Update app.py

app.py

@@ -329,6 +329,106 @@ Provide:
             recommendations.append("- Focus on improving operational efficiency to enhance returns")
         if metrics.get('revenue_growth', 0) < 5:
             recommendations.append("- Develop strategies to accelerate revenue growth")
+            recommendations.append("- Consider strategic acquisitions or new market entry")
+        
+            return "Key Recommendations:\n" + "\n".join(recommendations)
+
+        def analyze_financials(self, balance_sheet_file, income_stmt_file):
+        """Main analysis function"""
+            try:
+            # Validate input files
+                if not (self.is_valid_markdown(balance_sheet_file) and self.is_valid_markdown(income_stmt_file)):
+                    return "Error: One or both files are invalid or not in Markdown format."
+
+            # 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()
+
+            # Process financial data
+                income_data = self.parse_financial_data(income_stmt)
+                balance_data = self.parse_financial_data(balance_sheet)
+            
+            # Calculate metrics
+                metrics = self.calculate_metrics(income_data, balance_data)
+                self.last_metrics = metrics
+
+            # Generate analysis
+                analysis = self.generate_analysis(metrics)
+
+            # Prepare final results
+                results = {
+                "Financial Analysis": {
+                    "Key Metrics": {
+                        "Profitability": {
+                            "Gross Profit Margin": f"{metrics['gross_profit_margin']:.2f}%",
+                            "Net Profit Margin": f"{metrics['net_profit_margin']:.2f}%",
+                            "Return on Equity": f"{metrics['roe']:.2f}%"
+                        },
+                        "Liquidity": {
+                            "Current Ratio": f"{metrics['current_ratio']:.2f}",
+                            "Accounts Receivable Turnover": f"{metrics['ar_turnover']:.2f}"
+                        },
+                        "Solvency": {
+                            "Debt Ratio": f"{metrics['debt_ratio']:.2f}%",
+                            "Retained Earnings Ratio": f"{metrics['retained_earnings_ratio']:.2f}%"
+                        },
+                        "Growth": {
+                            "Sustainable Growth Rate": f"{metrics['sgr']:.2f}%",
+                            "Revenue Growth (YoY)": f"{metrics['revenue_growth']:.2f}%"
+                        }
+                    },
+                    "Analysis": analysis,
+                    "Analysis Period": "2021-2025",
+                    "Note": "Analysis based on CFI standards"
+                }
+            }
+
+                return json.dumps(results, indent=2)
+
+            except Exception as e:
+                return f"Error in analysis: {str(e)}\n\nDetails: {type(e).__name__}"
+
+        def fine_tune_models(self, train_texts, train_labels, epochs=3):
+            """Fine-tune the model with custom data"""
+            try:
+            # Prepare dataset
+                train_dataset = FinancialDataset(train_texts, train_labels, self.llama_tokenizer)
+            
+            # Training arguments
+                training_args = TrainingArguments(
+                output_dir="./financial_model_tuned",
+                num_train_epochs=epochs,
+                per_device_train_batch_size=4,
+                logging_dir="./logs",
+                logging_steps=10,
+                save_steps=50,
+                eval_steps=50,
+                learning_rate=2e-5,
+                weight_decay=0.01,
+                warmup_steps=500
+            )
+
+            # Initialize trainer
+                trainer = Trainer(
+                    model=self.llama_model,
+                    args=training_args,
+                    train_dataset=train_dataset
+            )
+
+            # Fine-tune the model
+                trainer.train()
+            
+            # Save the fine-tuned model
+                self.llama_model.save_pretrained("./financial_model_tuned")
+                self.llama_tokenizer.save_pretrained("./financial_model_tuned")
+            
+                print("Fine-tuning completed successfully!")
+            except Exception as e:
+                print(f"Error in fine-tuning: {str(e)}")
+
+
 
 def create_interface():
     analyzer = FinancialAnalyzer()