import requests
from bs4 import BeautifulSoup
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
from collections import Counter
import time
import numpy as np
import yfinance as yf
import pandas as pd
from datetime import datetime, timedelta
import json
from typing import Dict, List, Tuple
import re  # Add this import
import warnings
warnings.filterwarnings('ignore')

# Load FinBERT
model_name = "yiyanghkust/finbert-tone"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

class StockSentimentAnalyzer:
    def __init__(self):
        self.session = requests.Session()
        self.session.headers.update({
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
        })
        
        # API setup for Indian stock data
        self.api_url = "https://indian-stock-exchange-api2.p.rapidapi.com/stock"
        self.api_headers = {
            "x-rapidapi-host": "indian-stock-exchange-api2.p.rapidapi.com",
            "x-rapidapi-key": "a12f59fc40msh153da8fdf3885b6p100406jsn57d1d84b0d06"
        }
        self.symbol = None

    def get_stock_data(self, symbol: str, period: str = "1mo") -> pd.DataFrame:
        """Fetch stock data from Yahoo Finance"""
        try:
            # Add .NS for NSE stocks if not present
            if not symbol.endswith('.NS') and not symbol.endswith('.BO'):
                symbol += '.NS'

            stock = yf.Ticker(symbol)
            data = stock.history(period=period)
            return data
        except Exception as e:
            print(f"Error fetching stock data for {symbol}: {e}")
            return pd.DataFrame()

    def get_news_from_api(self, company_name: str) -> List[Dict]:
        """Get news articles from the API"""
        querystring = {"name": company_name}
        try:
            response = requests.get(self.api_url, headers=self.api_headers, params=querystring)
            data = response.json()
            news_data = data.get("recentNews", {})
            return news_data
        except Exception as e:
            print(f"Error fetching news from API: {e}")
            return []

    def scrape_news_sentiment(self, company_name: str, symbol: str) -> Dict:
        """Scrape news sentiment from multiple sources"""
        news_data = {
            'headlines': [],
            'sources': [],
            'sentiment_scores': [],
            'dates': [],
            'urls': []
        }

        # Get news from API
        api_news = self.get_news_from_api(company_name)
        urls = [item["url"] for item in api_news if isinstance(item, dict) and "url" in item]
        
        print(f"Found {len(urls)} news articles from API")
        
        # Process each URL
        for i, news_url in enumerate(urls):
            try:
                print(f"\n[{i+1}/{len(urls)}] Analyzing: {news_url[:60]}...")
                html = requests.get(news_url, timeout=10).text
                soup = BeautifulSoup(html, "html.parser")

                # Get title
                title = soup.title.string if soup.title else "No title"
                
                # Grab <p> tags and filter
                paragraphs = soup.find_all("p")
                if not paragraphs:
                    print("→ No content found")
                    continue

                content = " ".join(p.get_text() for p in paragraphs if len(p.get_text()) > 40)
                content = content.strip()
                if len(content) < 100:
                    print("→ Content too short")
                    continue

                # Truncate to 512 tokens max
                content = content[:1000]
                result = classifier(content[:512])[0]
                label = result['label'].lower()
                score = result['score']
                
                # Convert FinBERT sentiment to polarity score (-1 to 1)
                polarity = 0
                if label == "positive":
                    polarity = score
                elif label == "negative":
                    polarity = -score
                
                news_data['headlines'].append(title)
                news_data['sources'].append('API')
                news_data['sentiment_scores'].append(polarity)
                news_data['dates'].append(datetime.now())
                news_data['urls'].append(news_url)

                print(f"→ Sentiment: {label.upper()} (confidence: {score:.1%})")
                time.sleep(1.2)  # polite delay

            except Exception as e:
                print(f"❌ Error: {str(e)}")
                continue

