Update main.py

main.py

@@ -1,557 +1,751 @@
-from fastapi import FastAPI, HTTPException, File, UploadFile
-from fastapi.responses import JSONResponse, FileResponse
-from fastapi.middleware.cors import CORSMiddleware
-from pydantic import BaseModel, ValidationError
-from typing import Optional, List, Dict, Any
-import json
-import matplotlib
-matplotlib.use('Agg')  # Non-interactive backend for server
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-from datetime import datetime
-import io
-import base64
-from PIL import Image
-import seaborn as sns
-import uvicorn
-import os
-import tempfile
-from pathlib import Path
-
-# Set style for better-looking charts
-plt.style.use('seaborn-v0_8')
-sns.set_palette("husl")
-
-# Pydantic models for request/response validation
-class HoldingModel(BaseModel):
-    stockName: str
-    shares: int
-    purchasePrice: float
-    currentPrice: float
-    investment: float
-    currentValue: float
-    profitLoss: float
-    profitLossPercentage: float
-    fiftyTwoWeekLow: Optional[float] = None
-    fiftyTwoWeekHigh: Optional[float] = None
-    positionInRange: Optional[float] = None
-    sector: Optional[str] = "N/A"
-
-class PortfolioSummaryModel(BaseModel):
-    totalInvested: float
-    currentValue: float
-    netPL: float
-    netPLPercentage: float
-    reportDate: str
-
-class AllocationModel(BaseModel):
-    stock: str
-    percentage: float
-    value: float
-
-class InsightsModel(BaseModel):
-    bestPerformer: Optional[Dict[str, Any]] = None
-    underperformer: Optional[Dict[str, Any]] = None
-
-class MarketContextModel(BaseModel):
-    niftyLevel: Optional[float] = None
-    portfolioVsMarket: Optional[str] = None
-    marketTrend: Optional[str] = None
-
-class PortfolioDataModel(BaseModel):
-    portfolioSummary: PortfolioSummaryModel
-    holdings: List[HoldingModel]
-    allocation: Optional[List[AllocationModel]] = []
-    insights: Optional[InsightsModel] = None
-    marketContext: Optional[MarketContextModel] = None
-
-class PortfolioResponse(BaseModel):
-    combined_chart: str  # base64 encoded
-    allocation_chart: str
-    performance_chart: str
-    comparison_chart: str
-    text_summary: str
-    telegram_message: str
-    status: str = "success"
-
-# Initialize FastAPI app
-app = FastAPI(
-    title="📊 Portfolio Analysis API",
-    description="Comprehensive portfolio analysis with charts and insights",
-    version="1.0.0",
-    docs_url="/",  # Swagger UI at root
-    redoc_url="/docs"
-)
-
-# Add CORS middleware
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# Create temp directory for charts
-TEMP_DIR = Path(tempfile.gettempdir()) / "portfolio_charts"
-TEMP_DIR.mkdir(exist_ok=True)
-
-class PortfolioAnalyzer:
-    """Portfolio analysis engine"""
-    
-    def __init__(self):
-        self.colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEAA7', '#DDA0DD', '#98D8C8']
-    
-    def analyze_portfolio(self, portfolio_data: PortfolioDataModel) -> PortfolioResponse:
-        """Main analysis function"""
-        try:
-            # Create charts
-            charts = self._create_charts(
-                portfolio_data.portfolioSummary,
-                portfolio_data.holdings,
-                portfolio_data.allocation or [],
-                portfolio_data.marketContext or MarketContextModel()
-            )
-            
-            # Create text summaries
-            text_summary = self._create_text_summary(
-                portfolio_data.portfolioSummary,
-                portfolio_data.holdings,
-                portfolio_data.insights or InsightsModel(),
-                portfolio_data.marketContext or MarketContextModel()
-            )
-            
-            telegram_msg = self._create_telegram_message(
-                portfolio_data.portfolioSummary,
-                portfolio_data.holdings,
-                portfolio_data.insights or InsightsModel()
-            )
-            
-            return PortfolioResponse(
-                combined_chart=charts['combined'],
-                allocation_chart=charts['allocation'],
-                performance_chart=charts['performance'],
-                comparison_chart=charts['comparison'],
-                text_summary=text_summary,
-                telegram_message=telegram_msg
-            )
-            
-        except Exception as e:
-            raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")
-    
-    def _create_charts(self, portfolio_summary, holdings, allocation, market_context):
-        """Create multiple chart types for portfolio visualization"""
-        
-        # 1. COMBINED DASHBOARD
-        fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 12))
-        fig.suptitle(f'Portfolio Dashboard - {portfolio_summary.reportDate}', fontsize=16, fontweight='bold')
-        
-        # Portfolio overview text
-        total_value = portfolio_summary.currentValue
-        total_pl = portfolio_summary.netPL
-        pl_pct = portfolio_summary.netPLPercentage
-        
-        overview_text = f'Total Value: ₹{total_value:,.0f} | P&L: ₹{total_pl:,.0f} ({pl_pct:+.2f}%) | Invested: ₹{portfolio_summary.totalInvested:,.0f}'
-        fig.text(0.5, 0.95, overview_text, ha='center', fontsize=12, bbox=dict(boxstyle="round,pad=0.5", facecolor='lightblue', alpha=0.7))
-        
-        # Allocation Pie Chart
-        if allocation:
-            stocks = [a.stock for a in allocation]
-            percentages = [a.percentage for a in allocation]
-            ax1.pie(percentages, labels=stocks, autopct='%1.1f%%', colors=self.colors[:len(stocks)], startangle=90)
-            ax1.set_title('Portfolio Allocation', fontweight='bold')
-        
-        # P&L Bar Chart
-        stock_names = [h.stockName.replace('.NS', '') for h in holdings]
-        profit_loss = [h.profitLoss for h in holdings]
-        bars = ax2.bar(stock_names, profit_loss, color=['green' if pl > 0 else 'red' for pl in profit_loss])
-        ax2.set_title('Profit/Loss by Stock', fontweight='bold')
-        ax2.set_ylabel('P&L (₹)')
-        ax2.tick_params(axis='x', rotation=45)
-        
-        # Performance Percentage Chart
-        perf_percentages = [h.profitLossPercentage for h in holdings]
-        ax3.bar(stock_names, perf_percentages, color=['green' if p > 0 else 'red' for p in perf_percentages])
-        ax3.set_title('Returns (%)', fontweight='bold')
