth"]["revenue_growth"] is not None: | |
| growth_rates.append(ratios[year]["growth"]["revenue_growth"]) | |
| if not growth_rates: | |
| return "No growth data available" | |
| avg_growth = np.mean(growth_rates) | |
| if avg_growth > 10: | |
| return f"Strong growth (avg {avg_growth:.1f}%)" | |
| elif avg_growth > 0: | |
| return f"Moderate growth (avg {avg_growth:.1f}%)" | |
| else: | |
| return f"Declining growth (avg {avg_growth:.1f}%)" | |
| def generate_insights(self, ratios, trends): | |
| """Generate actionable insights""" | |
| insights = [] | |
| # Liquidity insights | |
| current_ratio = ratios[max(ratios.keys())]["liquidity"]["current_ratio"] | |
| if current_ratio < self.benchmarks["liquidity_ratios"]["current_ratio"]["warning"]: | |
| insights.append("ALERT: Liquidity needs immediate attention") | |
| elif current_ratio < self.benchmarks["liquidity_ratios"]["current_ratio"]["good"]: | |
| insights.append("WATCH: Liquidity is below ideal levels") | |
| # Profitability insights | |
| net_margin = ratios[max(ratios.keys())]["profitability"]["net_margin"] | |
| if net_margin > self.benchmarks["profitability_ratios"]["net_margin"]["good"]: | |
| insights.append("STRONG: Excellent profit margins") | |
| elif net_margin < self.benchmarks["profitability_ratios"]["net_margin"]["warning"]: | |
| insights.append("ALERT: Profit margins need improvement") | |
| # Growth insights | |
| if "growth" in trends: | |
| if "Strong" in trends["growth"]: | |
| insights.append("POSITIVE: Strong revenue growth trend") | |
| elif "Declining" in trends["growth"]: | |
| insights.append("WATCH: Revenue growth is slowing") | |
| return insights | |
| def analyze_financials(self, balance_sheet_file, income_stmt_file): | |
| """Main analysis function""" | |
| try: | |
| # Read files | |
| balance_sheet_df = pd.read_csv(balance_sheet_file) | |
| income_stmt_df = pd.read_csv(income_stmt_file) | |
| # Calculate ratios | |
| ratios = self.calculate_ratios(balance_sheet_df, income_stmt_df) | |
| # Analyze trends | |
| trends = self.analyze_trends(ratios) | |
| # Generate insights | |
| insights = self.generate_insights(ratios, trends) | |
| # Prepare comprehensive analysis | |
| analysis = { | |
| "Financial Ratios": ratios, | |
| "Trend Analysis": trends, | |
| "Key Insights": insights, | |
| "Summary": { | |
| "Latest Year Analysis": { | |
| "Current Ratio": f"{ratios[max(ratios.keys())]['liquidity']['current_ratio']:.2f}", | |
| "Net Margin": f"{ratios[max(ratios.keys())]['profitability']['net_margin']:.2%}", | |
| "Revenue Growth": f"{ratios[max(ratios.keys())]['growth']['revenue_growth']:.2f}%" if ratios[max(ratios.keys())]['growth']['revenue_growth'] else "N/A" | |
| } | |
| } | |
| } | |
| return json.dumps(analysis, indent=2) | |
| except Exception as e: | |
| return f"Error in analysis: {str(e)}" | |
| def create_interface(): | |
| analyzer = FastFinancialAnalyzer() | |
| iface = gr.Interface( | |
| fn=analyzer.analyze_financials, | |
| inputs=[ | |
| gr.File(label="Balance Sheet (CSV)", type="filepath"), | |
| gr.File(label="Income Statement (CSV)", type="filepath") | |
| ], | |
| outputs=gr.Textbox(label="Analysis Results", lines=20), | |
| title="Fast Financial Statement Analyzer", | |
| description="Upload financial statements for instant analysis with ratio calculations and trend detection." | |
| ) | |
| return iface | |
| if __name__ == "__main__": | |
| iface = create_interface() | |
| iface.launch() |