        # Economic Times
        try:
            et_url = f"https://economictimes.indiatimes.com/topic/{company_name.replace(' ', '-')}"
            response = self.session.get(et_url, timeout=10)
            soup = BeautifulSoup(response.content, 'html.parser')

            headlines = soup.find_all(['h2', 'h3', 'h4'], class_=re.compile('.*title.*|.*headline.*'))
            for headline in headlines[:5]:  # Limit to 5 headlines
                text = headline.get_text().strip()
                if text and len(text) > 10:
                    # Use FinBERT for sentiment analysis
                    result = classifier(text)[0]
                    label = result['label'].lower()
                    score = result['score']
                    
                    # Convert to polarity
                    polarity = 0
                    if label == "positive":
                        polarity = score
                    elif label == "negative":
                        polarity = -score
                    
                    news_data['headlines'].append(text)
                    news_data['sources'].append('Economic Times')
                    news_data['sentiment_scores'].append(polarity)
                    news_data['dates'].append(datetime.now())
                    news_data['urls'].append(et_url)
        except Exception as e:
            print(f"Error scraping Economic Times: {e}")

        return news_data

    def calculate_news_sentiment_score(self, news_data: Dict) -> Dict:
        """Calculate various sentiment scores from news data"""
        if not news_data['sentiment_scores']:
            return {
                'positive_score': 50,
                'negative_score': 50,
                'fear_score': 50,
                'confidence_score': 50,
                'overall_sentiment_score': 50
            }

        sentiments = news_data['sentiment_scores']
        headlines = news_data['headlines']

        # Count sentiments
        positive_count = sum(1 for s in sentiments if s > 0.1)
        negative_count = sum(1 for s in sentiments if s < -0.1)
        neutral_count = len(sentiments) - positive_count - negative_count

        total = len(sentiments)
        positive_score = (positive_count / total) * 100 if total > 0 else 50
        negative_score = (negative_count / total) * 100 if total > 0 else 50

        # Calculate average confidence
        confidence_values = [abs(s) for s in sentiments]
        avg_confidence = sum(confidence_values) / len(confidence_values) if confidence_values else 0
        confidence_score = avg_confidence * 100

        # Fear score based on keywords
        fear_keywords = ['fall', 'drop', 'crash', 'loss', 'decline', 'bear', 'sell', 'down', 'negative', 'risk']
        confidence_keywords = ['rise', 'gain', 'bull', 'buy', 'up', 'positive', 'growth', 'profit', 'strong']

        fear_mentions = sum(1 for headline in headlines
                           for keyword in fear_keywords
                           if keyword.lower() in headline.lower())

        confidence_mentions = sum(1 for headline in headlines
                                for keyword in confidence_keywords
                                if keyword.lower() in headline.lower())

        fear_score = min(100, (fear_mentions / len(headlines)) * 200) if headlines else 50
        confidence_boost = min(100, (confidence_mentions / len(headlines)) * 200) if headlines else 50

        # Overall sentiment score
        overall_sentiment = 50 + ((positive_score - negative_score) * 0.3) + ((confidence_boost - fear_score) * 0.2)
        
        return {
            'positive_score': round(positive_score, 2),
            'negative_score': round(negative_score, 2),
            'fear_score': round(fear_score, 2),
            'confidence_score': round(confidence_score, 2),
            'overall_sentiment_score': round(min(100, max(0, overall_sentiment)), 2)
        }

    def calculate_volatility_score(self, stock_data: pd.DataFrame) -> float:
        """Calculate innovative volatility score (0-100)"""
        if stock_data.empty:
            return 0

        # Calculate different volatility measures
        returns = stock_data['Close'].pct_change().dropna()

        # Standard deviation of returns (annualized)
        std_vol = returns.std() * np.sqrt(252) * 100

        # Average True Range volatility
        high_low = stock_data['High'] - stock_data['Low']
        high_close = np.abs(stock_data['High'] - stock_data['Close'].shift())
        low_close = np.abs(stock_data['Low'] - stock_data['Close'].shift())
        true_range = np.maximum(high_low, np.maximum(high_close, low_close))
        atr = true_range.rolling(14).mean().iloc[-1]
        atr_vol = (atr / stock_data['Close'].iloc[-1]) * 100

