Refactor app.py: Improve code readability and structure by consolidating conditional statements, enhancing string formatting, and ensuring consistent spacing throughout the analysis functions for budget, portfolio, and stock data.

app.py

@@ -37,33 +37,37 @@ def analyze_data_with_repl(data_type, data):
             if categories and values:
                 total_expenses = sum(values)
                 analysis_text = "💰 **Comprehensive Budget Analysis**\n\n"
-                
+
                 # Income vs Expenses Overview
                 analysis_text += "## 📈 **Income vs Expenses Overview**\n"
                 analysis_text += f"- **Monthly Income**: ${income:,.0f}\n"
                 analysis_text += f"- **Total Expenses**: ${total_expenses:,.0f}\n"
-                
+
                 if income > 0:
                     remaining = income - total_expenses
                     savings_rate = (remaining / income * 100) if income > 0 else 0
-                    
+
                     if remaining > 0:
                         analysis_text += f"- **💚 Surplus**: ${remaining:,.0f}\n"
                         analysis_text += f"- **💎 Savings Rate**: {savings_rate:.1f}%\n"
                     else:
                         analysis_text += f"- **🔴 Deficit**: ${abs(remaining):,.0f}\n"
-                        analysis_text += f"- **⚠️ Overspending**: {abs(savings_rate):.1f}%\n"
-                
+                        analysis_text += (
+                            f"- **⚠️ Overspending**: {abs(savings_rate):.1f}%\n"
+                        )
+
                 # Expense Breakdown with Progress Bars
                 analysis_text += "\n## 💳 **Expense Breakdown**\n"
                 for i, (category, amount) in enumerate(zip(categories, values)):
-                    percentage = (amount / total_expenses * 100) if total_expenses > 0 else 0
+                    percentage = (
+                        (amount / total_expenses * 100) if total_expenses > 0 else 0
+                    )
                     income_percentage = (amount / income * 100) if income > 0 else 0
                     bar = "█" * min(int(percentage / 3), 30)  # Max 30 chars
-                    
+
                     analysis_text += f"**{category.title()}**: ${amount:,.0f}\n"
                     analysis_text += f"  └─ {percentage:.1f}% of expenses | {income_percentage:.1f}% of income {bar}\n\n"
-                
+
                 # Financial Health Metrics
                 analysis_text += "## 📊 **Financial Health Metrics**\n"
                 avg_expense = total_expenses / len(values)
@@ -71,42 +75,54 @@ def analyze_data_with_repl(data_type, data):
                 smallest_expense = min(values)
                 largest_category = categories[values.index(largest_expense)]
                 smallest_category = categories[values.index(smallest_expense)]
-                
-                analysis_text += f"- **Average Category Expense**: ${avg_expense:,.0f}\n"
+
+                analysis_text += (
+                    f"- **Average Category Expense**: ${avg_expense:,.0f}\n"
+                )
                 analysis_text += f"- **Highest Expense**: {largest_category} (${largest_expense:,.0f})\n"
                 analysis_text += f"- **Lowest Expense**: {smallest_category} (${smallest_expense:,.0f})\n"
-                analysis_text += f"- **Expense Range**: ${largest_expense - smallest_expense:,.0f}\n"
-                
+                analysis_text += (
+                    f"- **Expense Range**: ${largest_expense - smallest_expense:,.0f}\n"
+                )
+
                 # Budget Recommendations
                 analysis_text += "\n## 💡 **Smart Budget Insights**\n"
-                
+
                 # 50/30/20 Rule Analysis
                 if income > 0:
                     needs_target = income * 0.50
                     wants_target = income * 0.30
                     savings_target = income * 0.20
-                    
-                    analysis_text += f"**50/30/20 Rule Comparison:**\n"
+
+                    analysis_text += "**50/30/20 Rule Comparison:**\n"
                     analysis_text += f"- Needs Target (50%): ${needs_target:,.0f}\n"
                     analysis_text += f"- Wants Target (30%): ${wants_target:,.0f}\n"
                     analysis_text += f"- Savings Target (20%): ${savings_target:,.0f}\n"
-                    
+
                     if savings_rate >= 20:
