added ford and tesla

app.py

@@ -14,12 +14,15 @@ from plotly.subplots import make_subplots
 
 # Read CSV file into pandas and extract timestamp data
 # dfSentiment = ### YOUR LINE OF CODE HERE
-dfSentiment = pd.read_csv('sentiment_data.csv')
+dfSentiment = pd.read_csv('final_sentiment_data.csv')
 dfSentiment['timestamp'] = [datetime.strptime(dt, '%Y-%m-%d') for dt in dfSentiment['timestamp'].tolist()]
 
 # Multi-select columns to build chart
 col_list = dfSentiment.columns.tolist()
 
+r_ticker = re.compile(".*ticker")
+ticker_cols = list(['FORD', 'TSLA'])
+
 
 r_sentiment = re.compile(".*sentiment")
 sentiment_cols = list(filter(r_sentiment.match, col_list))
@@ -40,18 +43,24 @@ volume_list = list(filter(r_volume.match, col_list))
 sentiment_cols = sentiment_cols + post_list
 stocks_cols = close_list + volume_list
 
+
+
+
 # Config for page
 st.set_page_config(
-    page_title= 'TSLA Bot',
-    page_icon='✅',
+    page_title= 'TSLA Stock Sentiment Analyzer with Reddit comments',
+    page_icon='🚙',
     layout='wide',
 )
 
 with st.sidebar:
     # FourthBrain logo to sidebar
     fourthbrain_logo = Image.open('./images/fourthbrain_logo.png')
-    st.image([fourthbrain_logo], width=300)
+    st.image([fourthbrain_logo], width=200)
 
+    # Types of Stock
+    stock_ticker_select = st.selectbox('Select Stock Ticker', ticker_cols)
+    
     # Date selection filters
     start_date_filter = st.date_input(
         'Start Date',
@@ -73,14 +82,21 @@ with st.sidebar:
 
 # Banner with TSLA and Reddit images
 tsla_logo = Image.open('./images/tsla_logo.png')
+ford_logo = Image.open('./images/ford_logo.png')
 reddit_logo = Image.open('./images/reddit_logo.png')
-st.image([tsla_logo, reddit_logo], width=200)
+
+spotlight_logo = tsla_logo if stock_ticker_select == 'TSLA' else ford_logo
+
+st.image([spotlight_logo, reddit_logo], width=150)
 
 # dashboard title
-st.title('TSLA Subreddit and Stock Price')
+st.title(stock_ticker_select + ' Subreddit and Stock Price')
 
 ## dataframe filter
 # start date
+
+dfSentiment = dfSentiment[dfSentiment['stock_ticker'] == stock_ticker_select] 
+
 dfSentiment = dfSentiment[dfSentiment['timestamp'] >= datetime(start_date_filter.year, start_date_filter.month, start_date_filter.day)]
     
 # end date
@@ -200,4 +216,4 @@ for i in range(1, len(dfSentiment)-1):
 
         st.markdown('### Detailed Data View')
         st.dataframe(dfSentiment.iloc[:, 1:][0:i])
-        time.sleep(1)
+        time.sleep(1)

final_sentiment_data.csv

@@ -0,0 +1,12 @@
+,timestamp,counter,close,volume,sentiment_score,close_lag1,perc_change_close,sentiment_score_lag1,perc_change_sentiment,sentiment_SMA3mo,stock_ticker
+0,2022-03-01,298908,1099.56995,40225400,0.8757717551892263,932.0,0.1797960836909871,0.8744244989083738,0.0015407348290612,0.0,TSLA
+1,2022-04-01,204020,1145.44995,45377900,0.8448942852492379,1099.56995,0.0417254036453068,0.8757717551892263,-0.0352574398032701,0.8650301797822794,TSLA
+2,2022-05-01,394149,952.62,48324400,0.8563334928811902,1145.44995,-0.1683442825240858,0.8448942852492379,0.0135392176650571,0.8589998444398849,TSLA
+3,2022-06-01,78141,775.0,40931000,0.858559500975687,952.62,-0.186454199995801,0.8563334928811902,0.0025994640090594,0.8532624263687051,TSLA
+4,2022-07-01,56180,752.28998,33343700,0.8514925640708996,775.0,-0.0293032516129032,0.858559500975687,-0.0082311556703483,0.8554618526425922,TSLA
+0,2022-02-01,388531,1.74,90500,0.8581291195395944,0.0,inf,0.8901475716809757,-0.0359698247346975,0.0,FORD
+1,2022-03-01,596183,1.75,83500,0.8644258568745963,1.74,0.0057471264367816,0.8581291195395944,0.0073377504522632,0.8709008493650554,FORD
+2,2022-04-01,121680,1.92,79700,0.8672561851831583,1.75,0.0971428571428571,0.8644258568745963,0.0032742291152595,0.8632703871991163,FORD
+3,2022-05-01,148176,1.63,8700,0.8847598667655673,1.92,-0.1510416666666666,0.8672561851831583,0.0201828270371024,0.8721473029411073,FORD
+4,2022-06-01,56784,1.62,9600,0.8769357754633977,1.63,-0.0061349693251532,0.8847598667655673,-0.0088431806143877,0.8763172758040411,FORD
+5,2022-07-01,1620,1.61,9900,0.8834256099330055,1.62,-0.0061728395061728,0.8769357754633977,0.0074005812639794,0.8817070840539901,FORD