        # Price range volatility
        price_range = ((stock_data['High'].max() - stock_data['Low'].min()) / stock_data['Close'].iloc[-1]) * 100

        # Combine and normalize to 0-100 scale
        volatility_score = min(100, (std_vol * 0.4 + atr_vol * 0.4 + price_range * 0.2))
        return round(volatility_score, 2)

    def calculate_momentum_score(self, stock_data: pd.DataFrame) -> float:
        """Calculate momentum score based on price trends (0-100)"""
        if stock_data.empty:
            return 50

        # RSI calculation
        delta = stock_data['Close'].diff()
        gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
        loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
        rs = gain / loss
        rsi = 100 - (100 / (1 + rs))
        current_rsi = rsi.iloc[-1] if not np.isnan(rsi.iloc[-1]) else 50

        # Price momentum (% change over different periods)
        mom_1d = ((stock_data['Close'].iloc[-1] - stock_data['Close'].iloc[-2]) / stock_data['Close'].iloc[-2]) * 100
        mom_5d = ((stock_data['Close'].iloc[-1] - stock_data['Close'].iloc[-6]) / stock_data['Close'].iloc[-6]) * 100 if len(stock_data) > 5 else 0
        mom_20d = ((stock_data['Close'].iloc[-1] - stock_data['Close'].iloc[-21]) / stock_data['Close'].iloc[-21]) * 100 if len(stock_data) > 20 else 0

        # Moving average trends
        ma_5 = stock_data['Close'].rolling(5).mean().iloc[-1]
        ma_20 = stock_data['Close'].rolling(20).mean().iloc[-1] if len(stock_data) > 20 else ma_5
        current_price = stock_data['Close'].iloc[-1]

        ma_score = 50
        if current_price > ma_5 > ma_20:
            ma_score = 75
        elif current_price > ma_5:
            ma_score = 65
        elif current_price < ma_5 < ma_20:
            ma_score = 25
        elif current_price < ma_5:
            ma_score = 35

        # Combine scores
        momentum_score = (current_rsi * 0.4 + ma_score * 0.3 +
                         min(max(mom_1d * 2 + 50, 0), 100) * 0.1 +
                         min(max(mom_5d + 50, 0), 100) * 0.1 +
                         min(max(mom_20d * 0.5 + 50, 0), 100) * 0.1)

        return round(momentum_score, 2)

    def calculate_liquidity_score(self, stock_data: pd.DataFrame) -> float:
        """Calculate liquidity score based on volume patterns (0-100)"""
        if stock_data.empty:
            return 0

        # Average volume
        avg_volume = stock_data['Volume'].mean()
        recent_volume = stock_data['Volume'].tail(5).mean()

        # Volume trend
        volume_trend = (recent_volume - avg_volume) / avg_volume * 100 if avg_volume > 0 else 0

        # Volume-price relationship
        price_changes = stock_data['Close'].pct_change()
        volume_changes = stock_data['Volume'].pct_change()
        correlation = price_changes.corr(volume_changes)
        correlation = 0 if np.isnan(correlation) else correlation

        # Normalize to 0-100 scale
        volume_score = min(100, max(0, 50 + volume_trend * 0.3 + correlation * 25))

        return round(volume_score, 2)

    def calculate_technical_strength_score(self, stock_data: pd.DataFrame) -> float:
        """Calculate technical strength based on multiple indicators (0-100)"""
        if stock_data.empty:
            return 50

        scores = []

        # Support/Resistance levels
        highs = stock_data['High'].rolling(20).max()
        lows = stock_data['Low'].rolling(20).min()
        current_price = stock_data['Close'].iloc[-1]

        # Price position within range
        price_position = ((current_price - lows.iloc[-1]) / (highs.iloc[-1] - lows.iloc[-1])) * 100
        scores.append(min(100, max(0, price_position)))

