tools/sentiment_analysis_util.py

import os
from dotenv import load_dotenv
from transformers import pipeline
import pandas as pd
#from langchain_openai import ChatOpenAI
import praw
from datetime import datetime
import numpy as np
#from tavily import TavilyClient

load_dotenv()
#TAVILY_API_KEY = os.environ["TAVILY_API_KEY"]

# def fetch_news(topic):
    
#     """ Fetches news articles within a specified date range.

#     Args:
#     - topic (str): Topic of interest
    
#     Returns:
#     - list: A list of dictionaries containing news. """
    
#     load_dotenv()
#     days_to_fetch_news = os.environ["DAYS_TO_FETCH_NEWS"]

#     googlenews = GoogleNews()
#     googlenews.set_period(days_to_fetch_news)
#     googlenews.get_news(topic)
#     news_json=googlenews.get_texts()
#     urls=googlenews.get_links()
    
#     no_of_news_articles_to_fetch = os.environ["NO_OF_NEWS_ARTICLES_TO_FETCH"]
#     news_article_list = []
#     counter = 0
#     for article in news_json:
        
#         if(counter >= int(no_of_news_articles_to_fetch)):
#             break

#         relevant_info = {
#             'News_Article': article,
#             'URL': urls[counter]
#         }
#         news_article_list.append(relevant_info)
#         counter+=1
#     return news_article_list

def fetch_tavily_news(topic):
    """ Fetches news articles.

    Args:
    - topic (str): Topic of interest
    
    Returns:
    - list: A list of dictionaries containing news. """
    
    # Step 1. Instantiating your TavilyClient
    tavily_client = TavilyClient(api_key=TAVILY_API_KEY)

    #response = tavily_client.search(topic)
    # Step 2.1. Executing a context search query
    answer = tavily_client.get_search_context(query=f"Give me news on {topic}")
    
    line=[]
    tavily_news=[]

    for i in range(len(answer.split("url")))[1:]:
        https_link=(answer.split("url")[i].split("\\\\\\")[2]).split('"')[1]
        topic_answer=answer.split("url")[i].split("\\\\\\")[-3]
        tavily_news=np.append(tavily_news,{'https':https_link,'topic_answer':topic_answer})

    return tavily_news

def fetch_reddit_news(topic):
    load_dotenv()
    REDDIT_USER_AGENT= os.environ["REDDIT_USER_AGENT"]
    REDDIT_CLIENT_ID= os.environ["REDDIT_CLIENT_ID"]
    REDDIT_CLIENT_SECRET= os.environ["REDDIT_CLIENT_SECRET"]
    #https://medium.com/geekculture/a-complete-guide-to-web-scraping-reddit-with-python-16e292317a52
    user_agent = REDDIT_USER_AGENT
    reddit = praw.Reddit (
    client_id= REDDIT_CLIENT_ID,
    client_secret= REDDIT_CLIENT_SECRET,
    user_agent=user_agent
    )

    headlines = set ( )
    for submission in reddit.subreddit('nova').search('job',time_filter='day'):
        headlines.add(submission.title + ', Date: ' +datetime.utcfromtimestamp(int(submission.created_utc)).strftime('%Y-%m-%d %H:%M:%S') + ', URL:' +submission.url)
    
    for submission in reddit.subreddit('fednews').search('labor',time_filter='day'):
        headlines.add(submission.title + ', Date: ' +datetime.utcfromtimestamp(int(submission.created_utc)).strftime('%Y-%m-%d %H:%M:%S') + ', URL:' +submission.url)
    
    for submission in reddit.subreddit('fednews').search('job',time_filter='day'):
        headlines.add(submission.title + ', Date: ' +datetime.utcfromtimestamp(int(submission.created_utc)).strftime('%Y-%m-%d %H:%M:%S') + ', URL:' +submission.url)
    
    for submission in reddit.subreddit('fednews').search('employment',time_filter='day'):
        headlines.add(submission.title + ', Date: ' +datetime.utcfromtimestamp(int(submission.created_utc)).strftime('%Y-%m-%d %H:%M:%S') + ', URL:' +submission.url)
    
    for submission in reddit.subreddit('fednews').search('layoff',time_filter='day'):
        headlines.add(submission.title + ', Date: ' +datetime.utcfromtimestamp(int(submission.created_utc)).strftime('%Y-%m-%d %H:%M:%S') + ', URL:' +submission.url)
    

    for submission in reddit.subreddit('washingtondc').search('job',time_filter='day'):
        headlines.add(submission.title + ', Date: ' +datetime.utcfromtimestamp(int(submission.created_utc)).strftime('%Y-%m-%d %H:%M:%S') + ', URL:' +submission.url)
    
    #if len(headlines)<10:
    #    for submission in reddit.subreddit('washingtondc').search(topic,time_filter='year'):
    #        headlines.add(submission.title + ', Date: ' +datetime.utcfromtimestamp(int(submission.created_utc)).strftime('%Y-%m-%d %H:%M:%S') + ', URL:' +submission.url)
    #if len(headlines)<10:
    #    for submission in reddit.subreddit('washingtondc').search(topic): #,time_filter='week'):
    #        headlines.add(submission.title + ', Date: ' +datetime.utcfromtimestamp(int(submission.created_utc)).strftime('%Y-%m-%d %H:%M:%S') + ', URL:' +submission.url)

    return headlines

def analyze_sentiment(article):
    """
    Analyzes the sentiment of a given news article.

    Args:
    - news_article (dict): Dictionary containing 'summary', 'headline', and 'created_at' keys.

    Returns:
    - dict: A dictionary containing sentiment analysis results.
    """

    #Analyze sentiment using default model
    #classifier = pipeline('sentiment-analysis')

    #Analyze sentiment using specific model
    classifier = pipeline(model='tabularisai/robust-sentiment-analysis') #mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis')
    sentiment_result = classifier(str(article))

    analysis_result = {
        'News_Article': article,
        'Sentiment': sentiment_result
    }

    return analysis_result


# def generate_summary_of_sentiment(sentiment_analysis_results): #, dominant_sentiment):
    
    
#     news_article_sentiment = str(sentiment_analysis_results)
#     print("News article sentiment : " + news_article_sentiment)
    

#     OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
#     model = ChatOpenAI(
#         model="gpt-4o",
#         temperature=0,
#         max_tokens=None,
#         timeout=None,
#         max_retries=2,
#         api_key=OPENAI_API_KEY,  # if you prefer to pass api key in directly instaed of using env vars
#         # base_url="...",
#         # organization="...",
#         # other params...
#     )

#     messages=[
#             {"role": "system", "content": "You are a helpful assistant that looks at all news articles with their sentiment, hyperlink and date in front of the article text, the articles MUST be ordered by date!, and generate a summary rationalizing dominant sentiment. At the end of the summary, add URL links with dates for all the articles in the markdown format for streamlit. Make sure the articles as well as the links are ordered descending by Date!!!!!!! Example of adding the URLs: The Check out the links: [link](%s) % url, 2024-03-01. "},
#             {"role": "user", "content": f"News articles and their sentiments: {news_article_sentiment}"} #, and dominant sentiment is: {dominant_sentiment}"}
#     ]
#     response = model.invoke(messages)
    

#     summary = response.content
#     print ("+++++++++++++++++++++++++++++++++++++++++++++++")
#     print(summary)
#     print ("+++++++++++++++++++++++++++++++++++++++++++++++")
#     return summary


# def plot_sentiment_graph(sentiment_analysis_results):
#     """
#     Plots a sentiment analysis graph 

#     Args:
#     - sentiment_analysis_result): (dict): Dictionary containing 'Review Title : Summary', 'Rating', and 'Sentiment' keys.

#     Returns:
#     - dict: A dictionary containing sentiment analysis results.
#     """
#     df = pd.DataFrame(sentiment_analysis_results)
#     print(df)

#     #Group by Rating, sentiment value count
#     grouped = df['Sentiment'].value_counts()

#     sentiment_counts = df['Sentiment'].value_counts()

#     # Plotting pie chart
#     # fig = plt.figure(figsize=(5, 3))
#     # plt.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%', startangle=140)
#     # plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
    
#     #Open below when u running this program locally and c
#     #plt.show()

#     return sentiment_counts


# def get_dominant_sentiment (sentiment_analysis_results):
#     """
#     Returns overall sentiment, negative or positive or neutral depending on the count of negative sentiment vs positive sentiment 

#     Args:
#     - sentiment_analysis_result): (dict): Dictionary containing 'summary', 'headline', and 'created_at' keys.

#     Returns:
#     - dict: A dictionary containing sentiment analysis results.
#     """
#     df = pd.DataFrame(sentiment_analysis_results)

#     # Group by the 'sentiment' column and count the occurrences of each sentiment value
#     print(df)
#     print(df['Sentiment'])
#     sentiment_counts = df['Sentiment'].value_counts().reset_index()
#     sentiment_counts.columns = ['sentiment', 'count']
#     print(sentiment_counts)

#     # Find the sentiment with the highest count
#     dominant_sentiment = sentiment_counts.loc[sentiment_counts['count'].idxmax()]

#     return dominant_sentiment['sentiment']

# #starting point of the program
# if __name__ == '__main__':
    
#     #fetch news
#     news_articles = fetch_news('AAPL')

#     analysis_results = []
    
#     #Perform sentiment analysis for each product review
#     for article in news_articles:
#         sentiment_analysis_result = analyze_sentiment(article['News_Article'])

#         # Display sentiment analysis results
#         print(f'News Article: {sentiment_analysis_result["News_Article"]} : Sentiment: {sentiment_analysis_result["Sentiment"]}', '\n')

#         result = {
#                     'News_Article': sentiment_analysis_result["News_Article"],
#                     'Sentiment': sentiment_analysis_result["Sentiment"][0]['label']
#                 }
        
#         analysis_results.append(result)

    
#     #Graph dominant sentiment based on sentiment analysis data of reviews
#     dominant_sentiment = get_dominant_sentiment(analysis_results)
#     print(dominant_sentiment)
    
#     #Plot graph
#     plot_sentiment_graph(analysis_results)