import requests
from bs4 import BeautifulSoup
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
from collections import Counter
import time
import json
import numpy as np

def sentiment_analysis(querystring, headers):

    # 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)

    def calculate_sentiment_scores(sentiment_data):
        # Convert list values to their lengths, excluding 'details'
        processed = {
            k: len(v) if isinstance(v, list) and k != 'details' else v
            for k, v in sentiment_data.items() if k != 'details'
        }
        total = sum(processed.values())
        
        return {
            "overall": max(processed, key=processed.get) if processed else "neutral",
            "positive_percent": processed.get("positive", 0) / total * 100 if total > 0 else 0,
            "negative_percent": processed.get("negative", 0) / total * 100 if total > 0 else 0,
            "sentiment_ratio": processed.get("positive", 0) / processed.get("negative", 1) if processed.get("negative", 1) != 0 else float('-99999999'),
            "average_confidence": sum(sentiment_data.get("confidence", [0])) / len(sentiment_data.get("confidence", [0])) if sentiment_data.get("confidence") else 0
        }

    # API setup
    url = "https://indian-stock-exchange-api2.p.rapidapi.com/stock"

    # Step 1: Get stock data
    print("Fetching stock data...")
    response = requests.get(url, headers=headers, params=querystring)
    data = response.json()
    news_data = data.get("recentNews", {})
    print(f"Found {len(news_data)} news articles")

    # Step 2: Extract URLs
    urls = [item["url"] for item in news_data if isinstance(item, dict) and "url" in item]
    print(f"Processing {len(urls)} articles...")

    # Step 3: Analyze sentiment for each article
    summary = Counter()
    details = []

    for i, news_item in enumerate(news_data):
                news_url = news_item.get("url")
                headline = news_item.get("headline", "")
                intro = news_item.get("intro", "")

                content_for_sentiment = ""
                if news_url:
                    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")

                        # Grab <p> tags and filter
                        paragraphs = soup.find_all("p")
                        if not paragraphs:
                            raise ValueError("No content found in paragraphs")

                        content_for_sentiment = " ".join(p.get_text() for p in paragraphs if len(p.get_text()) > 40)
                        content_for_sentiment = content_for_sentiment.strip()
                        if len(content_for_sentiment) < 100:
                            print("→ Content too short from web scraping, falling back to headline/intro")
                            content_for_sentiment = headline + " ." + intro

                    except Exception as e:
                        print(f"❌ Error scraping {news_url}: {str(e)}. Falling back to headline/intro for sentiment analysis.")
                        content_for_sentiment = headline + " ." + intro
                else:
                    print(f"\n[{i+1}/{len(urls)}] No URL provided, using headline/intro for sentiment analysis.")
                    content_for_sentiment = headline + " ." + intro

                if not content_for_sentiment.strip():
                    print("→ No content available for sentiment analysis, skipping.")
                    continue

                # Truncate to 512 tokens max
                content_for_sentiment = content_for_sentiment[:1000]
                result = classifier(content_for_sentiment[:512])[0]
                label = result['label'].lower()
                score = round(result['score'], 3)

                summary[label] += 1
                details.append({
                    "url": news_url,
                    "title": news_item.get("title", "No title"), # Use title from news_item if available
                    "sentiment": label,
                    "confidence": score,
                    "content_length": len(content_for_sentiment),
                    "image_222x148": news_item.get("image_222x148"),
                    "intro": intro,
                    "headline": headline
                })

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

    # Step 4: Generate comprehensive output
    sentiment_scores = calculate_sentiment_scores({
        "positive": summary["positive"],
        "negative": summary["negative"],
        "neutral": summary["neutral"],
        "details": details
    })

    output = {
        "metadata": {
            "total_articles": len(urls),
            "processed_articles": len(details),
            "processing_time": time.strftime("%Y-%m-%d %H:%M:%S")
        },
        "sentiment_metrics": {
            "overall_score": sentiment_scores["overall"], # Removed round() for string label
            "positive_score": round(sentiment_scores["positive_percent"], 2),
            "negative_score": round(sentiment_scores["negative_percent"], 2),
            "sentiment_ratio": round(sentiment_scores["sentiment_ratio"], 2),
            "average_confidence": round(sentiment_scores["average_confidence"], 2)
        },
        "article_details": details
    }

    # Print formatted results
    print("\n=== SENTIMENT ANALYSIS RESULTS ===")
    print(f"Overall Sentiment Score: {output['sentiment_metrics']['overall_score']}") # Updated print statement
    print(f"Positive/Negative Ratio: {output['sentiment_metrics']['sentiment_ratio']:.2f}")
    print(f"Average Confidence: {output['sentiment_metrics']['average_confidence']:.1f}%")


    """
    import json
    with open("sentiment_results.json", "w") as f:
        json.dump(output, f, indent=2)
    print("Results saved to sentiment_results.json")
    """
    
    return output

def mainOne(querystring):
    """
    Main function that takes querystring as parameter and runs sentiment analysis
    Args:
        querystring: Dictionary containing stock name (e.g. {'name': 'HDFC BANK'})
    Returns:
        Dictionary containing sentiment analysis results
    """
    try:
        headers = {
            "x-rapidapi-host": "indian-stock-exchange-api2.p.rapidapi.com",
            "x-rapidapi-key": "a12f59fc40msh153da8fdf3885b6p100406jsn57d1d84b0d06"
        }
        
        # Run the sentiment analysis
        results = sentiment_analysis(querystring, headers)
        return results
        
    except Exception as e:
        print(f"Error in main function: {str(e)}")
        return {"error": str(e)}