        # Volume-weighted average price deviation
        vwap = (stock_data['Close'] * stock_data['Volume']).sum() / stock_data['Volume'].sum()
        vwap_score = 50 + ((current_price - vwap) / vwap) * 100
        scores.append(min(100, max(0, vwap_score)))

        # Bollinger Bands position
        ma_20 = stock_data['Close'].rolling(20).mean()
        std_20 = stock_data['Close'].rolling(20).std()
        upper_band = ma_20 + (std_20 * 2)
        lower_band = ma_20 - (std_20 * 2)

        if not upper_band.empty and not lower_band.empty:
            bb_position = ((current_price - lower_band.iloc[-1]) /
                          (upper_band.iloc[-1] - lower_band.iloc[-1])) * 100
            scores.append(min(100, max(0, bb_position)))

        return round(np.mean(scores), 2)

    def calculate_market_correlation_score(self, symbol: str, stock_data: pd.DataFrame) -> float:
        """Calculate correlation with major indices (0-100)"""
        try:
            # Get Nifty 50 data for comparison
            nifty = yf.Ticker("^NSEI")
            nifty_data = nifty.history(period="1mo")

            if nifty_data.empty or stock_data.empty:
                return 50

            # Align dates
            common_dates = stock_data.index.intersection(nifty_data.index)
            if len(common_dates) < 5:
                return 50

            stock_returns = stock_data.loc[common_dates]['Close'].pct_change().dropna()
            nifty_returns = nifty_data.loc[common_dates]['Close'].pct_change().dropna()

            # Calculate correlation
            correlation = stock_returns.corr(nifty_returns)
            if np.isnan(correlation):
                return 50

            # Convert correlation to 0-100 score
            # High positive correlation = higher score (follows market)
            # Negative correlation = lower score (contrarian)
            correlation_score = (correlation + 1) * 50

            return round(correlation_score, 2)
        except Exception as e:
            print(f"Error calculating market correlation: {e}")
            return 50

    def calculate_growth_potential_score(self, stock_data: pd.DataFrame) -> float:
        """Calculate growth potential based on trend analysis (0-100)"""
        if stock_data.empty:
            return 50

        # Calculate different timeframe growth rates
        current_price = stock_data['Close'].iloc[-1]

        growth_scores = []

        # Weekly growth
        if len(stock_data) >= 5:
            week_ago_price = stock_data['Close'].iloc[-5]
            weekly_growth = ((current_price - week_ago_price) / week_ago_price) * 100
            weekly_score = min(100, max(0, 50 + weekly_growth * 2))
            growth_scores.append(weekly_score)

        # Monthly growth
        if len(stock_data) >= 20:
            month_ago_price = stock_data['Close'].iloc[-20]
            monthly_growth = ((current_price - month_ago_price) / month_ago_price) * 100
            monthly_score = min(100, max(0, 50 + monthly_growth))
            growth_scores.append(monthly_score)

        # Volume growth trend
        recent_volume = stock_data['Volume'].tail(5).mean()
        earlier_volume = stock_data['Volume'].head(5).mean()
        if earlier_volume > 0:
            volume_growth = ((recent_volume - earlier_volume) / earlier_volume) * 100
            volume_score = min(100, max(0, 50 + volume_growth * 0.5))
            growth_scores.append(volume_score)

        return round(np.mean(growth_scores) if growth_scores else 50, 2)

    def calculate_stability_score(self, stock_data: pd.DataFrame) -> float:
        """Calculate stability score based on price steadiness (0-100)"""
        if stock_data.empty:
            return 50

        # Calculate coefficient of variation
        returns = stock_data['Close'].pct_change().dropna()
        mean_return = returns.mean()
        std_return = returns.std()

        if mean_return != 0:
            cv = abs(std_return / mean_return)
            # Lower CV = higher stability
            stability_score = max(0, 100 - cv * 100)
        else:
            stability_score = 50

