Update app.py

app.py

@@ -1,21 +1,52 @@
 import streamlit as st
-from transformers import pipeline
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
+import yfinance as yf
+import pandas as pd
+import cufflinks as cf
 import datetime
-import streamlit as st
 
-#d = st.date_input("Chose the Date for which you are interested in", value=None)
-#st.write('You selected:', d)
+# App title
+st.markdown('''
+# Stock Price App
+Shown are the stock price data for query companies!
+
+**Credits**
+- App built by [Chanin Nantasenamat](https://medium.com/@chanin.nantasenamat) (aka [Data Professor](http://youtube.com/dataprofessor))
+- Built in `Python` using `streamlit`,`yfinance`, `cufflinks`, `pandas` and `datetime`
+''')
+st.write('---')
+
+# Sidebar
+st.sidebar.subheader('Query parameters')
+start_date = st.sidebar.date_input("Start date", datetime.date(2019, 1, 1))
+end_date = st.sidebar.date_input("End date", datetime.date(2021, 1, 31))
+
+# Retrieving tickers data
+ticker_list = pd.read_csv('https://raw.githubusercontent.com/dataprofessor/s-and-p-500-companies/master/data/constituents_symbols.txt')
+tickerSymbol = st.sidebar.selectbox('Stock ticker', ticker_list) # Select ticker symbol
+tickerData = yf.Ticker(tickerSymbol) # Get ticker data
+tickerDf = tickerData.history(period='1d', start=start_date, end=end_date) #get the historical prices for this ticker
+
+# Ticker information
+string_logo = '<img src=%s>' % tickerData.info['logo_url']
+st.markdown(string_logo, unsafe_allow_html=True)
+
+string_name = tickerData.info['longName']
+st.header('**%s**' % string_name)
 
-tokenizer = AutoTokenizer.from_pretrained("nickmuchi/sec-bert-finetuned-finance-classification")
-model = AutoModelForSequenceClassification.from_pretrained("nickmuchi/sec-bert-finetuned-finance-classification")
+string_summary = tickerData.info['longBusinessSummary']
+st.info(string_summary)
 
+# Ticker data
+st.header('**Ticker data**')
+st.write(tickerDf)
 
-pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=device)
-text = st.text_area("Enter some text")
+# Bollinger bands
+st.header('**Bollinger Bands**')
+qf=cf.QuantFig(tickerDf,title='First Quant Figure',legend='top',name='GS')
+qf.add_bollinger_bands()
+fig = qf.iplot(asFigure=True)
+st.plotly_chart(fig)
 
-if text:
-    out = pipe(text)
-    st.json(out)
+####
+#st.write('---')
+#st.write(tickerData.info)