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| import gradio as gr | |
| import pandas as pd | |
| import json | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| class FastFinancialAnalyzer: | |
| def __init__(self): | |
| print("Initializing Analyzer...") | |
| self.initialize_model() | |
| print("Initialization complete!") | |
| def initialize_model(self): | |
| """Initialize TinyLlama model""" | |
| self.tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0") | |
| self.model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0") | |
| self.model.eval() # Set to evaluation mode | |
| def parse_markdown_table(self, content, section_name=""): | |
| """Extract data from markdown table""" | |
| data = {} | |
| lines = content.split('\n') | |
| headers = [] | |
| current_section = section_name | |
| for line in lines: | |
| if line.startswith('##'): | |
| current_section = line.strip('#').strip() | |
| elif '|' in line: | |
| # Skip separator lines | |
| if '-|-' in line: | |
| continue | |
| # Process table rows | |
| cells = [cell.strip() for cell in line.split('|')[1:-1]] | |
| if not headers: | |
| headers = cells | |
| else: | |
| if len(cells) == len(headers): | |
| row_data = dict(zip(headers, cells)) | |
| key = row_data.get(headers[0], "").strip() | |
| if key: | |
| data[key] = row_data | |
| return {current_section: data} | |
| def clean_number(self, value): | |
| """Clean numerical values""" | |
| if isinstance(value, str): | |
| value = value.replace(',', '').replace('$', '').replace('(', '-').replace(')', '') | |
| value = value.strip() | |
| try: | |
| return float(value) | |
| except: | |
| return 0.0 | |
| def extract_key_metrics(self, income_data, balance_data): | |
| """Extract key financial metrics with safety checks""" | |
| try: | |
| # First, safely extract values with error handling | |
| revenue_2025 = self.safe_extract_number(income_data, "Total Net Revenue", "2025") | |
| revenue_2021 = self.safe_extract_number(income_data, "Total Net Revenue", "2021") | |
| profit_2025 = self.safe_extract_number(income_data, "Net Earnings", "2025") | |
| profit_2021 = self.safe_extract_number(income_data, "Net Earnings", "2021") | |
| assets_2025 = self.safe_extract_number(balance_data, "Total_Assets", "2025") | |
| assets_2021 = self.safe_extract_number(balance_data, "Total_Assets", "2021") | |
| metrics = { | |
| "Revenue": { | |
| "2025": revenue_2025, | |
| "2021": revenue_2021, | |
| "Growth": self.calculate_growth(revenue_2025, revenue_2021) | |
| }, | |
| "Profit": { | |
| "2025": profit_2025, | |
| "2021": profit_2021, | |
| "Growth": self.calculate_growth(profit_2025, profit_2021), | |
| "Margin_2025": self.calculate_percentage(profit_2025, revenue_2025) | |
| }, | |
| "Assets": { | |
| "2025": assets_2025, | |
| "2021": assets_2021, | |
| "Growth": self.calculate_growth(assets_2025, assets_2021) | |
| } | |
| } | |
| return metrics | |
| except Exception as e: | |
| print(f"Error in metric extraction: {str(e)}") | |
| return self.get_default_metrics() | |
| def safe_extract_number(self, data_dict, key, year): | |
| """Safely extract and convert number from data""" | |
| try: | |
| if isinstance(data_dict, dict): | |
| for k, v in data_dict.items(): | |
| if isinstance(v, dict) and key in k: | |
| value = v.get(year, '0') | |
| return self.clean_number(value) | |
| return 0.0 | |
| except Exception as e: | |
| print(f"Error extracting {key} for {year}: {str(e)}") | |
| return 0.0 | |
| def calculate_growth(self, current, previous): | |
| """Calculate growth percentage with safety check""" | |
| try: | |
| if previous and previous != 0: | |
| return ((current - previous) / abs(previous)) * 100 | |
| return 0.0 | |
| except: | |
| return 0.0 | |
| def calculate_percentage(self, numerator, denominator): | |
| """Calculate percentage with safety check""" | |
| try: | |
| if denominator and denominator != 0: | |
| return (numerator / denominator) * 100 | |
| return 0.0 | |
| except: | |
| return 0.0 | |
| def get_default_metrics(self): | |
| """Return default metrics structure""" | |
| return { | |
| "Revenue": {"2025": 0, "2021": 0, "Growth": 0}, | |
| "Profit": {"2025": 0, "2021": 0, "Growth": 0, "Margin_2025": 0}, | |