        # Consider price gaps
        gaps = abs(stock_data['Open'] - stock_data['Close'].shift()).dropna()
        avg_gap = gaps.mean()
        avg_price = stock_data['Close'].mean()

        if avg_price > 0:
            gap_ratio = avg_gap / avg_price
            gap_penalty = min(50, gap_ratio * 1000)
            stability_score = max(0, stability_score - gap_penalty)

        return round(stability_score, 2)

    def calculate_risk_score(self, analysis: Dict) -> float:
        """Calculate risk score based on multiple factors"""
        risk_factors = [
            analysis['volatility_score'],
            analysis['fear_score'],
            100 - analysis['liquidity_score'],
            100 - analysis['technical_strength_score'],
            100 - analysis['stability_score']
        ]
        return round(np.mean(risk_factors), 2)

    def calculate_investment_attractiveness(self, analysis: Dict) -> float:
        """Calculate investment attractiveness score"""
        attractiveness_factors = [
            analysis['overall_sentiment_score'],
            analysis['growth_potential_score'],
            analysis['momentum_score'],
            100 - analysis['risk_score']
        ]
        return round(np.mean(attractiveness_factors), 2)

    def get_comprehensive_analysis(self, symbol: str, company_name: str = None) -> Dict:
        """Get comprehensive sentiment analysis for a stock"""
        # If company name not provided, try to extract from symbol
        self.symbol = symbol
            # If company name not provided, try to extract from symbol
        if not company_name:
            try:
                # Add .NS for NSE stocks if not present
                if not symbol.endswith('.NS') and not symbol.endswith('.BO'):
                    symbol_with_suffix = symbol + '.NS'
                else:
                    symbol_with_suffix = symbol
                
                # Get company info from yfinance
                ticker = yf.Ticker(symbol_with_suffix)
                info = ticker.info
                
                # Extract company name with multiple fallbacks
                company_name = (
                    info.get('longName') or 
                    info.get('shortName') or 
                    info.get('name') or 
                    symbol  # Final fallback to symbol
                )
                
                # Validate the extracted name
                if company_name:
                    # Remove special characters and check if meaningful
                    cleaned_name = ''.join(c for c in company_name if c.isalnum() or c in (' ', '-', '&'))
                    if (len(cleaned_name.strip()) < 2 or  # Too short
                        cleaned_name.strip() == symbol or  # Same as symbol
                        any(x in cleaned_name for x in ['-', ' - ']) or  # Contains dashes (likely placeholder)
                        cleaned_name.isnumeric()):  # Just numbers
                        company_name = symbol  # Fallback to symbol if name is invalid
                else:
                    company_name = symbol
                    
            except Exception as e:
                print(f"⚠️ Could not fetch company name for {symbol}: {str(e)}")
                company_name = symbol
    
        # Ensure we have at least the symbol as name
        company_name = company_name or symbol
        print(company_name)

        print(f"\n{'='*80}")
        print(f"🔍 ANALYZING: {company_name.upper()} ({symbol})")
        print(f"{'='*80}")

        # Get stock data
        print("📊 Fetching stock data...")
        stock_data = self.get_stock_data(symbol)

        if stock_data.empty:
            print("❌ Could not fetch stock data. Please check the symbol.")
            return {}

        # Get news sentiment
        print("📰 Scraping news sentiment...")
        news_data = self.scrape_news_sentiment(company_name, symbol)

        # Calculate all scores
        print("🧮 Calculating sentiment scores...")

        # Basic stock info
        current_price = stock_data['Close'].iloc[-1]
        prev_close = stock_data['Close'].iloc[-2] if len(stock_data) > 1 else current_price
        price_change = current_price - prev_close
        price_change_pct = (price_change / prev_close) * 100 if prev_close != 0 else 0

        analysis = {
            'symbol': symbol,
            'company_name': company_name,
            'analysis_date': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
            'current_price': round(current_price, 2),
            'price_change': round(price_change, 2),
            'price_change_pct': round(price_change_pct, 2),
            'volume': int(stock_data['Volume'].iloc[-1]),
            'market_cap_approx': 'N/A',  # Would need additional API for exact market cap