-        ax3.set_ylabel('Return %')
-        ax3.tick_params(axis='x', rotation=45)
-        
-        # Holdings Value Chart
-        current_values = [h.currentValue for h in holdings]
-        investments = [h.investment for h in holdings]
-        x = np.arange(len(stock_names))
-        width = 0.35
-        ax4.bar(x - width/2, investments, width, label='Invested', color='lightblue')
-        ax4.bar(x + width/2, current_values, width, label='Current', color='darkblue')
-        ax4.set_title('Investment vs Current Value', fontweight='bold')
-        ax4.set_ylabel('Value (₹)')
-        ax4.set_xticks(x)
-        ax4.set_xticklabels(stock_names, rotation=45)
-        ax4.legend()
-        
-        plt.tight_layout()
-        combined_chart = self._save_chart_as_base64(fig)
-        plt.close(fig)
-        
-        # 2. ALLOCATION CHART
-        fig_alloc, ax_alloc = plt.subplots(figsize=(10, 8))
-        if allocation:
-            stocks = [a.stock for a in allocation]
-            percentages = [a.percentage for a in allocation]
-            wedges, texts, autotexts = ax_alloc.pie(
-                percentages, 
-                labels=stocks, 
-                autopct='%1.1f%%',
-                colors=self.colors[:len(stocks)], 
-                startangle=90,
-                explode=[0.05 if p == max(percentages) else 0 for p in percentages]
-            )
-            ax_alloc.set_title(f'Portfolio Allocation - Total: ₹{total_value:,.0f}', fontsize=16, fontweight='bold')
-        allocation_chart = self._save_chart_as_base64(fig_alloc)
-        plt.close(fig_alloc)
-        
-        # 3. PERFORMANCE ANALYSIS
-        fig_perf, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(14, 10))
-        
-        # P&L Bar Chart
-        ax1.bar(stock_names, profit_loss, color=['#2ECC71' if pl > 0 else '#E74C3C' for pl in profit_loss])
-        ax1.set_title('Profit/Loss (₹)', fontweight='bold')
-        ax1.tick_params(axis='x', rotation=45)
-        
-        # Percentage Returns
-        ax2.bar(stock_names, perf_percentages, color=['#2ECC71' if p > 0 else '#E74C3C' for p in perf_percentages])
-        ax2.set_title('Returns (%)', fontweight='bold')
-        ax2.tick_params(axis='x', rotation=45)
-        
-        # Investment vs Current Value
-        x = np.arange(len(stock_names))
-        width = 0.35
-        ax3.bar(x - width/2, investments, width, label='Invested', color='#3498DB', alpha=0.7)
-        ax3.bar(x + width/2, current_values, width, label='Current', color='#9B59B6', alpha=0.7)
-        ax3.set_title('Investment vs Current Value', fontweight='bold')
-        ax3.set_xticks(x)
-        ax3.set_xticklabels(stock_names, rotation=45)
-        ax3.legend()
-        
-        # Risk Analysis (52-week position)
-        positions = [h.positionInRange or 50 for h in holdings]
-        colors_risk = ['#E74C3C' if p < 20 else '#F39C12' if p < 60 else '#2ECC71' for p in positions]
-        ax4.bar(stock_names, positions, color=colors_risk)
-        ax4.set_title('52-Week Range Position (%)', fontweight='bold')
-        ax4.tick_params(axis='x', rotation=45)
-        ax4.axhline(y=20, color='red', linestyle='--', alpha=0.5)
-        ax4.axhline(y=80, color='green', linestyle='--', alpha=0.5)
-        
-        plt.tight_layout()
-        performance_chart = self._save_chart_as_base64(fig_perf)
-        plt.close(fig_perf)
-        
-        # 4. COMPARISON CHART
-        fig_comp, ax_comp = plt.subplots(figsize=(12, 6))
-        
-        metrics = ['Portfolio Return', 'Best Stock', 'Worst Stock', 'Market (Nifty)']
-        values = [
-            pl_pct,
-            max(perf_percentages) if perf_percentages else 0,
-            min(perf_percentages) if perf_percentages else 0,
-            -0.95
-        ]
-        
-        colors_comp = ['#3498DB', '#2ECC71', '#E74C3C', '#95A5A6']
-        bars_comp = ax_comp.bar(metrics, values, color=colors_comp)
-        
-        ax_comp.set_title('Performance Comparison', fontsize=16, fontweight='bold')
-        ax_comp.set_ylabel('Return (%)')
-        ax_comp.axhline(y=0, color='black', linestyle='-', alpha=0.3)
-        
-        # Add value labels
-        for bar, value in zip(bars_comp, values):
-            height = bar.get_height()
-            ax_comp.text(bar.get_x() + bar.get_width()/2., height,
-                        f'{value:+.2f}%', ha='center', va='bottom' if height > 0 else 'top')
-        
-        plt.xticks(rotation=45)
-        plt.tight_layout()
-        comparison_chart = self._save_chart_as_base64(fig_comp)
-        plt.close(fig_comp)
-        
-        return {
-            'combined': combined_chart,
-            'allocation': allocation_chart, 
-            'performance': performance_chart,
-            'comparison': comparison_chart
-        }
-    
-    def _save_chart_as_base64(self, fig):
-        """Convert matplotlib figure to base64 string"""
-        buf = io.BytesIO()
-        fig.savefig(buf, format='png', dpi=300, bbox_inches='tight')
-        buf.seek(0)
-        image_base64 = base64.b64encode(buf.getvalue()).decode('utf-8')
-        buf.close()
-        return image_base64
-    
-    def _create_text_summary(self, portfolio_summary, holdings, insights, market_context):
-        """Create detailed text summary"""
-        
-        total_stocks = len(holdings)
-        profitable_stocks = len([h for h in holdings if h.profitLoss > 0])
-        losing_stocks = len([h for h in holdings if h.profitLoss < 0])
-        
-        top_gainer = max(holdings, key=lambda x: x.profitLossPercentage) if holdings else None
-        top_loser = min(holdings, key=lambda x: x.profitLossPercentage) if holdings else None
-        
-        stocks_near_low = len([h for h in holdings if (h.positionInRange or 50) < 20])
-        
-        summary = f"""
-📊 PORTFOLIO ANALYSIS REPORT
-📅 Date: {portfolio_summary.reportDate}
-═══════════════════════════════════════
-💰 FINANCIAL OVERVIEW
-━━━━━━━━━━━━━━━━━━━━━━
-• Total Investment: ₹{portfolio_summary.totalInvested:,.2f}
-• Current Value: ₹{portfolio_summary.currentValue:,.2f}
-• Net P&L: ₹{portfolio_summary.netPL:,.2f} ({portfolio_summary.netPLPercentage:+.2f}%)
-• Portfolio Status: {'🟢 Profitable' if portfolio_summary.netPL > 0 else '🔴 Loss' if portfolio_summary.netPL < -1000 else '🟡 Neutral'}
-
-📈 STOCK PERFORMANCE
-━━━━━━━━━━━━━━━━━━━━━━
-• Total Stocks: {total_stocks}
-• Profitable: {profitable_stocks} | Losing: {losing_stocks}
-• Best Performer: {top_gainer.stockName.replace('.NS', '') if top_gainer else 'N/A'} ({top_gainer.profitLossPercentage:+.2f}% if top_gainer else 0)
-• Worst Performer: {top_loser.stockName.replace('.NS', '') if top_loser else 'N/A'} ({top_loser.profitLossPercentage:.2f}% if top_loser else 0)
-
-📊 INDIVIDUAL HOLDINGS
-━━━━━━━━━━━━━━━━━━━━━━"""
-
-        for holding in holdings:
-            status_emoji = "🟢" if holding.profitLoss > 0 else "🔴" if holding.profitLoss < 0 else "🟡"
-            summary += f"""
-{status_emoji} {holding.stockName.replace('.NS', '')}:
-   • Shares: {holding.shares} | Price: ₹{holding.currentPrice:.2f}
-   • Investment: ₹{holding.investment:,.0f} → Current: ₹{holding.currentValue:,.0f}
-   • P&L: ₹{holding.profitLoss:,.0f} ({holding.profitLossPercentage:+.2f}%)
-   • Sector: {holding.sector}"""
-
-        summary += f"""
-
-⚠️ RISK ANALYSIS
-━━━━━━━━━━━━━━━━━━━━━━
-• Stocks near 52W low: {stocks_near_low}
-• Risk Level: {'🔴 HIGH' if stocks_near_low > 2 else '🟡 MEDIUM' if stocks_near_low > 0 else '🟢 LOW'}
-
-📊 MARKET CONTEXT
-━━━━━━━━━━━━━━━━━━━━━━
-• Nifty 50: {market_context.niftyLevel or 'N/A'}
-• Portfolio vs Market: {market_context.portfolioVsMarket or 'N/A'}
-• Market Trend: {market_context.marketTrend or 'N/A'}
-
-💡 INSIGHTS & RECOMMENDATIONS
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"""
-
-        if portfolio_summary.netPLPercentage > 5:
-            summary += "\n• 🎯 Strong portfolio performance - consider partial profit booking"
-        elif portfolio_summary.netPLPercentage < -5:
-            summary += "\n• ⚠️ Portfolio underperforming - review individual positions"
-        
-        if stocks_near_low > 1:
-            summary += f"\n• 📉 {stocks_near_low} stocks near 52W lows - monitor closely"
-        
-        if profitable_stocks > losing_stocks:
-            summary += "\n• ✅ More winners than losers - good stock selection"
-        
-        summary += f"""
-
-🔸 Report generated at: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
-🔸 Disclaimer: This is for educational purposes only, not financial advice.
-"""
-        
-        return summary
-    
-    def _create_telegram_message(self, portfolio_summary, holdings, insights):
-        """Create Telegram-optimized message"""
-        
-        top_gainer = max(holdings, key=lambda x: x.profitLossPercentage) if holdings else None
-        top_loser = min(holdings, key=lambda x: x.profitLossPercentage) if holdings else None
-        
-        status_emoji = "📈" if portfolio_summary.netPL > 0 else "📉" if portfolio_summary.netPL < -1000 else "➡️"
-        
-        telegram_msg = f"""🏦 PORTFOLIO UPDATE
-📅 {portfolio_summary.reportDate}
-
-{status_emoji} PERFORMANCE
-💰 Value: ₹{portfolio_summary.currentValue:,.0f}
-📊 P&L: {'+' if portfolio_summary.netPL > 0 else ''}₹{portfolio_summary.netPL:,.0f} ({portfolio_summary.netPLPercentage:+.2f}%)
-
-🏆 TOP MOVES
-📈 Best: {top_gainer.stockName.replace('.NS', '') if top_gainer else 'N/A'} {top_gainer.profitLossPercentage:+.1f}% if top_gainer else 0
-📉 Worst: {top_loser.stockName.replace('.NS', '') if top_loser else 'N/A'} {top_loser.profitLossPercentage:.1f}% if top_loser else 0
-
-📊 HOLDINGS"""
-
-        for holding in holdings[:5]:  # Top 5 holdings
-            emoji = "🟢" if holding.profitLoss > 0 else "🔴"
-            telegram_msg += f"\n{emoji} {holding.stockName.replace('.NS', '')}: {holding.profitLossPercentage:+.1f}%"
-        
-        telegram_msg += f"\n\n💡 Status: {len([h for h in holdings if h.profitLoss > 0])}/{len(holdings)} profitable"
-        
-        return telegram_msg
-
-# Initialize analyzer
-analyzer = PortfolioAnalyzer()
-
-@app.get("/", response_class=JSONResponse)
-async def root():
-    """API Health Check"""
-    return {
-        "message": "Portfolio Analysis API is running!",
-        "status": "healthy",
-        "endpoints": {
-            "/analyze": "POST - Analyze portfolio data",
-            "/analyze-file": "POST - Upload JSON file for analysis",
-            "/sample": "GET - Get sample portfolio data",
-            "/docs": "GET - API documentation"
-        }
-    }
-
-@app.post("/analyze", response_model=PortfolioResponse)
-async def analyze_portfolio(portfolio_data: PortfolioDataModel):
-    """
-    Analyze portfolio data and generate comprehensive reports
-    
-    - **portfolio_data**: Complete portfolio data including summary, holdings, allocation
-    - Returns: Charts (base64), text summary, and Telegram message
-    """
-    try:
-        result = analyzer.analyze_portfolio(portfolio_data)
-        return result
-    except ValidationError as e:
-        raise HTTPException(status_code=422, detail=f"Invalid data format: {e}")
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")
-
-@app.post("/analyze-file")
-async def analyze_portfolio_file(file: UploadFile = File(...)):
-    """
-    Upload JSON file and analyze portfolio
-    
-    - **file**: JSON file containing portfolio data
-    - Returns: Charts (base64), text summary, and Telegram message
-    """
-    try:
-        # Validate file type
-        if not file.filename.endswith('.json'):
-            raise HTTPException(status_code=400, detail="Please upload a JSON file")
-        
-        # Read and parse JSON
-        content = await file.read()
-        portfolio_data = json.loads(content.decode('utf-8'))
-        
-        # Validate using Pydantic model
-        validated_data = PortfolioDataModel(**portfolio_data)
-        
-        # Analyze portfolio
-        result = analyzer.analyze_portfolio(validated_data)
-        return result
-        
-    except json.JSONDecodeError:
-        raise HTTPException(status_code=400, detail="Invalid JSON format")
-    except ValidationError as e:
-        raise HTTPException(status_code=422, detail=f"Invalid data structure: {e}")
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")
-
-@app.get("/sample")
-async def get_sample_data():
-    """Get sample portfolio data for testing"""
-    sample_data = {
-        "portfolioSummary": {
-            "totalInvested": 213600,
-            "currentValue": 214327.5,
-            "netPL": 727.5,
-            "netPLPercentage": 0.34,
-            "reportDate": "September 27, 2025"
-        },
-        "holdings": [
-            {
-                "stockName": "RELIANCE.NS",
-                "shares": 20,
-                "purchasePrice": 1400,
-                "currentPrice": 1375,
-                "investment": 28000,
-                "currentValue": 27500,
-                "profitLoss": -500,
-                "profitLossPercentage": -1.79,
-                "fiftyTwoWeekLow": 1114.85,
-                "fiftyTwoWeekHigh": 1551,
-                "positionInRange": 59.5,
-                "sector": "Energy"
-            },
-            {
-                "stockName": "HDFCBANK.NS",
-                "shares": 22,
-                "purchasePrice": 800,
-                "currentPrice": 950,
-                "investment": 17600,
-                "currentValue": 20900,
-                "profitLoss": 3300,
-                "profitLossPercentage": 18.75,
-                "fiftyTwoWeekLow": 806.5,
-                "fiftyTwoWeekHigh": 1018.85,
-                "positionInRange": 67.6,
-                "sector": "Banking"
-            },
-            {
-                "stockName": "ITC.NS",
-                "shares": 30,
-                "purchasePrice": 500,
-                "currentPrice": 403,
-                "investment": 15000,
-                "currentValue": 12090,
-                "profitLoss": -2910,
-                "profitLossPercentage": -19.4,
-                "fiftyTwoWeekLow": 390.15,
-                "fiftyTwoWeekHigh": 528.5,
-                "positionInRange": 9.3,
-                "sector": "FMCG"
-            }
-        ],
-        "allocation": [
-            {"stock": "HDFC", "percentage": 30.4, "value": 20900},
-            {"stock": "RIL", "percentage": 40.0, "value": 27500},
-            {"stock": "ITC", "percentage": 29.6, "value": 12090}
-        ],
-        "insights": {
-            "bestPerformer": {"stock": "HDFC", "returnPercentage": 18.75},
-            "underperformer": {"stock": "ITC", "returnPercentage": -19.4}
-        },
-        "marketContext": {
-            "niftyLevel": 24654.7,
-            "portfolioVsMarket": "Slightly outperforming with +0.34% overall return",
-            "marketTrend": "Cautious amid global uncertainties"
-        }
-    }
-    return sample_data
-
-@app.get("/health")
-async def health_check():
-    """Detailed health check"""
-    return {
-        "status": "healthy",
-        "timestamp": datetime.now().isoformat(),
-        "version": "1.0.0",
-        "dependencies": {
-            "matplotlib": "available",
-            "pandas": "available",
-            "numpy": "available"
-        }
-    }
-
-if __name__ == "__main__":
-    uvicorn.run(
-        "main:app",
-        host="0.0.0.0",
-        port=int(os.getenv("PORT", 7860)),  # HuggingFace default port
-        reload=False
+from fastapi import FastAPI, HTTPException, File, UploadFile
+from fastapi.responses import JSONResponse, FileResponse
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, ValidationError, Field
+from typing import Optional, List, Dict, Any, Union
+import json
+import matplotlib
+matplotlib.use('Agg')  # Non-interactive backend for server
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from datetime import datetime
+import io
+import base64
+from PIL import Image
+import seaborn as sns
+import uvicorn
+import os
+import tempfile
+from pathlib import Path
+
+# Set style for better-looking charts
+plt.style.use('seaborn-v0_8')
+sns.set_palette("husl")
+
+# Enhanced Pydantic models to handle extended data structure
+class ReportMetadataModel(BaseModel):
+    generatedDate: Optional[str] = None
+    reportDate: Optional[str] = None
+    status: Optional[str] = None
+    dataSource: Optional[str] = None
+
+class HoldingModel(BaseModel):
+    stockName: str
+    shares: int
+    purchasePrice: float
+    currentPrice: float
+    investment: float
+    currentValue: float
+    profitLoss: float
+    profitLossPercentage: float
+    fiftyTwoWeekLow: Optional[float] = None
+    fiftyTwoWeekHigh: Optional[float] = None
+    positionInRange: Optional[float] = None
+    sector: Optional[str] = "N/A"
+    volume: Optional[str] = None
+    educationalNote: Optional[str] = None
+    lastUpdated: Optional[str] = None
+    reportId: Optional[str] = None
+
+class PortfolioSummaryModel(BaseModel):
+    totalInvested: float
+    currentValue: float
+    netPL: float
+    netPLPercentage: float
+    reportDate: str
+
+class AllocationModel(BaseModel):
+    stock: str
+    percentage: float
+    value: float
+
+class PerformerModel(BaseModel):
+    stock: str
+    returnPercentage: float
+
+class InsightsModel(BaseModel):
+    bestPerformer: Optional[PerformerModel] = None
+    underperformer: Optional[PerformerModel] = None
+
+class MarketContextModel(BaseModel):
+    niftyLevel: Optional[float] = None
+    portfolioVsMarket: Optional[str] = None
+    marketTrend: Optional[str] = None
+
+class EducationalContentModel(BaseModel):
+    observations: Optional[List[str]] = []
+    disclaimer: Optional[str] = None
+
+class DebugModel(BaseModel):
+    totalStocksFound: Optional[int] = None
+    profitLossValues: Optional[List[Dict[str, Any]]] = []
+    totalLinesProcessed: Optional[int] = None
+
+class FlexiblePortfolioDataModel(BaseModel):
+    # Core required fields
+    portfolioSummary: PortfolioSummaryModel
+    holdings: List[HoldingModel]
+    
+    # Optional extended fields
+    allocation: Optional[List[AllocationModel]] = []
+    insights: Optional[InsightsModel] = None
+    marketContext: Optional[MarketContextModel] = None
+    
+    # New extended fields
+    reportMetadata: Optional[ReportMetadataModel] = None
+    stockRecords: Optional[List[HoldingModel]] = []
+    educationalContent: Optional[EducationalContentModel] = None
+    debug: Optional[DebugModel] = None
+    
+    # Handle raw data fields
+    data: Optional[List[str]] = []
+    output: Optional[str] = None
+
+class PortfolioResponse(BaseModel):
+    combined_chart: str  # base64 encoded
+    allocation_chart: str
+    performance_chart: str
+    comparison_chart: str
+    text_summary: str
+    telegram_message: str
+    status: str = "success"
+
+# Initialize FastAPI app
+app = FastAPI(
+    title="📊 Portfolio Analysis API - Enhanced",
+    description="Enhanced portfolio analysis with flexible data handling",
+    version="2.0.0",
+    docs_url="/",  # Swagger UI at root
+    redoc_url="/docs"
+)
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Create temp directory for charts
+TEMP_DIR = Path(tempfile.gettempdir()) / "portfolio_charts"
+TEMP_DIR.mkdir(exist_ok=True)
+
+class PortfolioAnalyzer:
+    """Enhanced Portfolio analysis engine"""
+    
+    def __init__(self):
+        self.colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEAA7', '#DDA0DD', '#98D8C8']
+    
+    def preprocess_data(self, raw_data: FlexiblePortfolioDataModel) -> Dict[str, Any]:
+        """Preprocess and normalize input data"""
+        
+        # If we have stringified JSON in data or output fields, try to parse it
+        if raw_data.data and len(raw_data.data) > 0:
+            try:
+                parsed_data = json.loads(raw_data.data[0])
+                if isinstance(parsed_data, dict):
+                    # Merge parsed data with existing data
+                    return self._merge_data_sources(raw_data, parsed_data)
+            except (json.JSONDecodeError, IndexError):
+                pass
+        
+        if raw_data.output:
+            try:
+                parsed_data = json.loads(raw_data.output)
+                if isinstance(parsed_data, dict):
+                    return self._merge_data_sources(raw_data, parsed_data)
+            except json.JSONDecodeError:
+                pass
+        
+        # Use the direct data structure
+        return {
+            'portfolioSummary': raw_data.portfolioSummary,
+            'holdings': raw_data.holdings,
+            'allocation': raw_data.allocation or [],
+            'insights': raw_data.insights,
+            'marketContext': raw_data.marketContext,
+            'reportMetadata': raw_data.reportMetadata
+        }
+    
+    def _merge_data_sources(self, raw_data: FlexiblePortfolioDataModel, parsed_data: Dict) -> Dict[str, Any]:
+        """Merge data from different sources"""
+        result = {
+            'portfolioSummary': raw_data.portfolioSummary,
+            'holdings': raw_data.holdings,
+            'allocation': raw_data.allocation or [],
+            'insights': raw_data.insights,
+            'marketContext': raw_data.marketContext,
+        }
+        
+        # Override with parsed data if available
+        if 'portfolioSummary' in parsed_data:
+            result['portfolioSummary'] = parsed_data['portfolioSummary']
+        if 'holdings' in parsed_data:
+            result['holdings'] = parsed_data['holdings']
+        if 'allocation' in parsed_data:
+            result['allocation'] = parsed_data['allocation']
+        if 'insights' in parsed_data:
+            result['insights'] = parsed_data['insights']
+        if 'marketContext' in parsed_data:
+            result['marketContext'] = parsed_data['marketContext']
+            
+        return result
+    
+    def analyze_portfolio(self, portfolio_data: FlexiblePortfolioDataModel) -> PortfolioResponse:
+        """Main analysis function with flexible input handling"""
+        try:
+            # Preprocess the data
+            processed_data = self.preprocess_data(portfolio_data)
+            
+            # Extract components
+            portfolio_summary = processed_data['portfolioSummary']
+            holdings = processed_data['holdings']
+            allocation = processed_data.get('allocation', [])
+            insights = processed_data.get('insights')
+            market_context = processed_data.get('marketContext')
+            
+            # If allocation is empty, generate it from holdings
+            if not allocation and holdings:
+                allocation = self._generate_allocation_from_holdings(holdings)
+            
+            # Create charts
+            charts = self._create_charts(
+                portfolio_summary,
+                holdings,
+                allocation,
+                market_context or {}
+            )
+            
+            # Create text summaries
+            text_summary = self._create_text_summary(
+                portfolio_summary,
+                holdings,
+                insights or {},
+                market_context or {}
+            )
+            
+            telegram_msg = self._create_telegram_message(
+                portfolio_summary,
+                holdings,
+                insights or {}
+            )
+            
+            return PortfolioResponse(
+                combined_chart=charts['combined'],
+                allocation_chart=charts['allocation'],
+                performance_chart=charts['performance'],
+                comparison_chart=charts['comparison'],
+                text_summary=text_summary,
+                telegram_message=telegram_msg
+            )
+            
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")
+    
+    def _generate_allocation_from_holdings(self, holdings: List) -> List[Dict]:
+        """Generate allocation data from holdings if not provided"""
+        total_value = sum(h.get('currentValue', 0) if isinstance(h, dict) else h.currentValue for h in holdings)
+        
+        allocation = []
+        for holding in holdings:
+            if isinstance(holding, dict):
+                stock_name = holding.get('stockName', '').replace('.NS', '')
+                current_value = holding.get('currentValue', 0)
+            else:
+                stock_name = holding.stockName.replace('.NS', '')
+                current_value = holding.currentValue
+                
+            if total_value > 0:
+                percentage = (current_value / total_value) * 100
+                allocation.append({
+                    'stock': stock_name,
+                    'percentage': percentage,
+                    'value': current_value
+                })
+        
+        return allocation
+    
+    def _create_charts(self, portfolio_summary, holdings, allocation, market_context):
+        """Create multiple chart types for portfolio visualization"""
+        
+        # Convert to dict format if needed
+        if hasattr(portfolio_summary, 'dict'):
+            portfolio_summary = portfolio_summary.dict()
+        if hasattr(market_context, 'dict'):
+            market_context = market_context.dict()
+            
+        # Process holdings
+        processed_holdings = []
+        for h in holdings:
+            if hasattr(h, 'dict'):
