Update app.py

app.py

@@ -1,80 +1,80 @@
-#importing libraries 
-import streamlit as st
-import yfinance as yf 
-from datetime import date
-from prophet import Prophet 
-from prophet.plot import plot_plotly
-from plotly import graph_objs as go
-
-
-#main function
-def main():
-    START="2017-01-01"
-    TODAY=date.today().strftime("%Y-%m-%d")
-
-    st.title("Stock Forecast App")
-    st.write("**Disclaimer:** The stock price predictions generated by this app should not be considered financial advice. Always consult with a qualified financial advisor before making investment decisions.")
-
-    stocks= ("MSFT","AAPL","GOOG")
-
-    st.write("""
-             ***Stock Tickers:***
-             - AAPL : Apple Inc.
-             - MSFT : Microsoft Corporation.
-             - GOOG : Alphabet Inc.(Google)
-             """)
-    
-    selected_stocks=st.selectbox('select dataset for prediction',stocks)
-    n_year=st.slider("**Select Year of prediction**",1,4)
-    period=n_year * 365
-
-    #download Dataset 
-    @st.cache_data
-    def load_data(ticker):
-        data=yf.download(ticker,START,TODAY)
-        data.reset_index(inplace=True)
-        return data
-    
-    data_load_state=st.text('Loading data..')
-    data=load_data(selected_stocks)
-    data_load_state.text("Done!!")
-
-    st.subheader("Raw data")
-    st.write(data.tail())
-
-    #plot the Raw Data
-    def plot_rawdata():
-        fig=go.Figure()
-        fig.add_trace(go.Scatter(x=data['Date'], y=data['Open'],name="stock_open"))
-        fig.add_trace(go.Scatter(x=data['Date'], y=data['Close'],name="stock_close"))
-        fig.layout.update(title_text="Forecast Data")
-        fig.update_xaxes(rangeslider_visible=True)
-        st.plotly_chart(fig)
-
-    plot_rawdata()
-
-    #Forecasting 
-
-    df_train=data[["Date",'Close']]
-    df_train=df_train.rename(columns={'Date':'ds','Close':'y'})
-
-    model=Prophet()
-    model.fit(df_train)
-
-    future=model.make_future_dataframe(periods=period)
-    forecast=model.predict(future)
-
-    #show and plot the feature
-    st.subheader("Forecasted dataset")
-    st.write(forecast.tail(5))
-
-    st.write("Forecast plot")
-    fig1=plot_plotly(model,forecast)
-    st.plotly_chart(fig1)
-
-    st.write("Forecast components")
-    fig2=model.plot_components(forecast)
-    st.write(fig2)
-
-if __name__=="__main__":
+#importing libraries 
+import streamlit as st
+import yfinance as yf
+from datetime import date
+from prophet import Prophet 
+from prophet.plot import plot_plotly
+from plotly import graph_objs as go
+
+
+#main function
+def main():
+    START="2017-01-01"
+    TODAY=date.today().strftime("%Y-%m-%d")
+
+    st.title("Stock Forecast App")
+    st.write("**Disclaimer:** The stock price predictions generated by this app should not be considered financial advice. Always consult with a qualified financial advisor before making investment decisions.")
+
+    stocks= ("MSFT","AAPL","GOOG")
+
+    st.write("""
+             ***Stock Tickers:***
+             - AAPL : Apple Inc.
+             - MSFT : Microsoft Corporation.
+             - GOOG : Alphabet Inc.(Google)
+             """)
+    
+    selected_stocks=st.selectbox('select dataset for prediction',stocks)
+    n_year=st.slider("**Select Year of prediction**",1,4)
+    period=n_year * 365
+
+    #download Dataset 
+    @st.cache_data
+    def load_data(ticker):
+        data=yf.download(ticker,START,TODAY)
+        data.reset_index(inplace=True)
+        return data
+    
+    data_load_state=st.text('Loading data..')
+    data=load_data(selected_stocks)
+    data_load_state.text("Done!!")
+
+    st.subheader("Raw data")
+    st.write(data.tail())
+
+    #plot the Raw Data
+    def plot_rawdata():
+        fig=go.Figure()
+        fig.add_trace(go.Scatter(x=data['Date'], y=data['Open'],name="stock_open"))
+        fig.add_trace(go.Scatter(x=data['Date'], y=data['Close'],name="stock_close"))
+        fig.layout.update(title_text="Forecast Data")
+        fig.update_xaxes(rangeslider_visible=True)
+        st.plotly_chart(fig)
+
+    plot_rawdata()
+
+    #Forecasting 
+
+    df_train=data[["Date",'Close']]
+    df_train=df_train.rename(columns={'Date':'ds','Close':'y'})
+
+    model=Prophet()
+    model.fit(df_train)
+
+    future=model.make_future_dataframe(periods=period)
+    forecast=model.predict(future)
+
+    #show and plot the feature
+    st.subheader("Forecasted dataset")
+    st.write(forecast.tail(5))
+
+    st.write("Forecast plot")
+    fig1=plot_plotly(model,forecast)
+    st.plotly_chart(fig1)
+
+    st.write("Forecast components")
+    fig2=model.plot_components(forecast)
+    st.write(fig2)
+
+if __name__=="__main__":
     main()