| "Assets": {"2025": 0, "2021": 0, "Growth": 0} | |
| } | |
| def generate_analysis_prompt(self, metrics): | |
| """Create focused analysis prompt with safety checks""" | |
| return f"""<human>Analyze these financial metrics and provide insights: | |
| Key Performance Indicators: | |
| 1. Revenue Performance: | |
| - 2025: ${metrics['Revenue']['2025']:,.1f}M | |
| - 2021: ${metrics['Revenue']['2021']:,.1f}M | |
| - 5-Year Growth: {metrics['Revenue']['Growth']:.1f}% | |
| 2. Profitability: | |
| - 2025 Net Profit: ${metrics['Profit']['2025']:,.1f}M | |
| - 2021 Net Profit: ${metrics['Profit']['2021']:,.1f}M | |
| - Profit Growth: {metrics['Profit']['Growth']:.1f}% | |
| - 2025 Profit Margin: {metrics['Profit']['Margin_2025']:.1f}% | |
| 3. Asset Base: | |
| - 2025 Total Assets: ${metrics['Assets']['2025']:,.1f}M | |
| - 2021 Total Assets: ${metrics['Assets']['2021']:,.1f}M | |
| - Asset Growth: {metrics['Assets']['Growth']:.1f}% | |
| Based on these metrics, provide: | |
| 1. Financial Performance Assessment | |
| 2. Key Strengths and Weaknesses | |
| 3. Strategic Recommendations</human>""" | |
| def generate_analysis_prompt(self, metrics): | |
| """Create focused analysis prompt""" | |
| return f"""<human>Analyze these financial metrics and provide insights: | |
| Key Performance Indicators (in millions): | |
| 1. Revenue: | |
| - 2025: ${metrics['Revenue']['2025']:.1f}M | |
| - 2021: ${metrics['Revenue']['2021']:.1f}M | |
| - Growth: {((metrics['Revenue']['2025'] - metrics['Revenue']['2021']) / metrics['Revenue']['2021'] * 100):.1f}% | |
| 2. Net Profit: | |
| - 2025: ${metrics['Profit']['2025']:.1f}M | |
| - 2021: ${metrics['Profit']['2021']:.1f}M | |
| - Margin 2025: {(metrics['Profit']['2025'] / metrics['Revenue']['2025'] * 100):.1f}% | |
| 3. Asset Utilization: | |
| - 2025: ${metrics['Assets']['2025']:.1f}M | |
| - 2021: ${metrics['Assets']['2021']:.1f}M | |
| - Growth: {((metrics['Assets']['2025'] - metrics['Assets']['2021']) / metrics['Assets']['2021'] * 100):.1f}% | |
| Provide: | |
| 1. Performance Assessment | |
| 2. Key Strengths and Concerns | |
| 3. Strategic Recommendations</human>""" | |
| def generate_analysis(self, prompt): | |
| """Generate analysis using TinyLlama""" | |
| try: | |
| inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1500) | |
| outputs = self.model.generate( | |
| inputs["input_ids"], | |
| max_new_tokens=500, | |
| temperature=0.7, | |
| top_p=0.9, | |
| do_sample=True, | |
| pad_token_id=self.tokenizer.eos_token_id, | |
| no_repeat_ngram_size=3 | |
| ) | |
| return self.tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| except Exception as e: | |
| return f"Error generating analysis: {str(e)}" | |
| def analyze_financials(self, balance_sheet_file, income_stmt_file): | |
| """Main analysis function""" | |
| try: | |
| # Read files | |
| with open(balance_sheet_file, 'r') as f: | |
| balance_sheet = f.read() | |
| with open(income_stmt_file, 'r') as f: | |
| income_stmt = f.read() | |
| # Parse data | |
| income_data = self.parse_markdown_table(income_stmt, "Income Statement") | |
| balance_data = self.parse_markdown_table(balance_sheet, "Balance Sheet") | |
| # Extract metrics | |
| metrics = self.extract_key_metrics(income_data.get("Income Statement", {}), | |
| balance_data.get("Balance Sheet", {})) | |
| # Generate analysis | |
| analysis_prompt = self.generate_analysis_prompt(metrics) | |
| analysis = self.generate_analysis(analysis_prompt) | |
| # Prepare results | |
| results = { | |
| "Financial Analysis": { | |
| "Key Metrics": metrics, | |
| "AI Analysis": analysis.split("<human>")[-1].strip(), | |
| "Analysis Period": "2021-2025", | |
| "Note": "All monetary values in millions ($M)" | |
| } | |
| } | |
| return json.dumps(results, 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 (Markdown)", type="filepath"), | |
| gr.File(label="Income Statement (Markdown)", type="filepath") | |
| ], | |
| outputs=gr.Textbox(label="Analysis Results", lines=25), | |
| title="Fast Financial Statement Analyzer", | |
| description="Upload financial statements in Markdown format for quick AI-powered analysis" | |
| ) | |
| return iface | |
| if __name__ == "__main__": | |
| iface = create_interface() | |
| iface.launch() |