Update app.py

app.py

@@ -2,7 +2,7 @@ import streamlit as st
 import tweepy as tw
 import pandas as pd
 from transformers import pipeline
-
+import os
 
 consumer_key = 'OCgWzDW6PaBvBeVimmGBqdAg1'
 consumer_secret = 'tBKnmyg5Jfsewkpmw74gxHZbbZkGIH6Ee4rsM0lD1vFL7SrEIM'
@@ -14,22 +14,46 @@ auth.set_access_token(access_token, access_token_secret)
 api = tw.API(auth, wait_on_rate_limit=True)
 classifier = pipeline('sentiment-analysis')
 
-st.title('Live Twitter Sentiment Analysis with Tweepy and HuggingFace Transformers')
-st.markdown('This app uses tweepy to get tweets from twitter based on the input name/phrase. It then processes the tweets through HuggingFace transformers pipeline function for sentiment analysis. The resulting sentiments and corresponding tweets are then put in a dataframe for display which is what you see as result.')
+# Define file name and headers
+FILE_NAME = 'query_history.csv'
+HEADERS = ['Search Query', 'Number of Tweets', 'Results', 'Date']
 
-def run():
+# Create file if it does not exist
+if not os.path.isfile(FILE_NAME):
+    df = pd.DataFrame(columns=HEADERS)
+    df.to_csv(FILE_NAME, index=False)
+
+# Make program take up full width of screen
+st.set_page_config(page_title='😃 Twitter Sentiment Analysis', layout='wide')
 
+# Define function to display history
+def display_history():
+    df = pd.read_csv(FILE_NAME)
+    st.dataframe(df.style.highlight_max(axis=0))
+
+# Define main function
+def run():
     with st.form(key='Enter name'):
-        search_words = st.text_input('Enter the name for which you want to know the sentiment')
-        number_of_tweets = st.number_input('Enter the number of latest tweets for which you want to know the sentiment(Maximum 50 tweets)', 0,50,10)
+        search_words = st.text_input('Enter a word or phrase you want to know about')
+        number_of_tweets = st.number_input('How many tweets do you want to see? (maximum 50)', 0,50,10)
         submit_button = st.form_submit_button(label='Submit')
+
     if submit_button:
         tweets =tw.Cursor(api.search_tweets,q=search_words,lang="en").items(number_of_tweets)
         tweet_list = [i.text for i in tweets]
         p = [i for i in classifier(tweet_list)]
         q=[p[i]['label'] for i in range(len(p))]
-        df = pd.DataFrame(list(zip(tweet_list, q)),columns =['Latest '+str(number_of_tweets)+' Tweets'+' on '+search_words, 'sentiment'])
-        st.write(df)
+        df = pd.DataFrame(list(zip([search_words], [number_of_tweets], [tweet_list], [pd.Timestamp.now()])),columns = HEADERS)
+        st.write(pd.DataFrame(list(zip(tweet_list, q)),columns =['Latest '+str(number_of_tweets)+' Tweets'+' on '+search_words, 'sentiment']))
+        with open(FILE_NAME, mode='a', newline='') as file:
+            df.to_csv(file, header=False, index=False)
+            
+    if st.button('Clear History'):
+        os.remove(FILE_NAME)
+        st.write('History has been cleared.')
+        
+    if st.button('Display History'):
+        display_history()
 
 if __name__=='__main__':
-    run()
+    run()