Update app.py

app.py

@@ -12,81 +12,8 @@ from transformers import pipeline
 import torch
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-# App title
-st.markdown('''
-# Sovrenn Market Sentiment Indicator App
-Shown are the stock price data for the selected company!
-**Credits**
-- App built by SRL
-''')
-st.write('---')
-
-# Sidebar
-st.sidebar.subheader('Query parameters')
-start_date = st.sidebar.date_input("Start date", datetime.date(2023,9, 20))
-#start_date = start_date - datetime.timedelta(days=1) 
-end_date = start_date + datetime.timedelta(days=14)
-
-# User input for the stock ticker symbol
-tickerSymbol = st.sidebar.text_input('Enter Stock Ticker Symbol')
-
-if tickerSymbol:
-    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
-
-    string_name = tickerData.info.get('longName', 'Company Name Not Available')
-    st.header('**%s**' % string_name)
-
-    # Try to get the business summary, handle KeyError if not available
-    try:
-        string_summary = tickerData.info['longBusinessSummary']
-        st.info(string_summary)
-    except KeyError:
-        st.warning("Business summary not available for this company.")
-
-    # Ticker data
-    st.header('**Ticker data**')
-    st.write(tickerDf)
-
-
-
-    # Create a candlestick chart and volume bar chart
-    fig_candlestick = go.Figure(data=[go.Candlestick(x=tickerDf.index,
-                                                     open=tickerDf['Open'],
-                                                     high=tickerDf['High'],
-                                                     low=tickerDf['Low'],
-                                                     close=tickerDf['Close'])])
-
-    fig_volume = go.Figure(data=[go.Bar(x=tickerDf.index, y=tickerDf['Volume'])])
-
-    st.header('**Candlestick Chart**')
-    st.plotly_chart(fig_candlestick)
-
-    st.header('**Volume Bar Chart**')
-    st.plotly_chart(fig_volume)
-    
-    
-    st.write(start_date)
-    tickerDf = pd.DataFrame(tickerDf).reset_index()
-
-#    st.write(tickerDf)
-
-    #date = datetime.date(start_date)
-    date_str = start_date.strftime("%Y-%m-%d")
-    st.write(date_str)
-    df = tickerDf[tickerDf["Date"]==date_str]
-    st.write(df)
-
-    if (df["Close"][0] > df["Open"][0] ):
-        st.write("NSE has uptrend on " +date_str )
-    if (df["Close"][0] < df["Open"][0] ):
-        st.write(" NSE has downdtrend on " +date_str )
-
-
-
-
-
 
+def scrapper (start_date):
     d0 = start_date
     d1 = datetime.date(2008, 1, 1)
     delta = d0 - d1
@@ -113,10 +40,91 @@ if tickerSymbol:
                 headlines.append(target_element.text)
             except:
                 continue
+    return headlines
 
 
+# App title
+st.markdown('''
+# Sovrenn Market Sentiment Indicator App
+Shown are the stock price data for the selected company!
+**Credits**
+- App built by SRL
+''')
+st.write('---')
+
+# Sidebar
+st.sidebar.subheader('Query parameters')
+start_date = st.sidebar.date_input("Start date", datetime.date(2023,9, 20))
+#start_date = start_date - datetime.timedelta(days=1) 
+end_date = start_date + datetime.timedelta(days=14)
+
+# User input for the stock ticker symbol
+tickerSymbol = st.sidebar.text_input('Enter Stock Ticker Symbol')
 
+if tickerSymbol:
+    try:
+        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
+    
+        string_name = tickerData.info.get('longName', 'Company Name Not Available')
+        st.header('**%s**' % string_name)
+    
+        # Try to get the business summary, handle KeyError if not available
+        try:
+            string_summary = tickerData.info['longBusinessSummary']
+            st.info(string_summary)
+        except KeyError:
+            st.warning("Business summary not available for this company.")
+    
+        # Ticker data
+        st.header('**Ticker data**')
+        st.write(tickerDf)
+    
+    
+    
+        # Create a candlestick chart and volume bar chart
+        fig_candlestick = go.Figure(data=[go.Candlestick(x=tickerDf.index,
+                                                         open=tickerDf['Open'],
+                                                         high=tickerDf['High'],
+                                                         low=tickerDf['Low'],
+                                                         close=tickerDf['Close'])])
+    
+        fig_volume = go.Figure(data=[go.Bar(x=tickerDf.index, y=tickerDf['Volume'])])
+    
+        st.header('**Candlestick Chart**')
+        st.plotly_chart(fig_candlestick)
+    
+        st.header('**Volume Bar Chart**')
+        st.plotly_chart(fig_volume)
+        
+        
+        st.write(start_date)
+        tickerDf = pd.DataFrame(tickerDf).reset_index()
+    
+    #    st.write(tickerDf)
+    
+        #date = datetime.date(start_date)
+        date_str = start_date.strftime("%Y-%m-%d")
+        st.write(date_str)
+        df = tickerDf[tickerDf["Date"]==date_str]
+        st.write(df)
+    
+        if (df["Close"][0] > df["Open"][0] ):
+            st.write("NSE has uptrend on " +date_str )
+        if (df["Close"][0] < df["Open"][0] ):
+            st.write(" NSE has downdtrend on " +date_str )
+    except:
+        st.write("Stock Data of this Index on the selected Date is not available \n")
+
+
+
+
+
+
+
+    
 
+    headlines = scrapper(start_date)
     index = [idx for idx, s in enumerate(headlines) if s=='Most Read' ][0]
     del headlines[index:]
     news = pd.DataFrame({"News": headlines})