3

app.py

@@ -2,8 +2,6 @@ import streamlit as st
 from urllib.request import urlopen, Request
 from bs4 import BeautifulSoup
 import pandas as pd
-import plotly
-import plotly.express as px
 import json # for graph plotting in website
 # NLTK VADER for sentiment analysis
 import nltk
@@ -85,25 +83,6 @@ def score_news(parsed_news_df):
 
     return parsed_and_scored_news
 
-
-def plot_hourly_sentiment(parsed_and_scored_news, ticker):
-
-    # Group by date and ticker columns from scored_news and calculate the mean
-    mean_scores = parsed_and_scored_news.resample('H').mean()
-
-    # Plot a bar chart with plotly
-    fig = px.bar(mean_scores, x=mean_scores.index, y='sentiment_score', title = ticker + ' Hourly Sentiment Scores')
-    return fig # instead of using fig.show(), we return fig and turn it into a graphjson object for displaying in web page later
-
-def plot_daily_sentiment(parsed_and_scored_news, ticker):
-
-    # Group by date and ticker columns from scored_news and calculate the mean
-    mean_scores = parsed_and_scored_news.resample('D').mean()
-
-    # Plot a bar chart with plotly
-    fig = px.bar(mean_scores, x=mean_scores.index, y='sentiment_score', title = ticker + ' Daily Sentiment Scores')
-    return fig # instead of using fig.show(), we return fig and turn it into a graphjson object for displaying in web page later
-
 # for extracting data from finviz
 finviz_url = 'https://finviz.com/quote.ashx?t='
 
@@ -121,7 +100,6 @@ try:
 	parsed_news_df = parse_news(news_table)
 	print(parsed_news_df)
 	parsed_and_scored_news = score_news(parsed_news_df)
-
 	st.table(parsed_and_scored_news)
 
 except Exception as e: