Update app.py

app.py

@@ -187,6 +187,18 @@ class FinancialAnalyzer:
             print(f"Error extracting metrics: {str(e)}")
             return {}
 
+    def convert_to_serializable(obj):
+        """Convert numpy values to Python native types"""
+        if isinstance(obj, np.float32):
+            return float(obj)
+        elif isinstance(obj, np.ndarray):
+            return obj.tolist()
+        elif isinstance(obj, dict):
+            return {key: convert_to_serializable(value) for key, value in obj.items()}
+        elif isinstance(obj, list):
+            return [convert_to_serializable(item) for item in obj]
+        return obj
+
     def get_sentiment_analysis(self, metrics):
         """Get financial sentiment analysis using FinBERT"""
         try:
@@ -197,20 +209,69 @@ class FinancialAnalyzer:
             Interest coverage: {metrics['Ratios'].get('Interest_Coverage', 0):.2f}
             Current ratio: {metrics['Ratios'].get('Current_Ratio', 0):.2f}
             """
-            
+        
             inputs = self.finbert_tokenizer(financial_text, return_tensors="pt", padding=True, truncation=True)
             outputs = self.finbert_model(**inputs)
             probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
             sentiment_scores = probabilities.detach().numpy()[0]
-            
+        
             sentiments = ['negative', 'neutral', 'positive']
-            sentiment_dict = dict(zip(sentiments, sentiment_scores))
-            
+            sentiment_dict = dict(zip(sentiments, [float(score) for score in sentiment_scores]))
+        
             return sentiment_dict
         except Exception as e:
             print(f"Error in sentiment analysis: {str(e)}")
             return {}
 
+    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)
+            metrics = self.extract_metrics(income_data, balance_data)
+
+        # Get sentiment analysis
+            sentiment_dict = self.get_sentiment_analysis(metrics)
+
+        # Generate and get analysis
+            prompt = self.generate_prompt(metrics, sentiment_dict)
+            analysis = self.generate_analysis(prompt)
+
+        # Convert all numpy values to Python native types
+            metrics = convert_to_serializable(metrics)
+            sentiment_dict = convert_to_serializable(sentiment_dict)
+
+        # Prepare final results
+            results = {
+                "Financial Analysis": {
+                "Key Metrics": metrics,
+                "Market Sentiment": sentiment_dict,
+                "AI Insights": analysis,
+                "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)}\n\nDetails: {type(e).__name__}"
+
+
+
+    
+
     def generate_prompt(self, metrics, sentiment_dict):
         """Create enhanced analysis prompt with sentiment"""
         try: