backend/stock_analyzer.py

import yfinance as yf
import pandas as pd
import plotly.graph_objects as go
import plotly.io as pio
from datetime import datetime, timedelta
import json
import os
import modal
import logging
import requests
from typing import Dict, Any

# DEBUG: Print modal module path and version
print(f"DEBUG: modal module loaded from: {modal.__file__}")
print(f"DEBUG: modal version: {modal.__version__}")

from .data_collector import StockDataCollector, YFinanceRateLimitError
from .moat_analyzer import MoatAnalyzer
from .config import AppConfig

class StockAnalyzer:
    """Handles stock analysis and visualization."""
    
    def __init__(self):
        self.config = AppConfig()
        self.data_collector = StockDataCollector()
        self.moat_analyzer = MoatAnalyzer()
        self._setup_logging()
    
    # Helper function to format numbers or return 'N/A'
    def _format_num(self, value):
        """Helper to format numbers, handling N/A gracefully."""
        if isinstance(value, (int, float)):
            return f"{value:,.2f}"
        return "N/A"
    
    def _setup_logging(self):
        """Setup logging configuration."""
        logging.basicConfig(
            level=logging.INFO,
            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        )
        self.logger = logging.getLogger(__name__)
    
    def analyze(self, ticker):
        """Analyze a stock and return the analysis results."""
        try:
            stock = yf.Ticker(ticker)
            info = stock.info
            if not info or not info.get('regularMarketPrice'): # Check for essential info
                return {"analysis": f"Could not retrieve comprehensive stock information for {ticker}. Please check the ticker symbol and try again.", "revenue_chart": None, "fcf_chart": None, "shares_chart": None}

            # Initialize variables to None or default empty values
            latest_revenue = 'N/A'
            latest_fcf = 'N/A'
            latest_shares = 'N/A'
            latest_bs_data = {}
            latest_is_data = {}
            moat_summary_text = 'N/A'
            moat_rating = 'N/A'
            llm_insights = "AI insights temporarily unavailable."
            serper_results_str = ""
            revenue_chart_json = None
            fcf_chart_json = None
            shares_chart_json = None

            # Collect data for charts and LLM
            revenue_data = self.data_collector.get_revenue_history(ticker)
            fcf_data = self.data_collector.get_fcf_history(ticker)
            shares_data = self.data_collector.get_shares_history(ticker)

            # Generate charts
            revenue_chart = self._create_revenue_chart(revenue_data)
            fcf_chart = self._create_fcf_chart(fcf_data)
            shares_chart = self._create_shares_chart(shares_data)
            
            # Convert Plotly figures to JSON strings
            revenue_chart_json = pio.to_json(revenue_chart) if revenue_chart else None
            fcf_chart_json = pio.to_json(fcf_chart) if fcf_chart else None
            shares_chart_json = pio.to_json(shares_chart) if shares_chart else None

            # Safely get latest values with proper error handling
            if revenue_data is not None and not revenue_data.empty:
                latest_revenue = revenue_data.iloc[-1]['Revenue']
            if fcf_data is not None and not fcf_data.empty:
                latest_fcf = fcf_data.iloc[-1]['FCF']
            if shares_data is not None and not shares_data.empty:
                latest_shares = shares_data.iloc[-1]['Shares']

            # Attempt to get full balance sheet and income statement data
            balance_sheet = stock.balance_sheet
            income_statement = stock.financials

            # Extract relevant recent quarterly/annual data (handle potential empty dataframes)
            if balance_sheet is not None and not balance_sheet.empty:
                latest_bs_data = balance_sheet.iloc[:, 0].to_dict()
            if income_statement is not None and not income_statement.empty:
                latest_is_data = income_statement.iloc[:, 0].to_dict()

            # Get moat analysis
            moat_analyzer = MoatAnalyzer()
            moat_analysis_results = moat_analyzer.analyze_moat(ticker)
            moat_summary_text = moat_analysis_results["summary"]
            moat_rating = moat_analysis_results["rating"]
            
            try:
                # Look up the deployed Modal LLM app and get the remote function here
                generate_llm_response_modal = modal.Function.from_name("buffetbot-llm", "generate_llm_response")

                # Perform Serper search for moat articles
                data_collector = StockDataCollector()
                moat_search_results = data_collector._perform_serper_search(f"{ticker} economic moat analysis articles")
                
                # Format Serper search results
                serper_results_str = ""
                if moat_search_results:
                    serper_results_str = "\n\n#### External Moat Analysis (Web Search)\n" + "\n".join([
                        f"- **{r['title']}** (Source: {r['link']})\n  Snippet: {r['snippet']}"
                        for r in moat_search_results
                    ])
                
                # Get additional comprehensive data for LLM prompt
                revenue_data = data_collector.get_revenue_history(ticker)
                fcf_data = data_collector.get_fcf_history(ticker)
                shares_data = data_collector.get_shares_history(ticker)

                # Generate charts
                revenue_chart = self._create_revenue_chart(revenue_data)
                fcf_chart = self._create_fcf_chart(fcf_data)
                shares_chart = self._create_shares_chart(shares_data)
                
                # Convert Plotly figures to JSON strings
                revenue_chart_json = pio.to_json(revenue_chart) if revenue_chart else None
                fcf_chart_json = pio.to_json(fcf_chart) if fcf_chart else None
                shares_chart_json = pio.to_json(shares_chart) if shares_chart else None

                # Safely get latest values with proper error handling
                if revenue_data is not None and not revenue_data.empty:
                    latest_revenue = revenue_data.iloc[-1]['Revenue']
                if fcf_data is not None and not fcf_data.empty:
                    latest_fcf = fcf_data.iloc[-1]['FCF']
                if shares_data is not None and not shares_data.empty:
                    latest_shares = shares_data.iloc[-1]['Shares']

                # Attempt to get full balance sheet and income statement data
                balance_sheet = stock.balance_sheet
                income_statement = stock.financials

                # Extract relevant recent quarterly/annual data (handle potential empty dataframes)
                if balance_sheet is not None and not balance_sheet.empty:
                    latest_bs_data = balance_sheet.iloc[:, 0].to_dict()
                if income_statement is not None and not income_statement.empty:
                    latest_is_data = income_statement.iloc[:, 0].to_dict()

                # Format detailed financial data for the LLM prompt
                financial_data_str = f"""
#### Recent Financials (Latest Available)
- Latest Revenue: {self._format_num(latest_revenue)}
- Latest Free Cash Flow: {self._format_num(latest_fcf)}
- Latest Shares Outstanding: {self._format_num(latest_shares)}

#### Key Balance Sheet Items
- Total Assets: {self._format_num(latest_bs_data.get('Total Assets', 'N/A'))}
- Total Liabilities: {self._format_num(latest_bs_data.get('Total Liabilities', 'N/A'))}
- Total Debt: {self._format_num(info.get('totalDebt', 'N/A'))}
- Total Cash: {self._format_num(info.get('totalCash', 'N/A'))}
- Total Equity: {self._format_num(latest_bs_data.get('Total Equity', 'N/A'))}
- Debt to Equity: {self._format_num(info.get('debtToEquity', 'N/A'))}

#### Key Income Statement Items
- Gross Profits: {self._format_num(latest_is_data.get('Gross Profit', 'N/A'))}
- Operating Income: {self._format_num(latest_is_data.get('Operating Income', 'N/A'))}
- Net Income: {self._format_num(latest_is_data.get('Net Income', 'N/A'))}
- Earnings Growth (YoY): {self._format_num(info.get('earningsQuarterlyGrowth', 'N/A'))}%
"""

                # Ensure the LLM knows to use BuffetBot's persona and clearly mark AI insights
                system_message = """
As an AI-powered stock analyst named BuffetBot, your goal is to provide concise, clear, and actionable investment insights. When giving recommendations, state "BuffetBot recommends" instead of "I recommend". Clearly mark your analysis as "AI Insights:" at the beginning of your comprehensive insight. Focus on explaining key numbers, financial health, growth metrics, economic moat significance, and overall investment potential. Do not include "What to look for" sections for charts or specific metrics, as these will be provided separately. Avoid garbled text or incomplete sentences.
"""

                llm_prompt = self._generate_llm_prompt(ticker, {
                    'info': info,
                    'moat_summary': moat_summary_text,
                    'serper_results': serper_results_str,
                    'financial_data_str': financial_data_str
                })

                llm_insights = generate_llm_response_modal.remote(
                    system_message=system_message,
                    prompt=llm_prompt
                )
            except Exception as llm_e:
                self.logger.warning(f"Could not get LLM insights for {ticker}: {llm_e}")
                llm_insights = "AI insights temporarily unavailable. Please ensure Modal LLM service is deployed and accessible and Modal API keys are set as Hugging Face Space secrets."

            # Generate analysis text
            analysis = self._generate_analysis(ticker, info, moat_summary_text, moat_rating, llm_insights, latest_bs_data, latest_is_data, latest_revenue, latest_fcf, latest_shares)
            
            return {
                "analysis": analysis,
                "revenue_chart": revenue_chart_json,
                "fcf_chart": fcf_chart_json,
                "shares_chart": shares_chart_json
            }
            
        except YFinanceRateLimitError as e:
            self.logger.error(f"Yfinance rate limit hit for {ticker}: {e}")
            return {"analysis": f"Error analyzing stock: {e}. Charts may be unavailable.", "revenue_chart": None, "fcf_chart": None, "shares_chart": None}
        except requests.exceptions.HTTPError as e:
            if e.response.status_code == 429:
                # This case should now be handled by YFinanceRateLimitError
                self.logger.error(f"Unexpected 429 HTTP Error for {ticker}: {e}")
                return {"analysis": f"Error analyzing stock: You have been rate-limited by Yahoo Finance. Please wait a few minutes and try again. Charts may be unavailable.", "revenue_chart": None, "fcf_chart": None, "shares_chart": None}
            else:
                self.logger.error(f"HTTP error fetching data for {ticker}: {e}")
                return {"analysis": f"Error analyzing stock: An HTTP error occurred: {e}. Charts may be unavailable.", "revenue_chart": None, "fcf_chart": None, "shares_chart": None}
        except Exception as e:
            self.logger.error(f"Error analyzing stock {ticker}: {e}", exc_info=True) # Log full traceback
            return {"analysis": f"An unexpected error occurred while analyzing {ticker}: {str(e)}. Charts may be unavailable.", "revenue_chart": None, "fcf_chart": None, "shares_chart": None}
    
    def _create_revenue_chart(self, revenue_data):
        """Create revenue growth chart."""
        if revenue_data is None or revenue_data.empty:
            return None
            
        fig = go.Figure()
        fig.add_trace(go.Scatter(
            x=revenue_data.index,
            y=revenue_data['Revenue'],
            mode='lines+markers',
            name='Revenue'
        ))
        
        fig.update_layout(
            title='Revenue Growth',
            xaxis_title='Year',
            yaxis_title='Revenue (USD)',
            template='plotly_white'
        )
        
        return fig
    
    def _create_fcf_chart(self, fcf_data):
        """Create free cash flow chart."""
        if fcf_data is None or fcf_data.empty:
            return None
            
        fig = go.Figure()
        fig.add_trace(go.Scatter(
            x=fcf_data.index,
            y=fcf_data['FCF'],
            mode='lines+markers',
            name='Free Cash Flow'
        ))
        
        fig.update_layout(
            title='Free Cash Flow',
            xaxis_title='Year',
            yaxis_title='Free Cash Flow (USD)',
            template='plotly_white'
        )
        
        return fig
    
    def _create_shares_chart(self, shares_data):
        """Create shares outstanding chart."""
        if shares_data is None or shares_data.empty:
            return None
            
        fig = go.Figure()
        fig.add_trace(go.Scatter(
            x=shares_data.index,
            y=shares_data['Shares'],
            mode='lines+markers',
            name='Shares Outstanding'
        ))
        
        fig.update_layout(
            title='Shares Outstanding',
            xaxis_title='Year',
            yaxis_title='Shares (millions)',
            template='plotly_white'
        )
        
        return fig
    
    def _generate_analysis(self, ticker, info, moat_summary, moat_rating, llm_insights, latest_bs_data, latest_is_data, latest_revenue, latest_fcf, latest_shares):
        """Generate analysis text."""
        data_as_of_timestamp = info.get('regularMarketTime')
        data_as_of_date = datetime.fromtimestamp(data_as_of_timestamp).strftime('%Y-%m-%d %H:%M:%S') if data_as_of_timestamp else 'N/A'

        # Initialize serper_results_str
        serper_results_str = ""

        analysis = f"""
# {info.get('longName', ticker)} ({ticker}) Analysis

*Data as of: {data_as_of_date}*

## Company Overview
- Sector: {info.get('sector', 'N/A')}
- Industry: {info.get('industry', 'N/A')}
- Full-time Employees: {self._format_num(info.get('fullTimeEmployees', 'N/A'))}

## Current Status
- Current Price: {self._format_num(info.get('currentPrice', 'N/A'))}
- 52 Week High: {self._format_num(info.get('fiftyTwoWeekHigh', 'N/A'))}
- 52 Week Low: {self._format_num(info.get('fiftyTwoWeekLow', 'N/A'))}
- Market Cap: {self._format_num(info.get('marketCap', 'N/A'))}

## Key Valuation Metrics
- P/E Ratio: {self._format_num(info.get('trailingPE', 'N/A'))}
- Forward P/E: {self._format_num(info.get('forwardPE', 'N/A'))}
- PEG Ratio: {self._format_num(info.get('pegRatio', 'N/A'))}
- Dividend Yield: {self._format_num(info.get('dividendYield', 'N/A'))}%

## Growth and Profitability
- Revenue Growth (YoY): {self._format_num(info.get('revenueGrowth', 'N/A'))}%
- Profit Margins: {self._format_num(info.get('profitMargins', 'N/A'))}%
- Operating Margins: {self._format_num(info.get('operatingMargins', 'N/A'))}%

## Financial Health Indicators
- Current Ratio: {self._format_num(info.get('currentRatio', 'N/A'))}
- Debt to Equity: {self._format_num(info.get('debtToEquity', 'N/A'))}
- Return on Equity: {self._format_num(info.get('returnOnEquity', 'N/A'))}%
- Debt to Asset Ratio: {self._format_num(info.get('totalDebt', 0) / info.get('totalAssets', 1) if info.get('totalAssets') else 'N/A')}

#### Recent Financials (Latest Available)
- Latest Revenue: {self._format_num(latest_revenue)}
- Latest Free Cash Flow: {self._format_num(latest_fcf)}
- Latest Shares Outstanding: {self._format_num(latest_shares)}

#### Key Balance Sheet Items
- Total Assets: {self._format_num(latest_bs_data.get('Total Assets', 'N/A'))}
- Total Liabilities: {self._format_num(latest_bs_data.get('Total Liabilities', 'N/A'))}
- Total Debt: {self._format_num(info.get('totalDebt', 'N/A'))}
- Total Cash: {self._format_num(info.get('totalCash', 'N/A'))}
- Total Equity: {self._format_num(latest_bs_data.get('Total Equity', 'N/A'))}
- Debt to Equity: {self._format_num(info.get('debtToEquity', 'N/A'))}

#### Key Income Statement Items
- Gross Profits: {self._format_num(latest_is_data.get('Gross Profit', 'N/A'))}
- Operating Income: {self._format_num(latest_is_data.get('Operating Income', 'N/A'))}
- Net Income: {self._format_num(latest_is_data.get('Net Income', 'N/A'))}
- Earnings Growth (YoY): {self._format_num(info.get('earningsQuarterlyGrowth', 'N/A'))}%

## Economic Moat Analysis
{moat_summary}
{serper_results_str}

## AI Insights
{llm_insights}
"""
        
        return analysis 

    def _generate_llm_prompt(self, ticker: str, data: Dict[str, Any]) -> str:
        """Generate a focused prompt for the LLM."""
        info = data.get('info', {})
        moat_summary_text = data.get('moat_summary', 'N/A')
        serper_results_str = data.get('serper_results', 'N/A')
        financial_data_str = data.get('financial_data_str', 'N/A')

        llm_prompt = f"""
Analyze {ticker} ({info.get('longName', ticker)}) as an investment opportunity.

Key Metrics:
- Price: ${self._format_num(info.get('currentPrice', 'N/A'))}
- P/E: {self._format_num(info.get('trailingPE', 'N/A'))}
- Revenue Growth: {self._format_num(info.get('revenueGrowth', 'N/A'))}%
- Profit Margin: {self._format_num(info.get('profitMargins', 'N/A'))}%
- Debt/Equity: {self._format_num(info.get('debtToEquity', 'N/A'))}
- ROE: {self._format_num(info.get('returnOnEquity', 'N/A'))}%

Moat Analysis:
{moat_summary_text}
{serper_results_str}

Provide a concise analysis focusing on:
1. Key strengths and risks
2. Economic moat significance
3. Clear investment recommendation

Keep your response under 200 words.
"""
        return llm_prompt