app.py

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)