                         analysis_text += "✅ **Excellent savings rate!**\n"
                     elif savings_rate >= 10:
                         analysis_text += "⚠️ **Good savings, aim for 20%**\n"
                     else:
-                        analysis_text += "🔴 **Consider reducing expenses to save more**\n"
-                
+                        analysis_text += (
+                            "🔴 **Consider reducing expenses to save more**\n"
+                        )
+
                 # Category Warnings
                 for category, amount in zip(categories, values):
                     if income > 0:
-                        cat_percentage = (amount / income * 100)
-                        if category.lower() in ['rent', 'housing'] and cat_percentage > 30:
+                        cat_percentage = amount / income * 100
+                        if (
+                            category.lower() in ["rent", "housing"]
+                            and cat_percentage > 30
+                        ):
                             analysis_text += f"⚠️ **Housing costs high**: {cat_percentage:.1f}% (recommend <30%)\n"
-                        elif category.lower() in ['food', 'dining'] and cat_percentage > 15:
+                        elif (
+                            category.lower() in ["food", "dining"]
+                            and cat_percentage > 15
+                        ):
                             analysis_text += f"⚠️ **Food costs high**: {cat_percentage:.1f}% (recommend <15%)\n"
-                
+
                 return analysis_text
         except Exception as e:
             return f"Error analyzing budget data: {str(e)}"
@@ -116,42 +132,52 @@ def analyze_data_with_repl(data_type, data):
             portfolio_data = json.loads(data)
             holdings = portfolio_data.get("holdings", [])
             total_value = sum(holding.get("value", 0) for holding in holdings)
-            
+
             analysis_text = "📊 **Advanced Portfolio Analysis**\n\n"
-            
+
             # Portfolio Overview
             analysis_text += "## 💼 **Portfolio Overview**\n"
             analysis_text += f"- **Total Portfolio Value**: ${total_value:,.2f}\n"
             analysis_text += f"- **Number of Holdings**: {len(holdings)}\n"
-            
+
             if holdings:
                 values = [holding.get("value", 0) for holding in holdings]
                 avg_holding = sum(values) / len(values)
                 max_holding = max(values)
                 min_holding = min(values)
-                
+
                 analysis_text += f"- **Average Holding Size**: ${avg_holding:,.2f}\n"
                 analysis_text += f"- **Largest Position**: ${max_holding:,.2f}\n"
                 analysis_text += f"- **Smallest Position**: ${min_holding:,.2f}\n"
-            
+
             # Detailed Holdings breakdown
             analysis_text += "\n## 📈 **Holdings Breakdown**\n"
-            sorted_holdings = sorted(holdings, key=lambda x: x.get("value", 0), reverse=True)
-            
+            sorted_holdings = sorted(
+                holdings, key=lambda x: x.get("value", 0), reverse=True
+            )
+
             for i, holding in enumerate(sorted_holdings, 1):
                 symbol = holding.get("symbol", "Unknown")
                 value = holding.get("value", 0)
                 shares = holding.get("shares", 0)
-                allocation = holding.get("allocation", (value/total_value*100) if total_value > 0 else 0)
+                allocation = holding.get(
+                    "allocation", (value / total_value * 100) if total_value > 0 else 0
+                )
                 sector = holding.get("sector", "Unknown")
-                
+
                 # Calculate position concentration risk
-                risk_level = "🟢 Low" if allocation < 10 else "🟡 Medium" if allocation < 25 else "🔴 High"
-                
+                risk_level = (
+                    "🟢 Low"
+                    if allocation < 10
+                    else "🟡 Medium"
+                    if allocation < 25
+                    else "🔴 High"
+                )
+
                 analysis_text += f"**#{i} {symbol}** - {sector}\n"
                 analysis_text += f"  └─ Value: ${value:,.2f} | Shares: {shares:,.0f} | Weight: {allocation:.1f}%\n"
                 analysis_text += f"  └─ Concentration Risk: {risk_level}\n\n"
-            
+
             # Sector analysis with advanced metrics
             sectors = {}
             sector_values = {}
@@ -159,18 +185,20 @@ def analyze_data_with_repl(data_type, data):
                 sector = holding.get("sector", "Unknown")
                 allocation = holding.get("allocation", 0)
                 value = holding.get("value", 0)
-                
+
                 sectors[sector] = sectors.get(sector, 0) + allocation
                 sector_values[sector] = sector_values.get(sector, 0) + value
-            
+
             if sectors:
                 analysis_text += "## 🏭 **Sector Diversification Analysis**\n"
-                sorted_sectors = sorted(sectors.items(), key=lambda x: x[1], reverse=True)
-                
+                sorted_sectors = sorted(
+                    sectors.items(), key=lambda x: x[1], reverse=True
+                )
+
                 for sector, allocation in sorted_sectors:
                     bar = "█" * min(int(allocation / 2), 30)
                     value = sector_values.get(sector, 0)
-                    
+
                     # Sector concentration assessment
                     if allocation > 40:
                         risk_emoji = "🔴"
@@ -181,13 +209,13 @@ def analyze_data_with_repl(data_type, data):
                     else:
                         risk_emoji = "🟢"
                         risk_text = "Well diversified"
-                    
+
                     analysis_text += f"**{sector}**: {allocation:.1f}% (${value:,.2f}) {risk_emoji}\n"
                     analysis_text += f"  └─ {bar} {risk_text}\n\n"
-            
+
             # Portfolio Health Metrics
             analysis_text += "## 🎯 **Portfolio Health Assessment**\n"
-            
+
             # Diversification Score
             num_sectors = len(sectors)
             if num_sectors >= 8:
@@ -196,42 +224,46 @@ def analyze_data_with_repl(data_type, data):
                 diversification = "🟡 Good"
             else:
                 diversification = "🔴 Poor"
-            
+
             analysis_text += f"- **Sector Diversification**: {diversification} ({num_sectors} sectors)\n"
-            
+
             # Concentration Risk
             if holdings:
-                top_3_allocation = sum(sorted([h.get("allocation", 0) for h in holdings], reverse=True)[:3])
+                top_3_allocation = sum(
+                    sorted([h.get("allocation", 0) for h in holdings], reverse=True)[:3]
+                )
                 if top_3_allocation > 60:
                     concentration_risk = "🔴 High"
                 elif top_3_allocation > 40:
                     concentration_risk = "🟡 Medium"
                 else:
                     concentration_risk = "🟢 Low"
-                
+
                 analysis_text += f"- **Concentration Risk**: {concentration_risk} (Top 3: {top_3_allocation:.1f}%)\n"
-            
+
             # Portfolio Recommendations
             analysis_text += "\n## 💡 **Portfolio Optimization Recommendations**\n"
-            
+
             # Check for over-concentration
             for holding in holdings:
                 allocation = holding.get("allocation", 0)
                 if allocation > 25:
                     analysis_text += f"⚠️ **{holding.get('symbol', 'Unknown')}** is over-weighted at {allocation:.1f}% (consider rebalancing)\n"
-            
+
             # Sector recommendations
             for sector, allocation in sectors.items():
                 if allocation > 40:
                     analysis_text += f"⚠️ **{sector}** sector over-weighted at {allocation:.1f}% (consider diversification)\n"
-            
+
             # Diversification suggestions
             if num_sectors < 5:
                 analysis_text += "💡 **Consider adding exposure to more sectors for better diversification**\n"
-            
+
             if len(holdings) < 10:
-                analysis_text += "💡 **Consider adding more holdings to reduce single-stock risk**\n"
-            
+                analysis_text += (
+                    "💡 **Consider adding more holdings to reduce single-stock risk**\n"
+                )
+
             return analysis_text
         except Exception as e:
             return f"Error analyzing portfolio data: {str(e)}"
@@ -241,9 +273,9 @@ def analyze_data_with_repl(data_type, data):
             stock_data = json.loads(data)
             symbol = stock_data.get("symbol", "Unknown")