+                processed_holdings.append(h.dict())
+            else:
+                processed_holdings.append(h)
+        holdings = processed_holdings
+        
+        # Process allocation
+        processed_allocation = []
+        for a in allocation:
+            if hasattr(a, 'dict'):
+                processed_allocation.append(a.dict())
+            else:
+                processed_allocation.append(a)
+        allocation = processed_allocation
+        
+        # 1. COMBINED DASHBOARD
+        fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 12))
+        fig.suptitle(f'Portfolio Dashboard - {portfolio_summary.get("reportDate", "Unknown Date")}', 
+                     fontsize=16, fontweight='bold')
+        
+        # Portfolio overview text
+        total_value = portfolio_summary.get('currentValue', 0)
+        total_pl = portfolio_summary.get('netPL', 0)
+        pl_pct = portfolio_summary.get('netPLPercentage', 0)
+        total_invested = portfolio_summary.get('totalInvested', 0)
+        
+        overview_text = f'Total Value: ₹{total_value:,.0f} | P&L: ₹{total_pl:,.0f} ({pl_pct:+.2f}%) | Invested: ₹{total_invested:,.0f}'
+        fig.text(0.5, 0.95, overview_text, ha='center', fontsize=12, 
+                bbox=dict(boxstyle="round,pad=0.5", facecolor='lightblue', alpha=0.7))
+        
+        # Allocation Pie Chart
+        if allocation:
+            stocks = [a.get('stock', 'Unknown') for a in allocation]
+            percentages = [a.get('percentage', 0) for a in allocation]
+            # Filter out zero percentages
+            filtered_data = [(s, p) for s, p in zip(stocks, percentages) if p > 0]
+            if filtered_data:
+                stocks, percentages = zip(*filtered_data)
+                ax1.pie(percentages, labels=stocks, autopct='%1.1f%%', 
+                       colors=self.colors[:len(stocks)], startangle=90)
+            ax1.set_title('Portfolio Allocation', fontweight='bold')
+        
+        # P&L Bar Chart
+        stock_names = [h.get('stockName', 'Unknown').replace('.NS', '') for h in holdings]
+        profit_loss = [h.get('profitLoss', 0) for h in holdings]
+        bars = ax2.bar(stock_names, profit_loss, 
+                      color=['green' if pl > 0 else 'red' for pl in profit_loss])
+        ax2.set_title('Profit/Loss by Stock', fontweight='bold')
+        ax2.set_ylabel('P&L (₹)')
+        ax2.tick_params(axis='x', rotation=45)
+        
+        # Performance Percentage Chart
+        perf_percentages = [h.get('profitLossPercentage', 0) for h in holdings]
+        ax3.bar(stock_names, perf_percentages, 
+               color=['green' if p > 0 else 'red' for p in perf_percentages])
+        ax3.set_title('Returns (%)', fontweight='bold')
+        ax3.set_ylabel('Return %')
+        ax3.tick_params(axis='x', rotation=45)
+        
+        # Holdings Value Chart
+        current_values = [h.get('currentValue', 0) for h in holdings]
+        investments = [h.get('investment', 0) for h in holdings]
+        x = np.arange(len(stock_names))
+        width = 0.35
+        ax4.bar(x - width/2, investments, width, label='Invested', color='lightblue')
+        ax4.bar(x + width/2, current_values, width, label='Current', color='darkblue')
+        ax4.set_title('Investment vs Current Value', fontweight='bold')
+        ax4.set_ylabel('Value (₹)')
+        ax4.set_xticks(x)
+        ax4.set_xticklabels(stock_names, rotation=45)
+        ax4.legend()
+        
+        plt.tight_layout()
+        combined_chart = self._save_chart_as_base64(fig)
+        plt.close(fig)
+        
+        # 2. ALLOCATION CHART
+        fig_alloc, ax_alloc = plt.subplots(figsize=(10, 8))
+        if allocation:
+            filtered_data = [(a.get('stock', 'Unknown'), a.get('percentage', 0)) 
+                           for a in allocation if a.get('percentage', 0) > 0]
+            if filtered_data:
+                stocks, percentages = zip(*filtered_data)
+                wedges, texts, autotexts = ax_alloc.pie(
+                    percentages, 
+                    labels=stocks, 
+                    autopct='%1.1f%%',
+                    colors=self.colors[:len(stocks)], 
+                    startangle=90,
+                    explode=[0.05 if p == max(percentages) else 0 for p in percentages]
+                )
+                ax_alloc.set_title(f'Portfolio Allocation - Total: ₹{total_value:,.0f}', 
+                                  fontsize=16, fontweight='bold')
+        allocation_chart = self._save_chart_as_base64(fig_alloc)
+        plt.close(fig_alloc)
+        
+        # 3. PERFORMANCE ANALYSIS
+        fig_perf, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(14, 10))
+        
+        # P&L Bar Chart
+        ax1.bar(stock_names, profit_loss, 
+               color=['#2ECC71' if pl > 0 else '#E74C3C' for pl in profit_loss])
+        ax1.set_title('Profit/Loss (₹)', fontweight='bold')
+        ax1.tick_params(axis='x', rotation=45)
+        
+        # Percentage Returns
+        ax2.bar(stock_names, perf_percentages, 
+               color=['#2ECC71' if p > 0 else '#E74C3C' for p in perf_percentages])
+        ax2.set_title('Returns (%)', fontweight='bold')
+        ax2.tick_params(axis='x', rotation=45)
+        
+        # Investment vs Current Value
+        x = np.arange(len(stock_names))
+        width = 0.35
+        ax3.bar(x - width/2, investments, width, label='Invested', color='#3498DB', alpha=0.7)
+        ax3.bar(x + width/2, current_values, width, label='Current', color='#9B59B6', alpha=0.7)
+        ax3.set_title('Investment vs Current Value', fontweight='bold')
+        ax3.set_xticks(x)
+        ax3.set_xticklabels(stock_names, rotation=45)
+        ax3.legend()
+        
+        # Risk Analysis (52-week position)
+        positions = [h.get('positionInRange', 50) for h in holdings]
+        colors_risk = ['#E74C3C' if p < 20 else '#F39C12' if p < 60 else '#2ECC71' for p in positions]
+        ax4.bar(stock_names, positions, color=colors_risk)
+        ax4.set_title('52-Week Range Position (%)', fontweight='bold')
+        ax4.tick_params(axis='x', rotation=45)
+        ax4.axhline(y=20, color='red', linestyle='--', alpha=0.5)
+        ax4.axhline(y=80, color='green', linestyle='--', alpha=0.5)
+        
+        plt.tight_layout()
+        performance_chart = self._save_chart_as_base64(fig_perf)
+        plt.close(fig_perf)
+        
+        # 4. COMPARISON CHART
+        fig_comp, ax_comp = plt.subplots(figsize=(12, 6))
+        
+        metrics = ['Portfolio Return', 'Best Stock', 'Worst Stock', 'Market (Nifty)']
+        values = [
+            pl_pct,
+            max(perf_percentages) if perf_percentages else 0,
+            min(perf_percentages) if perf_percentages else 0,
+            -0.95
+        ]
+        
+        colors_comp = ['#3498DB', '#2ECC71', '#E74C3C', '#95A5A6']
+        bars_comp = ax_comp.bar(metrics, values, color=colors_comp)
+        
+        ax_comp.set_title('Performance Comparison', fontsize=16, fontweight='bold')
+        ax_comp.set_ylabel('Return (%)')
+        ax_comp.axhline(y=0, color='black', linestyle='-', alpha=0.3)
+        
+        # Add value labels
+        for bar, value in zip(bars_comp, values):
+            height = bar.get_height()
+            ax_comp.text(bar.get_x() + bar.get_width()/2., height,
+                        f'{value:+.2f}%', ha='center', va='bottom' if height > 0 else 'top')
+        
+        plt.xticks(rotation=45)
+        plt.tight_layout()
+        comparison_chart = self._save_chart_as_base64(fig_comp)
+        plt.close(fig_comp)
+        
+        return {
+            'combined': combined_chart,
+            'allocation': allocation_chart, 
+            'performance': performance_chart,
+            'comparison': comparison_chart
+        }
+    
+    def _save_chart_as_base64(self, fig):
+        """Convert matplotlib figure to base64 string"""
+        buf = io.BytesIO()
+        fig.savefig(buf, format='png', dpi=300, bbox_inches='tight')
+        buf.seek(0)
+        image_base64 = base64.b64encode(buf.getvalue()).decode('utf-8')
+        buf.close()
+        return image_base64
+    
+    def _create_text_summary(self, portfolio_summary, holdings, insights, market_context):
+        """Create detailed text summary with flexible data handling"""
+        
+        # Handle different data formats
+        if hasattr(portfolio_summary, 'dict'):
+            portfolio_summary = portfolio_summary.dict()
+        if hasattr(insights, 'dict'):
+            insights = insights.dict()
+        if hasattr(market_context, 'dict'):
+            market_context = market_context.dict()
+            
+        # Process holdings
+        processed_holdings = []
+        for h in holdings:
+            if hasattr(h, 'dict'):
+                processed_holdings.append(h.dict())
+            else:
+                processed_holdings.append(h)
+        holdings = processed_holdings
+        
+        total_stocks = len(holdings)
+        profitable_stocks = len([h for h in holdings if h.get('profitLoss', 0) > 0])
+        losing_stocks = len([h for h in holdings if h.get('profitLoss', 0) < 0])
+        
+        # Find top performers
+        perf_percentages = [h.get('profitLossPercentage', 0) for h in holdings]
+        if perf_percentages:
+            max_idx = perf_percentages.index(max(perf_percentages))
+            min_idx = perf_percentages.index(min(perf_percentages))
+            top_gainer = holdings[max_idx]
+            top_loser = holdings[min_idx]
+        else:
+            top_gainer = {}
+            top_loser = {}
+        
+        stocks_near_low = len([h for h in holdings if h.get('positionInRange', 50) < 20])
+        
+        summary = f"""
+📊 PORTFOLIO ANALYSIS REPORT (Enhanced)
+📅 Date: {portfolio_summary.get('reportDate', 'Unknown')}
+═══════════════════════════════════════
+💰 FINANCIAL OVERVIEW
+━━━━━━━━━━━━━━━━━━━━━━
+• Total Investment: ₹{portfolio_summary.get('totalInvested', 0):,.2f}
+• Current Value: ₹{portfolio_summary.get('currentValue', 0):,.2f}
+• Net P&L: ₹{portfolio_summary.get('netPL', 0):,.2f} ({portfolio_summary.get('netPLPercentage', 0):+.2f}%)
+• Portfolio Status: {'🟢 Profitable' if portfolio_summary.get('netPL', 0) > 0 else '🔴 Loss' if portfolio_summary.get('netPL', 0) < -1000 else '🟡 Neutral'}
+
+📈 STOCK PERFORMANCE
+━━━━━━━━━━━━━━━━━━━━━━
+• Total Stocks: {total_stocks}
+• Profitable: {profitable_stocks} | Losing: {losing_stocks}
+• Best Performer: {top_gainer.get('stockName', 'N/A').replace('.NS', '')} ({top_gainer.get('profitLossPercentage', 0):+.2f}%)
+• Worst Performer: {top_loser.get('stockName', 'N/A').replace('.NS', '')} ({top_loser.get('profitLossPercentage', 0):.2f}%)
+
+📊 INDIVIDUAL HOLDINGS
+━━━━━━━━━━━━━━━━━━━━━━"""
+
+        for holding in holdings:
+            status_emoji = "🟢" if holding.get('profitLoss', 0) > 0 else "🔴" if holding.get('profitLoss', 0) < 0 else "🟡"
+            summary += f"""
+{status_emoji} {holding.get('stockName', 'Unknown').replace('.NS', '')}:
+   • Shares: {holding.get('shares', 0)} | Price: ₹{holding.get('currentPrice', 0):.2f}
+   • Investment: ₹{holding.get('investment', 0):,.0f} → Current: ₹{holding.get('currentValue', 0):,.0f}
+   • P&L: ₹{holding.get('profitLoss', 0):,.0f} ({holding.get('profitLossPercentage', 0):+.2f}%)
+   • Sector: {holding.get('sector', 'N/A')}"""
+
+        summary += f"""
+
+⚠️ RISK ANALYSIS
+━━━━━━━━━━━━━━━━━━━━━━
+• Stocks near 52W low: {stocks_near_low}
+• Risk Level: {'🔴 HIGH' if stocks_near_low > 2 else '🟡 MEDIUM' if stocks_near_low > 0 else '🟢 LOW'}
+
+📊 MARKET CONTEXT
+━━━━━━━━━━━━━━━━━━━━━━
+• Nifty 50: {market_context.get('niftyLevel', 'N/A')}
+• Portfolio vs Market: {market_context.get('portfolioVsMarket', 'N/A')}
+• Market Trend: {market_context.get('marketTrend', 'N/A')}
+
+💡 INSIGHTS & RECOMMENDATIONS
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"""
+
+        if portfolio_summary.get('netPLPercentage', 0) > 5:
+            summary += "\n• 🎯 Strong portfolio performance - consider partial profit booking"
+        elif portfolio_summary.get('netPLPercentage', 0) < -5:
+            summary += "\n• ⚠️ Portfolio underperforming - review individual positions"
+        
+        if stocks_near_low > 1:
+            summary += f"\n• 📉 {stocks_near_low} stocks near 52W lows - monitor closely"
+        
+        if profitable_stocks > losing_stocks:
+            summary += "\n• ✅ More winners than losers - good stock selection"
+        
+        summary += f"""
+
+🔸 Report generated at: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+🔸 Enhanced API v2.0 - Flexible data processing
+🔸 Disclaimer: This is for educational purposes only, not financial advice.
+"""
+        
+        return summary
+    
+    def _create_telegram_message(self, portfolio_summary, holdings, insights):
+        """Create Telegram-optimized message with flexible data handling"""
+        
+        # Handle different data formats
+        if hasattr(portfolio_summary, 'dict'):
+            portfolio_summary = portfolio_summary.dict()
+        if hasattr(insights, 'dict'):
+            insights = insights.dict()
+            
+        # Process holdings
+        processed_holdings = []
+        for h in holdings:
+            if hasattr(h, 'dict'):
+                processed_holdings.append(h.dict())
+            else:
+                processed_holdings.append(h)
+        holdings = processed_holdings
+        
+        # Find top performers
+        perf_percentages = [h.get('profitLossPercentage', 0) for h in holdings]
+        if perf_percentages:
+            max_idx = perf_percentages.index(max(perf_percentages))
+            min_idx = perf_percentages.index(min(perf_percentages))
+            top_gainer = holdings[max_idx]
+            top_loser = holdings[min_idx]
+        else:
+            top_gainer = {}
+            top_loser = {}
+        
+        status_emoji = "📈" if portfolio_summary.get('netPL', 0) > 0 else "📉" if portfolio_summary.get('netPL', 0) < -1000 else "➡️"
+        
+        telegram_msg = f"""🏦 PORTFOLIO UPDATE (Enhanced)
+📅 {portfolio_summary.get('reportDate', 'Unknown')}
+
+{status_emoji} PERFORMANCE
+💰 Value: ₹{portfolio_summary.get('currentValue', 0):,.0f}
+📊 P&L: {'+' if portfolio_summary.get('netPL', 0) > 0 else ''}₹{portfolio_summary.get('netPL', 0):,.0f} ({portfolio_summary.get('netPLPercentage', 0):+.2f}%)
+
+🏆 TOP MOVES
+📈 Best: {top_gainer.get('stockName', 'N/A').replace('.NS', '')} {top_gainer.get('profitLossPercentage', 0):+.1f}%
+📉 Worst: {top_loser.get('stockName', 'N/A').replace('.NS', '')} {top_loser.get('profitLossPercentage', 0):.1f}%
+
+📊 HOLDINGS"""
+
+        for holding in holdings[:5]:  # Top 5 holdings
+            emoji = "🟢" if holding.get('profitLoss', 0) > 0 else "🔴"
+            telegram_msg += f"\n{emoji} {holding.get('stockName', 'Unknown').replace('.NS', '')}: {holding.get('profitLossPercentage', 0):+.1f}%"
+        
+        profitable_count = len([h for h in holdings if h.get('profitLoss', 0) > 0])
+        telegram_msg += f"\n\n💡 Status: {profitable_count}/{len(holdings)} profitable"
+        telegram_msg += f"\n🚀 Enhanced API v2.0"
+        
+        return telegram_msg
+
+# Initialize analyzer
+analyzer = PortfolioAnalyzer()
+
+@app.get("/", response_class=JSONResponse)
+async def root():
+    """API Health Check"""
+    return {
+        "message": "Portfolio Analysis API v2.0 is running!",
+        "status": "healthy",
+        "version": "2.0.0",
+        "features": ["Flexible data handling", "Extended field support", "Enhanced analysis"],
+        "endpoints": {
+            "/analyze": "POST - Analyze portfolio data (flexible format)",
+            "/analyze-file": "POST - Upload JSON file for analysis",
+            "/sample": "GET - Get sample portfolio data",
+            "/docs": "GET - API documentation"
+        }
+    }
+
+@app.post("/analyze", response_model=PortfolioResponse)
+async def analyze_portfolio(portfolio_data: FlexiblePortfolioDataModel):
+    """
+    Analyze portfolio data with flexible input handling
+    
+    Supports multiple data formats including:
+    - Standard portfolio structure
+    - Extended data with metadata, debug info, etc.
+    - Stringified JSON in data/output fields
+    """
+    try:
+        result = analyzer.analyze_portfolio(portfolio_data)
+        return result
+    except ValidationError as e:
+        raise HTTPException(status_code=422, detail=f"Invalid data format: {e}")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")
+
+@app.post("/analyze-file")
+async def analyze_portfolio_file(file: UploadFile = File(...)):
+    """Upload JSON file and analyze portfolio"""
+    try:
+        if not file.filename.endswith('.json'):
+            raise HTTPException(status_code=400, detail="Please upload a JSON file")
+        
+        content = await file.read()
+        portfolio_data = json.loads(content.decode('utf-8'))
+        validated_data = FlexiblePortfolioDataModel(**portfolio_data)
+        result = analyzer.analyze_portfolio(validated_data)
+        return result
+        
+    except json.JSONDecodeError:
+        raise HTTPException(status_code=400, detail="Invalid JSON format")
+    except ValidationError as e:
+        raise HTTPException(status_code=422, detail=f"Invalid data structure: {e}")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")
+
+@app.get("/sample")
+async def get_sample_data():
+    """Get sample portfolio data"""
+    sample_data = {
+        "portfolioSummary": {
+            "totalInvested": 213600,
+            "currentValue": 214327.5,
+            "netPL": 727.5,
+            "netPLPercentage": 0.34,
+            "reportDate": "September 27, 2025"
+        },
+        "holdings": [
+            {
+                "stockName": "RELIANCE.NS",
+                "shares": 20,
+                "purchasePrice": 1400,
+                "currentPrice": 1375,
+                "investment": 28000,
+                "currentValue": 27500,
+                "profitLoss": -500,
+                "profitLossPercentage": -1.79,
+                "fiftyTwoWeekLow": 1114.85,
+                "fiftyTwoWeekHigh": 1551,
+                "positionInRange": 59.5,
+                "sector": "Energy"
+            },
+            {
+                "stockName": "HDFCBANK.NS",
+                "shares": 22,
+                "purchasePrice": 800,
+                "currentPrice": 950,
+                "investment": 17600,
+                "currentValue": 20900,
+                "profitLoss": 3300,
+                "profitLossPercentage": 18.75,
+                "fiftyTwoWeekLow": 806.5,
+                "fiftyTwoWeekHigh": 1018.85,
+                "positionInRange": 67.6,
+                "sector": "Banking"
+            }
+        ],
+        "allocation": [
+            {"stock": "HDFC", "percentage": 54.4, "value": 20900},
+            {"stock": "RIL", "percentage": 45.6, "value": 27500}
+        ]
+    }
+    return sample_data
+
+@app.get("/health")
+async def health_check():
+    """Detailed health check"""
+    return {
+        "status": "healthy",
+        "timestamp": datetime.now().isoformat(),
+        "version": "2.0.0",
+        "features": {
+            "flexible_data_handling": "enabled",
+            "extended_field_support": "enabled",
+            "chart_generation": "enabled",
+            "enhanced_analysis": "enabled"
+        },
+        "dependencies": {
+            "matplotlib": "available",
+            "pandas": "available",
+            "numpy": "available",
+            "pydantic": "available"
+        }
+    }
+
+if __name__ == "__main__":
+    uvicorn.run(
+        "main:app",
+        host="0.0.0.0",
+        port=int(os.getenv("PORT", 7860)),
+        reload=False
     )