Update app.py

app.py

@@ -1,157 +1,153 @@
-import gradio as gr
-import pandas as pd
-import json
-
-def analyze_financial_statements(income_statement, balance_sheet):
-    """Analyze uploaded financial statements"""
-    try:
-        # Read the uploaded files
-        income_df = pd.read_csv(income_statement.name)
-        balance_df = pd.read_csv(balance_sheet.name)
-        
-        # Calculate key metrics
-        metrics = calculate_metrics(income_df, balance_df)
-        
-        # Generate analysis
-        analysis = generate_analysis(metrics)
-        
-        # Format output
-        return format_results(metrics, analysis)
-        
-    except Exception as e:
-        return f"Error analyzing files: {str(e)}"
-
-def calculate_metrics(income_df, balance_df):
-    """Calculate financial metrics"""
-    # Clean numeric data
-    income_df = income_df.replace({'\$': '', ' ': '', ',': '', '\(': '-', '\)': ''}, regex=True)
-    balance_df = balance_df.replace({'\$': '', ' ': '', ',': '', '\(': '-', '\)': ''}, regex=True)
-    
-    # Convert to numeric
-    numeric_cols = ['Revenue', 'Net Income', 'Gross Profit', 'Total Assets', 
-                   'Total Liabilities', "Shareholder's Equity"]
-    
-    for col in numeric_cols:
-        if col in income_df.columns:
-            income_df[col] = pd.to_numeric(income_df[col], errors='coerce')
-        if col in balance_df.columns:
-            balance_df[col] = pd.to_numeric(balance_df[col], errors='coerce')
-    
-    # Calculate metrics
-    metrics = {
-        "Profitability": {
-            "Gross Margin (%)": round((income_df['Gross Profit'].iloc[-1] / 
-                                     income_df['Revenue'].iloc[-1] * 100), 2),
-            "Net Margin (%)": round((income_df['Net Income'].iloc[-1] / 
-                                   income_df['Revenue'].iloc[-1] * 100), 2)
-        },
-        "Growth": {
-            "Revenue Growth (%)": round(((income_df['Revenue'].iloc[-1] / 
-                                        income_df['Revenue'].iloc[0] - 1) * 100), 2),
-            "Net Income Growth (%)": round(((income_df['Net Income'].iloc[-1] / 
-                                           income_df['Net Income'].iloc[0] - 1) * 100), 2)
-        },
-        "Financial Position": {
-            "Debt to Equity": round(balance_df['Total Liabilities'].iloc[-1] / 
-                                  balance_df["Shareholder's Equity"].iloc[-1], 2),
-            "Equity Ratio (%)": round((balance_df["Shareholder's Equity"].iloc[-1] / 
-                                     balance_df['Total Assets'].iloc[-1] * 100), 2)
-        }
-    }
-    
-    return metrics
-
-def generate_analysis(metrics):
-    """Generate insights and recommendations"""
-    analysis = {
-        "Key Insights": [],
-        "Recommendations": [],
-        "Risk Factors": []
-    }
-    
-    # Analyze profitability
-    net_margin = metrics["Profitability"]["Net Margin (%)"]
-    if net_margin < 5:
-        analysis["Key Insights"].append(
-            "Low profit margins indicating operational efficiency challenges")
-        analysis["Recommendations"].append({
-            "Priority": "High",
-            "Area": "Profitability",
-            "Action": "Implement cost optimization program",
-            "Impact": "Improve net margins by 2-3%"
-        })
-    
-    # Analyze growth
-    revenue_growth = metrics["Growth"]["Revenue Growth (%)"]
-    if revenue_growth < 5:
-        analysis["Key Insights"].append(
-            "Slow revenue growth suggesting need for market expansion")
-        analysis["Recommendations"].append({
-            "Priority": "High",
-            "Area": "Growth",
-            "Action": "Develop market expansion strategy",
-            "Impact": "Accelerate revenue growth to 8-10%"
-        })
-    
-    # Analyze financial position
-    equity_ratio = metrics["Financial Position"]["Equity Ratio (%)"]
-    if equity_ratio > 70:
-        analysis["Key Insights"].append(
-            "High equity ratio suggests potential for leveraged growth")
-        analysis["Recommendations"].append({
-            "Priority": "Medium",
-            "Area": "Capital Structure",
-            "Action": "Evaluate growth financing options",
-            "Impact": "Optimize capital structure"
-        })
-    
-    return analysis
-
-def format_results(metrics, analysis):
-    """Format analysis results"""
-    output = "# Financial Analysis Report\n\n"
-    
-    # Add Metrics
-    output += "## Financial Metrics\n\n"
-    for category, values in metrics.items():
-        output += f"### {category}\n"
-        for metric, value in values.items():
-            output += f"- {metric}: {value}\n"
-        output += "\n"
-    
-    # Add Insights
-    output += "## Key Insights\n\n"
-    for insight in analysis["Key Insights"]:
-        output += f"- {insight}\n"
-    output += "\n"
-    
-    # Add Recommendations
-    output += "## Recommendations\n\n"
-    for rec in analysis["Recommendations"]:
-        output += f"### {rec['Area']} ({rec['Priority']} Priority)\n"
-        output += f"- Action: {rec['Action']}\n"
-        output += f"- Expected Impact: {rec['Impact']}\n\n"
-    
-    return output
-
-# Create Gradio interface