             price_str = stock_data.get("current_price", "0")
-            
+
             analysis_text = f"📈 **Comprehensive Stock Analysis: {symbol}**\n\n"
-            
+
             # Company Overview
             analysis_text += "## 🏢 **Company Overview**\n"
             analysis_text += f"- **Symbol**: {symbol}\n"
@@ -251,18 +283,20 @@ def analyze_data_with_repl(data_type, data):
             analysis_text += f"- **Company**: {stock_data.get('company_name', 'N/A')}\n"
             analysis_text += f"- **Sector**: {stock_data.get('sector', 'N/A')}\n"
             analysis_text += f"- **Industry**: {stock_data.get('industry', 'N/A')}\n"
-            analysis_text += f"- **Market Cap**: {stock_data.get('market_cap', 'N/A')}\n\n"
-            
+            analysis_text += (
+                f"- **Market Cap**: {stock_data.get('market_cap', 'N/A')}\n\n"
+            )
+
             # Financial Metrics
             financials = stock_data.get("financials", {})
             if financials:
                 analysis_text += "## 💹 **Key Financial Metrics**\n"
-                
+
                 # Valuation metrics
                 pe_ratio = financials.get("pe_ratio", "N/A")
                 pb_ratio = financials.get("pb_ratio", "N/A")
                 ps_ratio = financials.get("ps_ratio", "N/A")
-                
+
                 analysis_text += f"- **P/E Ratio**: {pe_ratio}"
                 if pe_ratio != "N/A" and isinstance(pe_ratio, (int, float)):
                     if pe_ratio < 15:
@@ -272,26 +306,28 @@ def analyze_data_with_repl(data_type, data):
                     else:
                         analysis_text += " 🟡 (Fairly Valued)"
                 analysis_text += "\n"
-                
+
                 analysis_text += f"- **P/B Ratio**: {pb_ratio}\n"
                 analysis_text += f"- **P/S Ratio**: {ps_ratio}\n"
-                
+
                 # Profitability metrics
                 analysis_text += f"- **ROE**: {financials.get('roe', 'N/A')}\n"
                 analysis_text += f"- **ROA**: {financials.get('roa', 'N/A')}\n"
-                analysis_text += f"- **Profit Margin**: {financials.get('profit_margin', 'N/A')}\n"
+                analysis_text += (
+                    f"- **Profit Margin**: {financials.get('profit_margin', 'N/A')}\n"
+                )
                 analysis_text += f"- **Revenue Growth**: {financials.get('revenue_growth', 'N/A')}\n\n"
-            
+
             # Performance analysis with trend indicators
             performance = stock_data.get("performance", {})
             if performance:
                 analysis_text += "## 📊 **Performance Analysis**\n"
-                
+
                 periods = ["1d", "1w", "1m", "3m", "6m", "1y", "ytd"]
                 for period in periods:
                     if period in performance:
                         return_pct = performance[period]
-                        
+
                         # Add trend indicators
                         if isinstance(return_pct, str) and "%" in return_pct:
                             try:
@@ -306,19 +342,21 @@ def analyze_data_with_repl(data_type, data):
                                 trend = ""
                         else:
                             trend = ""
-                        
-                        analysis_text += f"- **{period.upper()}**: {return_pct} {trend}\n"
+
+                        analysis_text += (
+                            f"- **{period.upper()}**: {return_pct} {trend}\n"
+                        )
                 analysis_text += "\n"
-            
+
             # Advanced Risk Assessment
             risk_data = stock_data.get("risk_assessment", {})
             if risk_data:
                 analysis_text += "## ⚠️ **Risk Assessment**\n"
-                
-                risk_level = risk_data.get('risk_level', 'N/A')
-                volatility = risk_data.get('volatility_30d', 'N/A')
-                beta = risk_data.get('beta', 'N/A')
-                
+
+                risk_level = risk_data.get("risk_level", "N/A")
+                volatility = risk_data.get("volatility_30d", "N/A")
+                beta = risk_data.get("beta", "N/A")
+
                 # Risk level with emoji indicators
                 if risk_level.lower() == "low":
                     risk_emoji = "🟢"
@@ -328,11 +366,11 @@ def analyze_data_with_repl(data_type, data):
                     risk_emoji = "🔴"
                 else:
                     risk_emoji = ""
-                
+
                 analysis_text += f"- **Risk Level**: {risk_level} {risk_emoji}\n"
                 analysis_text += f"- **30-Day Volatility**: {volatility}\n"
                 analysis_text += f"- **Beta**: {beta}"
-                
+
                 if beta != "N/A" and isinstance(beta, (int, float)):
                     if beta > 1.2:
                         analysis_text += " (High volatility vs market)"
@@ -341,7 +379,7 @@ def analyze_data_with_repl(data_type, data):
                     else:
                         analysis_text += " (Similar to market)"
                 analysis_text += "\n\n"
-            
+
             # Technical Analysis
             technical = stock_data.get("technical_analysis", {})
             if technical:
@@ -349,18 +387,22 @@ def analyze_data_with_repl(data_type, data):
                 analysis_text += f"- **50-Day MA**: {technical.get('ma_50', 'N/A')}\n"
                 analysis_text += f"- **200-Day MA**: {technical.get('ma_200', 'N/A')}\n"
                 analysis_text += f"- **RSI**: {technical.get('rsi', 'N/A')}\n"
-                analysis_text += f"- **Support Level**: {technical.get('support', 'N/A')}\n"
-                analysis_text += f"- **Resistance Level**: {technical.get('resistance', 'N/A')}\n\n"
-            
+                analysis_text += (
+                    f"- **Support Level**: {technical.get('support', 'N/A')}\n"
+                )
+                analysis_text += (
+                    f"- **Resistance Level**: {technical.get('resistance', 'N/A')}\n\n"
+                )
+
             # Investment Recommendation with detailed reasoning
             recommendation = stock_data.get("recommendation", {})
             if recommendation:
-                action = recommendation.get('action', 'N/A')
-                confidence = recommendation.get('confidence', 'N/A')
-                reasoning = recommendation.get('reasoning', '')
-                
+                action = recommendation.get("action", "N/A")
+                confidence = recommendation.get("confidence", "N/A")
+                reasoning = recommendation.get("reasoning", "")
+
                 analysis_text += "## 💡 **Investment Recommendation**\n"
-                
+
                 # Action with emoji
                 if action.lower() == "buy":
                     action_emoji = "🟢"
@@ -370,38 +412,38 @@ def analyze_data_with_repl(data_type, data):
                     action_emoji = "🟡"
                 else:
                     action_emoji = ""
-                
+
                 analysis_text += f"- **Action**: {action} {action_emoji}\n"
                 analysis_text += f"- **Confidence**: {confidence}\n"
-                
+
                 if reasoning:
                     analysis_text += f"- **Reasoning**: {reasoning}\n"
-                
+
                 analysis_text += "\n"
-            
+
             # Additional Investment Considerations
             analysis_text += "## 🎯 **Investment Considerations**\n"
-            
+
             # Dividend info
             dividend_yield = stock_data.get("dividend_yield", "N/A")
             if dividend_yield != "N/A":
                 analysis_text += f"- **Dividend Yield**: {dividend_yield}\n"
-            
+
             # Analyst ratings
             analyst_rating = stock_data.get("analyst_rating", "N/A")
             if analyst_rating != "N/A":
                 analysis_text += f"- **Analyst Rating**: {analyst_rating}\n"
-            
+
             # Price targets
             price_target = stock_data.get("price_target", "N/A")
             if price_target != "N/A":
                 analysis_text += f"- **Price Target**: {price_target}\n"
-            
+
             # ESG score
             esg_score = stock_data.get("esg_score", "N/A")
             if esg_score != "N/A":
                 analysis_text += f"- **ESG Score**: {esg_score}\n"
-            
+
             return analysis_text
         except Exception as e:
             return f"Error analyzing stock data: {str(e)}"
@@ -412,53 +454,115 @@ def analyze_data_with_repl(data_type, data):
 def determine_intended_tool(message):
     """Determine which tool the AI intends to use based on the message"""
     message_lower = message.lower()
-    
+
     tool_detection_map = {
-        "budget_planner": ["budget", "income", "expense", "spending", "allocat", "monthly", "plan", "financial plan", "money", "track", "categoriz", "cost"],
-        "investment_analyzer": ["stock", "invest", "buy", "sell", "analyze", "AAPL", "GOOGL", "TSLA", "share", "equity"],
-        "portfolio_analyzer": ["portfolio", "holdings", "allocation", "diversif", "asset", "position"],
-        "market_trends": ["market", "trend", "news", "sector", "economic", "latest", "current"]
+        "budget_planner": [
+            "budget",
+            "income",
+            "expense",
+            "spending",
+            "allocat",
+            "monthly",
+            "plan",
+            "financial plan",
+            "money",
+            "track",
+            "categoriz",
+            "cost",
+        ],
+        "investment_analyzer": [
+            "stock",
+            "invest",
+            "buy",
+            "sell",
+            "analyze",
+            "AAPL",
+            "GOOGL",
+            "TSLA",
+            "share",
+            "equity",
+        ],
+        "portfolio_analyzer": [
+            "portfolio",
+            "holdings",
+            "allocation",
+            "diversif",
+            "asset",
+            "position",
+        ],
+        "market_trends": [
+            "market",
+            "trend",
+            "news",
+            "sector",
+            "economic",
+            "latest",
+            "current",
+        ],
     }
-    
+
     tool_names = {
         "budget_planner": "Budget Planner",
         "investment_analyzer": "Investment Analyzer",
         "market_trends": "Market Trends Analyzer",
         "portfolio_analyzer": "Portfolio Analyzer",
     }
-    
+
     for tool_key, keywords in tool_detection_map.items():
         if any(keyword in message_lower for keyword in keywords):
             return tool_key, tool_names.get(tool_key, tool_key)
-    
+
     return None, None
 
 
 def determine_response_type(message):
     """Determine if user wants detailed report or short response"""
     message_lower = message.lower()
-    
+
     # Keywords indicating detailed response preference
     detailed_keywords = [
-        "detailed", "detail", "comprehensive", "thorough", "in-depth", "full analysis", 
-        "complete", "report", "breakdown", "explain", "elaborate", "deep dive",
-        "extensive", "detailed analysis", "full report", "comprehensive report"
+        "detailed",
+        "detail",
+        "comprehensive",
+        "thorough",
+        "in-depth",
+        "full analysis",
+        "complete",
+        "report",
+        "breakdown",
+        "explain",
+        "elaborate",
+        "deep dive",
+        "extensive",
+        "detailed analysis",
+        "full report",
+        "comprehensive report",
     ]
-    
-    # Keywords indicating short response preference  
+
+    # Keywords indicating short response preference
     short_keywords = [
-        "quick", "brief", "short", "summary", "concise", "simple", "fast",
-        "just tell me", "quickly", "in short", "tldr", "bottom line"
+        "quick",
+        "brief",
+        "short",
+        "summary",
+        "concise",
+        "simple",
+        "fast",
+        "just tell me",
+        "quickly",
+        "in short",
+        "tldr",
+        "bottom line",
     ]
-    
+
     # Check for detailed indicators first
     if any(keyword in message_lower for keyword in detailed_keywords):
         return "detailed"
-    
+
     # Check for short indicators
     if any(keyword in message_lower for keyword in short_keywords):
         return "short"
-    
+
     # Default to short response
     return "short"
 
@@ -494,37 +598,46 @@ def process_financial_query(message, history):
     # Start timer
     start_time = time.time()
     init_message_start_index = len(history)
-    
+
     try:
         # Show what tool will be used and processing status
-        intended_tool_key, intended_tool_name = determine_intended_tool(last_user_message)
+        intended_tool_key, intended_tool_name = determine_intended_tool(
+            last_user_message
+        )
         response_type = determine_response_type(last_user_message)
-        
+
         # Always show status for all tools with expected time estimates
         if intended_tool_name:
             if intended_tool_key == "market_trends":
-                status_msg = f"🔍 Fetching market news & analyzing trends (estimated 20-30 seconds)..."
+                status_msg = "🔍 Fetching market news & analyzing trends (estimated 20-30 seconds)..."
             elif intended_tool_key == "investment_analyzer":
-                status_msg = f"📈 Analyzing stock data & calculating metrics (estimated 10-15 seconds)..."
+                status_msg = "📈 Analyzing stock data & calculating metrics (estimated 10-15 seconds)..."
             elif intended_tool_key == "budget_planner":
-                status_msg = f"💰 Processing budget analysis (estimated 5-10 seconds)..."
+                status_msg = "💰 Processing budget analysis (estimated 5-10 seconds)..."
             elif intended_tool_key == "portfolio_analyzer":
-                status_msg = f"📊 Analyzing portfolio data (estimated 8-12 seconds)..."
+                status_msg = "📊 Analyzing portfolio data (estimated 8-12 seconds)..."
             else:
-                status_msg = f"🔄 Using {intended_tool_name} (estimated 5-15 seconds)..."
-            
+                status_msg = (
+                    f"🔄 Using {intended_tool_name} (estimated 5-15 seconds)..."
+                )
+
             history.append(ChatMessage(role="assistant", content=status_msg))
             yield history
         else:
             # If no tool detected, show generic processing message
-            history.append(ChatMessage(role="assistant", content=f"🧠 Processing your request (estimated 10-15 seconds)..."))
+            history.append(
+                ChatMessage(
+                    role="assistant",
+                    content="🧠 Processing your request (estimated 10-15 seconds)...",
+                )
+            )
             yield history
-        
+
         # Process message through agent
-        response, tool_used, tool_result, all_tools, all_results = agent.process_message_with_details(
-            last_user_message, agent_history
+        response, tool_used, tool_result, all_tools, all_results = (
+            agent.process_message_with_details(last_user_message, agent_history)
         )
-        
+
         # Clear the processing message now that tool is complete
         if len(history) > init_message_start_index:
             history.pop()  # Remove the processing message
@@ -532,25 +645,25 @@ def process_financial_query(message, history):
         if all_tools and all_results:
             # Remove initialization messages but keep all previous conversation and tool info
             history = history[:init_message_start_index]
-            
+
             tool_names = {
                 "budget_planner": "Budget Planner",
                 "investment_analyzer": "Investment Analyzer",
                 "market_trends": "Market Trends Analyzer",
                 "portfolio_analyzer": "Portfolio Analyzer",
             }
-            
+
             tool_emojis = {
                 "Budget Planner": "💰",
                 "Investment Analyzer": "📈",
                 "Market Trends Analyzer": "📰",
                 "Portfolio Analyzer": "📊",
             }
-            
+
             # Show results for all tools used
             for i, (used_tool, result) in enumerate(zip(all_tools, all_results)):
                 tool_display_name = tool_names.get(used_tool, used_tool)
-                
+
                 if result:
                     # Format tool result for display
                     try:
@@ -578,11 +691,9 @@ def process_financial_query(message, history):
                         else:
                             # Truncate non-JSON results
                             display_result = (
-                                result[:1000] + "..."
-                                if len(result) > 1000
-                                else result
+                                result[:1000] + "..." if len(result) > 1000 else result
                             )
-                    except Exception as e:
+                    except Exception:
                         display_result = (
                             str(result)[:1000] + "..."
                             if len(str(result)) > 1000
@@ -590,7 +701,7 @@ def process_financial_query(message, history):
                         )
 
                     tool_emoji = tool_emojis.get(tool_display_name, "🔧")
-                    
+
                     collapsible_content = f"""
 <details>
 <summary><strong>{tool_emoji} {tool_display_name} Results</strong> - Click to expand</summary>
@@ -599,20 +710,26 @@ def process_financial_query(message, history):
 
 </details>
 """
-                    
-                    history.append(ChatMessage(
-                        role="assistant",
-                        content=collapsible_content,
-                    ))
+
+                    history.append(
+                        ChatMessage(
+                            role="assistant",
+                            content=collapsible_content,
+                        )
+                    )
                     yield history
 
         # Add visualization for all applicable tools
         if all_tools and all_results:
             for used_tool, result in zip(all_tools, all_results):
-                if result and used_tool in ["budget_planner", "portfolio_analyzer", "investment_analyzer"]:
+                if result and used_tool in [
+                    "budget_planner",
+                    "portfolio_analyzer",
+                    "investment_analyzer",
+                ]:
                     viz_type = {
                         "budget_planner": "budget",
-                        "portfolio_analyzer": "portfolio", 
+                        "portfolio_analyzer": "portfolio",
                         "investment_analyzer": "stock",
                     }.get(used_tool)
 
@@ -625,7 +742,7 @@ def process_financial_query(message, history):
                                 "portfolio_analyzer": "Portfolio",
                                 "investment_analyzer": "Stock",
                             }.get(used_tool, "Data")
-                            
+
                             # Create collapsible data analysis output
                             collapsible_analysis = f"""
 <details>
@@ -635,14 +752,16 @@ def process_financial_query(message, history):
 
 </details>
 """
-                            
-                            history.append(ChatMessage(
-                                role="assistant",
-                                content=collapsible_analysis,
-                            ))
+
+                            history.append(
+                                ChatMessage(
+                                    role="assistant",
+                                    content=collapsible_analysis,
+                                )
+                            )
                             yield history
 
-                    except Exception as e:
+                    except Exception:
                         # Silently continue if analysis fails
                         pass
 
@@ -651,8 +770,10 @@ def process_financial_query(message, history):
             # Use real LLM streaming with response type
             streaming_content = ""
             history.append(ChatMessage(role="assistant", content=""))
-            
-            for chunk in agent.stream_response(last_user_message, tool_result, tool_used, response_type):
+
+            for chunk in agent.stream_response(
+                last_user_message, tool_result, tool_used, response_type
+            ):
                 streaming_content += chunk
                 history[-1] = ChatMessage(role="assistant", content=streaming_content)
                 yield history
@@ -675,10 +796,11 @@ def process_financial_query(message, history):
 
 # Create the Gradio interface
 with gr.Blocks(theme=gr.themes.Base(), title="Financial Advisory Agent") as demo:
-    gr.HTML("""<center><img src="/gradio_api/file=public/images/fin_logo.png" alt="Fin Logo" style="width: 50px; vertical-align: middle;">
+    gr.HTML("""<div style="text-align: center;">
+    <img src="/gradio_api/file=public/images/fin_logo.png" alt="Fin Logo" style="width: 50px; vertical-align: middle;">
     <h1 style="text-align: center;">AI Financial Advisory Agent</h1>
-    Your AI-powered financial advisor for budgeting, investments, portfolio analysis, and market trends.
-                </center>
+    <p>Your AI-powered financial advisor for budgeting, investments, portfolio analysis, and market trends.</p>
+                </div>
     """)
 
     chatbot = gr.Chatbot(