from flask import Flask, request, jsonify, send_file
from bs4 import BeautifulSoup
from newspaper import Article
from textblob import TextBlob
from newsapi import NewsApiClient
from transformers import pipeline
import requests
from utils import *
import pandas as pd
import base64

app = Flask(__name__)

newsapi = NewsApiClient(api_key='YOUR_NEWS_API_KEY')  # Replace with your API key

@app.route('/analyze_news', methods=['GET'])
def analyze_news():
    company = request.args.get('company')
    source = request.args.get('company')
    if not company or not source:
        return jsonify({"error": "Please provide a company name as a query parameter"}), 400
    
    all_articles = []
    output = {"Company": f"{company}", "Articles": all_articles}

    if source == "NewsOrg":
        # Fetch articles from News API
        response = newsapi.get_everything(q=company, page_size=5, sort_by='publishedAt', language='en')

        results = []
        sentiment_count = {"Positive": 0, "Negative": 0, "Neutral": 0}

        for idx, article in enumerate(response['articles']):
            url = article.get("url")
            news_article = Article(url)
            try:
                news_article.download()
                news_article.parse()
            except:
                continue

            blob = TextBlob(news_article.text)
            polarity = blob.sentiment.polarity

            if polarity > 0.3:
                sentiment = "Positive"
            elif polarity < -0.3:
                sentiment = "Negative"
            else:
                sentiment = "Neutral"

            sentiment_count[sentiment] += 1

            results.append({
                "title": article.get("title"),
                "author": article.get("author"),
                "summary": article.get("description"),
                "sentiment": sentiment,
                "url": article.get("url")
            })

        return jsonify({
            "company": company,
            "sentiment_distribution": sentiment_count,
            "articles": results
        })
    elif source == "Yahoo News":
        url = f"https://finance.yahoo.com/quote/{company}/news/"
        headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36 Edg/120.0.0.0'}
        response = requests.get(url, headers=headers)

        if response.status_code != 200:
            print("Failed to fetch news articles")
            return {}

        paragraphs = []
        titles = []
        summaries = []
        soup = BeautifulSoup(response.content, 'html.parser')

        overall_sentiment_count = 0
        positive_sentiment_count = 0
        negative_sentiment_count = 0
        neutral_sentiment_count = 0

        for news in soup.find_all("div", class_="holder yf-1napat3"):
            title_all = news.find_all('h3', class_="clamp yf-82qtw3")
            summary_all = news.find_all('p', class_="clamp yf-82qtw3")
            for title, summary in zip(title_all, summary_all):
                title_text = title.get_text()
                summary_text = summary.get_text()
                paragraph = title_text + ' ' + summary_text
                titles.append(title_text)
                summaries.append(summary_text)
                paragraphs.append(paragraph)
        
            # Analyze sentiment and prepare the output
            for i, paragraph in enumerate(paragraphs):
                sentiment = analyze_sentiment(paragraph)
                if sentiment == "POSITIVE":
                    positive_sentiment_count += 1
                    overall_sentiment_count += 1
                elif sentiment == "NEGATIVE":
                    negative_sentiment_count += 1
                    overall_sentiment_count -= 1
                else:
                    neutral_sentiment_count += 1
                # top_words = 


            article = {
                "Title": titles[i],
                "Summary": summaries[i],
                "Sentiment": sentiment
            }

            all_articles.append(article)

        output["Comparitive Sentiment Score"]["Sentiment Distribution"] = {
            "Positive": positive_sentiment_count,
            "Negative": negative_sentiment_count,
            "Neutral": neutral_sentiment_count,
        }

        if overall_sentiment_count>0:
            output["Final Sentiment Analysis"] = f"{company.capitalize()}'s lastest news is mostly positive. Potential stock growth expected."
        elif overall_sentiment_count<0:
            output["Final Sentiment Analysis"] = f"{company.capitalize()}'s lastest news is mostly negative. Potential stock decline expected."
        else:
            output["Final Sentiment Analysis"] = f"{company.capitalize()}'s lastest news is mostly neutral. Stocks going to stay stagnant for some time."
        
        df = pd.DataFrame(article)
        text_to_summarize = " ".join([d['Title'] + " " + d['summary'] for d in article[:5]])
        summary_final = summarize_text(text_to_summarize)

        audio_path = generate_hindi_tts(summary_final)
        if audio_path:# and os.path.exists(audio_path):
            # Convert audio file to base64
            with open(audio_path, "rb") as f:
                audio_base64 = base64.b64encode(f.read()).decode('utf-8')
            
            output["Audio"] = audio_base64

            return output

        else:
            return jsonify({"error": "Failed to generate audio"}), 500

if __name__ == '__main__':
    app.run(debug=True, port=8000)