            # Innovative Scores
            'volatility_score': self.calculate_volatility_score(stock_data),
            'momentum_score': self.calculate_momentum_score(stock_data),
            'liquidity_score': self.calculate_liquidity_score(stock_data),
            'technical_strength_score': self.calculate_technical_strength_score(stock_data),
            'market_correlation_score': self.calculate_market_correlation_score(symbol, stock_data),
            'growth_potential_score': self.calculate_growth_potential_score(stock_data),
            'stability_score': self.calculate_stability_score(stock_data),

            # News sentiment scores
            **self.calculate_news_sentiment_score(news_data),

            # Additional metrics
            'news_count': len(news_data['headlines']),
            'recent_headlines': news_data['headlines'][:5]  # Top 5 headlines
        }

        # Calculate risk score
        analysis['risk_score'] = self.calculate_risk_score(analysis)
        
        # Calculate risk level based on risk score
        if analysis['risk_score'] >= 75:
            analysis['risk_level'] = "VERY HIGH"
        elif analysis['risk_score'] >= 60:
            analysis['risk_level'] = "HIGH"
        elif analysis['risk_score'] >= 40:
            analysis['risk_level'] = "MODERATE"
        elif analysis['risk_score'] >= 25:
            analysis['risk_level'] = "LOW"
        else:
            analysis['risk_level'] = "VERY LOW"

        # Add risk factors based on analysis
        analysis['risk_factors'] = []
        if analysis['volatility_score'] > 70:
            analysis['risk_factors'].append("High market volatility")
        if analysis['fear_score'] > 60:
            analysis['risk_factors'].append("Elevated market fear")
        if analysis['negative_score'] > 60:
            analysis['risk_factors'].append("Negative sentiment trend")
        if analysis['market_correlation_score'] < 30:
            analysis['risk_factors'].append("Low market correlation")
        if analysis['stability_score'] < 40:
            analysis['risk_factors'].append("Low stability indicators")
            
        # Calculate investment attractiveness
        analysis['investment_attractiveness_score'] = self.calculate_investment_attractiveness(analysis)

        return analysis

    def generate_recommendation(self, analysis: Dict) -> str:
        """Generate trading recommendation based on analysis"""
        if not analysis:
            return "Unable to generate recommendation - insufficient data"

        sentiment = analysis['overall_sentiment_score']
        risk = analysis['risk_score']
        momentum = analysis['momentum_score']
        volatility = analysis['volatility_score']
        attractiveness = analysis['investment_attractiveness_score']

        if sentiment > 70 and risk < 40 and momentum > 60 and attractiveness > 65:
            return "🟢 STRONG BUY - High sentiment, low risk, strong momentum"
        elif sentiment > 60 and risk < 50 and attractiveness > 55:
            return "🟢 BUY - Positive sentiment with manageable risk"
        elif sentiment > 40 and sentiment < 60 and risk < 60:
            return "🟡 HOLD - Neutral sentiment, monitor closely"
        elif sentiment < 40 and risk > 60:
            return "🔴 SELL - Negative sentiment with high risk"
        elif sentiment < 30 or risk > 75:
            return "🔴 STRONG SELL - Very negative sentiment or very high risk"
        else:
            return "🟡 HOLD - Mixed signals, proceed with caution"

    def display_analysis(self, analysis: Dict):
        """Display comprehensive analysis in a formatted way"""
        if not analysis:
            print("❌ No analysis data available")
            return

        print(f"\n{'='*80}")
        print(f"📈 COMPREHENSIVE STOCK ANALYSIS REPORT")
        print(f"{'='*80}")