-iface = gr.Interface(
-    fn=analyze_financial_statements,
-    inputs=[
-        gr.File(label="Income Statement (CSV)"),
-        gr.File(label="Balance Sheet (CSV)")
-    ],
-    outputs=gr.Markdown(),
-    title="Financial Statement Analysis",
-    description="Upload your Income Statement and Balance Sheet (CSV format) for analysis.",
-    examples=[
-        [
-            "OFINTECH-Income Statement-template.csv",
-            "OFINTECH Balance Sheet template.csv"
-        ]
-    ]
-)
-
-# Launch the interface
-if __name__ == "__main__":
+import gradio as gr
+import pandas as pd
+from transformers import pipeline
+import torch
+
+class FinancialAnalyzer:
+    def __init__(self):
+        """Initialize models"""
+        # 1. Llama 2 for strategic analysis
+        self.strategic_analyzer = pipeline(
+            "text-generation",
+            model="meta-llama/Llama-2-7b-chat-hf",
+            device_map="auto"
+        )
+        
+        # 2. FinBERT for financial sentiment
+        self.financial_analyzer = pipeline(
+            "text-classification",
+            model="ProsusAI/finbert",
+            return_all_scores=True
+        )
+        
+        # 3. Falcon for recommendations
+        self.recommendation_generator = pipeline(
+            "text-generation",
+            model="tiiuae/falcon-7b-instruct",
+            device_map="auto"
+        )
+    
+    def generate_strategic_analysis(self, financial_data):
+        """Generate strategic analysis using Llama 2"""
+        prompt = f"""[INST] As a senior financial analyst, analyze these financial statements:
+
+        Financial Data:
+        {financial_data}
+
+        Provide:
+        1. Business Health Assessment
+        2. Key Strategic Insights
+        3. Market Position Analysis
+        4. Growth Opportunities
+        5. Risk Factors [/INST]"""
+        
+        response = self.strategic_analyzer(
+            prompt,
+            max_length=1000,
+            temperature=0.7
+        )
+        return response[0]['generated_text']
+    
+    def analyze_sentiment(self, text):
+        """Analyze financial sentiment using FinBERT"""
+        return self.financial_analyzer(text)
+    
+    def generate_recommendations(self, analysis):
+        """Generate recommendations using Falcon"""
+        prompt = f"""Based on this financial analysis:
+        {analysis}
+        
+        Provide specific, actionable recommendations covering:
+        1. Strategic Initiatives
+        2. Operational Improvements
+        3. Financial Management
+        4. Risk Mitigation
+        5. Growth Strategy"""
+        
+        response = self.recommendation_generator(
+            prompt,
+            max_length=800,
+            temperature=0.6
+        )
+        return response[0]['generated_text']
+
+def analyze_financial_statements(income_statement, balance_sheet):
+    """Main analysis function"""
+    try:
+        # Read files
+        income_df = pd.read_csv(income_statement.name)
+        balance_df = pd.read_csv(balance_sheet.name)
+        
+        # Initialize analyzer
+        analyzer = FinancialAnalyzer()
+        
+        # Prepare financial data
+        financial_data = f"""
+        Income Statement Summary:
+        {income_df.to_string()}
+        
+        Balance Sheet Summary:
+        {balance_df.to_string()}
+        """
+        
+        # Generate strategic analysis
+        strategic_analysis = analyzer.generate_strategic_analysis(financial_data)
+        
+        # Analyze sentiment
+        sentiment = analyzer.analyze_sentiment(strategic_analysis)
+        
+        # Generate recommendations
+        recommendations = analyzer.generate_recommendations(strategic_analysis)
+        
+        # Format output
+        output = format_results(strategic_analysis, sentiment, recommendations)
+        
+        return output
+        
+    except Exception as e:
+        return f"Error analyzing files: {str(e)}"
+
+def format_results(analysis, sentiment, recommendations):
+    """Format analysis results"""
+    output = "# Financial Analysis Report\n\n"
+    
+    # Strategic Analysis
+    output += "## Strategic Analysis\n\n"
+    output += analysis + "\n\n"
+    
+    # Sentiment Analysis
+    output += "## Market Sentiment\n\n"
+    for score in sentiment[0]:
+        output += f"- {score['label']}: {score['score']:.2%}\n"
+    output += "\n"
+    
+    # Recommendations
+    output += "## Strategic Recommendations\n\n"
+    output += recommendations
+    
+    return output
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=analyze_financial_statements,
+    inputs=[
+        gr.File(label="Income Statement (CSV)"),
+        gr.File(label="Balance Sheet (CSV)")
+    ],
+    outputs=gr.Markdown(),
+    title="AI-Powered Financial Statement Analysis",
+    description="""Upload your financial statements for comprehensive analysis using:
+    - Llama 2: Strategic Analysis
+    - FinBERT: Financial Sentiment Analysis
+    - Falcon: Strategic Recommendations""",
+    examples=[
+        [
+            "OFINTECH-Income Statement-template.csv",
+            "OFINTECH Balance Sheet template.csv"
+        ]
+    ]
+)
+
+# Launch the interface
+if __name__ == "__main__":
     iface.launch()