from flask import Flask, render_template, request
import plotly.graph_objs as go
from transformers import pipeline
import yfinance as yf
import requests
from newspaper import Article
from newspaper import Config
from bs4 import BeautifulSoup
from datetime import datetime, timedelta
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import BartForConditionalGeneration, BartTokenizer


app = Flask(__name__)

# Set up sentiment analysis and summarization pipelines
finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3)
sentiment_tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone')
summarization_model = BartForConditionalGeneration.from_pretrained("facebook/bart-large", forced_bos_token_id=0)
summarization_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large")
sentiment_analysis_pipeline = pipeline("sentiment-analysis",model=finbert,tokenizer=sentiment_tokenizer,truncation = True,max_length = 512)
summarization_pipeline = pipeline("summarization",model=summarization_model,tokenizer=summarization_tokenizer,max_length = 512,truncation=True)

# News API setup
def get_news_articles_info(ticker_name):
    # URL of the search results page
    url = "https://www.marketwatch.com/search?q=" + ticker_name + "&ts=0&tab=All%20News"

    # Send an HTTP GET request to the URL
    response = requests.get(url)

    # Parse the HTML content using BeautifulSoup
    soup = BeautifulSoup(response.content, "html.parser")

    article_links = []
    
    for content in soup.find_all("h3",class_="article__headline"):
        for link in content.find_all("a"):
            if link['href'] != "#":
                article_links.append(link['href'])

    article_links = article_links[18:36]
    ticker_news_extracted = []
    count = 0
    for link in article_links:
        article_info = {}
        article_info['text'] = ''
        article_info['url'] = link
        try:
            url = article_info['url']
            url = requests.head(url, allow_redirects=True).url
            
            user_agent = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.121 Safari/537.36'
            config = Config()
            config.browser_user_agent = user_agent

            article = Article(url, config=config)
            article.download()
            article.parse()
            article_info['title'] = article.title
            article_info['text'] = article.text
            article_info['url'] = url
            count = count + 1
            print(count,url)
        except Exception as error:
            print("Error",url,error)
            continue
        if article_info['text'] == '':
            print('No text',url)
            continue
        if count > 5:
            break
        ticker_news_extracted.append(article_info)
    return ticker_news_extracted

@app.route("/", methods=["GET", "POST"])
def index():
    if request.method == "POST":
        ticker = request.form["ticker"]
        
        # Get top 5 news articles for the given ticker
        search_results = get_news_articles_info(ticker_name=ticker)
        articles = search_results[:5]

        print("Getting the info finished")
        
        news_data = []
        
        for article in articles:
            title = article['title']
            link = article['url']
            content = article['text']
            
            # Perform sentiment analysis on article content
            sentiment = sentiment_analysis_pipeline(content)[0]
            
            # Summarize article content
            summary = summarization_pipeline(content, max_length=100, min_length=30, do_sample=False)[0]["summary_text"]
            
            news_data.append({
                "title": title,
                "link": link,
                "sentiment": sentiment["label"],
                "sentiment_score": round(sentiment["score"],3),
                "summary": summary,
                "Ticker": ticker
            })

            print(link)
        
        # Define the stock ticker symbol and the date range
        ticker_symbol = ticker  # Example: Apple Inc.
        end_date = datetime.today()
        start_date = end_date - timedelta(days=90)

        # Fetch historical data using yfinance
        data = yf.download(ticker_symbol, start=start_date, end=end_date)

        # Create a candlestick graph using Plotly
        fig = go.Figure(data=[go.Candlestick(x=data.index,
                        open=data['Open'],
                        high=data['High'],
                        low=data['Low'],
                        close=data['Close'])])

        # Customize the layout
        fig.update_layout(title=f'Candlestick Chart for {ticker_symbol} in the Last 90 Days',
                        xaxis_title='Date',
                        yaxis_title='Price',
                        xaxis_rangeslider_visible=False)

        # convert the fig to HTML DIV element
        graph_html = fig.to_html(full_html=False)
        
        return render_template("index.html", news_data=news_data, candlestick_graph=graph_html,ticker = ticker)
    
    return render_template("index.html")

if __name__ == "__main__":
    app.run(host='0.0.0.0',port=7860)