Delete financial-ai-analyzer.txt

financial-ai-analyzer.txt

@@ -1,126 +0,0 @@
-# Financial AI Analyzer.ipynb
-
-import torch
-from transformers import (
-    AutoTokenizer, 
-    AutoModelForCausalLM,
-    AutoModelForSequenceClassification
-)
-import gradio as gr
-from financial_metrics_calculator import FinancialMetricsCalculator  # Import from first notebook
-
-class FinancialAIAnalyzer:
-    def __init__(self):
-        print("Initializing AI Models...")
-        self.metrics_calculator = FinancialMetricsCalculator()
-        self.initialize_models()
-        self.last_metrics = {}
-        print("Initialization complete!")
-
-    def initialize_models(self):
-        """Initialize both TinyLlama and FinBERT models"""
-        try:
-            # Initialize TinyLlama
-            self.llama_tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
-            self.llama_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
-            self.llama_model.eval()
-
-            # Initialize FinBERT
-            self.finbert_tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")
-            self.finbert_model = AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert")
-            self.finbert_model.eval()
-
-            print("Models loaded successfully!")
-        except Exception as e:
-            print(f"Error initializing models: {str(e)}")
-            raise
-
-    def get_sentiment_analysis(self, metrics):
-        """Get financial sentiment analysis using FinBERT"""
-        try:
-            financial_text = f"""
-            Revenue growth: {metrics['Ratios'].get('Revenue_Growth', 0):.2f}%
-            Profit margin: {metrics['Ratios'].get('Net_Margin', 0):.2f}%
-            Debt to equity: {metrics['Ratios'].get('Debt_to_Equity', 0):.2f}
-            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, [float(score) for score in sentiment_scores]))
-            
-            return sentiment_dict
-        except Exception as e:
-            print(f"Error in sentiment analysis: {str(e)}")
-            return {}
-
-    def generate_prompt(self, metrics, sentiment_dict):
-        """Create analysis prompt"""
-        try:
-            # [Previous generate_prompt code remains the same]
-            pass
-        except Exception as e:
-            print(f"Error generating prompt: {str(e)}")
-            return ""
-
-    def generate_analysis(self, prompt):
-        """Generate analysis using TinyLlama"""
-        try:
-            # [Previous generate_analysis code remains the same]
-            pass
-        except Exception as e:
-            print(f"Error generating analysis: {str(e)}")
-            return self.generate_fallback_analysis(self.last_metrics)
-
-    def generate_fallback_analysis(self, metrics):
-        """Generate fallback analysis"""
-        try:
-            # [Previous generate_fallback_analysis code remains the same]
-            pass
-        except Exception as e:
-            return f"Error generating fallback analysis: {str(e)}"
-
-    def analyze_financials(self, balance_sheet_file, income_stmt_file):
-        """Main analysis function"""
-        try:
-            # Get metrics from calculator
-            metrics = self.metrics_calculator.process_financial_statements(balance_sheet_file, income_stmt_file)
-            if isinstance(metrics, str):  # Error message
-                return metrics
-            
-            self.last_metrics = metrics
-            sentiment_dict = self.get_sentiment_analysis(metrics)
-            prompt = self.generate_prompt(metrics, sentiment_dict)
-            analysis = self.generate_analysis(prompt)
-
-            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)}"
-
-def create_interface():
-    analyzer = FinancialAIAnalyzer()
-    
-    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="AI Financial Statement Analyzer",
-        description="Upload financial statements in Markdown format for AI-