        # Basic Info
        print(f"\n📊 BASIC INFORMATION:")
        print(f"Company: {analysis['company_name']}")
        print(f"Symbol: {analysis['symbol']}")
        print(f"Current Price: ₹{analysis['current_price']}")
        print(f"Price Change: ₹{analysis['price_change']} ({analysis['price_change_pct']:+.2f}%)")
        print(f"Volume: {analysis['volume']:,}")
        print(f"Analysis Date: {analysis['analysis_date']}")

        # Sentiment Scores
        print(f"\n🎯 SENTIMENT SCORES (0-100):")
        print(f"Overall Sentiment Score: {analysis['overall_sentiment_score']}/100")
        print(f"Positive Score: {analysis['positive_score']}/100")
        print(f"Negative Score: {analysis['negative_score']}/100")
        print(f"Fear Score: {analysis['fear_score']}/100")
        print(f"Confidence Score: {analysis['confidence_score']}/100")

        # Technical Scores
        print(f"\n⚙️ TECHNICAL SCORES (0-100):")
        print(f"Volatility Score: {analysis['volatility_score']}/100")
        print(f"Momentum Score: {analysis['momentum_score']}/100")
        print(f"Technical Strength: {analysis['technical_strength_score']}/100")
        print(f"Liquidity Score: {analysis['liquidity_score']}/100")
        print(f"Market Correlation: {analysis['market_correlation_score']}/100")

        # Advanced Scores
        print(f"\n🚀 ADVANCED SCORES (0-100):")
        print(f"Growth Potential: {analysis['growth_potential_score']}/100")
        print(f"Stability Score: {analysis['stability_score']}/100")
        print(f"Risk Score: {analysis['risk_score']}/100")
        print(f"Investment Attractiveness: {analysis['investment_attractiveness_score']}/100")

        # Recommendation
        recommendation = self.generate_recommendation(analysis)
        print(f"\n💡 RECOMMENDATION:")
        print(f"{recommendation}")

        # News Analysis
        print(f"\n📰 NEWS ANALYSIS:")
        print(f"Headlines Analyzed: {analysis['news_count']}")
        if analysis['recent_headlines']:
            print(f"\n📋 Recent Headlines:")
            for i, headline in enumerate(analysis['recent_headlines'], 1):
                print(f"{i}. {headline}")

        # Risk Assessment
        print(f"\n⚠️ RISK ASSESSMENT:")
        print(f"Risk Level: {analysis['risk_level']}")
        print(f"Key Risk Factors:")
        for risk_factor in analysis['risk_factors']:
            print(f"- {risk_factor}")

        # Save analysis to JSON
        output_file = f"analysis_{self.symbol}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
        with open(output_file, 'w') as f:
            json.dump(analysis, f, indent=4)
        print(f"\n💾 Analysis saved to {output_file}")

def main():
    """Main function to run the stock analysis"""
    analyzer = StockSentimentAnalyzer()

    print("🚀 Welcome to Stock Sentiment Analyzer!")
    print("Enter stock symbols (e.g., RELIANCE, TCS, HDFCBANK)")
    print("The system will automatically add .NS for NSE stocks")
    print("Type 'quit' to exit\n")

    while True:
        try:
            # Get user input
            user_input = input("Enter stock symbol: ").strip().upper()

            if user_input.lower() == 'quit':
                print("👋 Thank you for using Stock Sentiment Analyzer!")
                break

            if not user_input:
                print("❌ Please enter a valid stock symbol")
                continue

            # Get company name (optional)
            company_name = input("Enter company name (optional, press Enter to skip): ").strip()

            # Perform analysis
            analysis = analyzer.get_comprehensive_analysis(user_input, company_name if company_name else None)
            
            # Display results
            if analysis:
                analyzer.display_analysis(analysis)

        except Exception as e:
            print(f"❌ Error: {str(e)}")
            print("Please try again with a different stock symbol")

if __name__ == "__main__":
    main()