Update app.py

app.py

@@ -1,51 +1,63 @@
-## importing various libraries
-import streamlit as st 
+import streamlit as st
 import yfinance as yf
-
 from prophet import Prophet
 from prophet.plot import plot_plotly
-from datetime import date
 from plotly import graph_objs as go
+from datetime import date
 
 START = "2015-01-01"
 TODAY = date.today().strftime("%Y-%m-%d")
 
-st.title("Stock Prediction Algorithm")
+st.title('Stock Forecast App')
+stocks = ('GOOG', 'AAPL', 'MSFT', 'GME')
+selected_stock = st.selectbox('Select a stock price for prediction', stocks)
+n_years = st.slider('Years of prediction', 1, 4)
+periods = n_years * 365
 
-stocks = ("AAPL", "GOOG", "MSFT", "GME")
-selected_stocks = st.selectbox("Select dataset for prediction", stocks)
-slider_year = st.slider("Years of Prediction:", 1, 10)
-period = slider_year * 365
 
-@st.cache
+@st.cache_data  # Use st.cache_data instead of st.cache
 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) ## call the function specified abpve
-data_load_state.text("Data has been loaded")
 
-st.subheader('Raw Data')
+data_load_state = st.text('Loading data...')
+data = load_data(selected_stock)
+data_load_state.text('Loading data... done!')
+
+st.subheader('Raw data')
 st.write(data.tail())
 
-def raw_Plot():
+
+# Plot raw data
+def plot_raw_data():
     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 = "Time Series Data", xaxis_rangeslider_visible=True)
+    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='Time Series data with Rangeslider', xaxis_rangeslider_visible=True)
     st.plotly_chart(fig)
 
-raw_Plot()
 
-df_train = data[['Date','Close']]
-df_train = df_train.rename(columns = {"Date": "ds", "Close": "y"})
+plot_raw_data()
+
+# Predict forecast with Prophet.
+df_train = data[['Date', 'Close']]
+df_train = df_train.rename(columns={"Date": "ds", "Close": "y"})
+
+m = Prophet()
+m.fit(df_train)
+future = m.make_future_dataframe(periods=periods)
+forecast = m.predict(future)
+
+# Show and plot forecast
+st.subheader('Forecast data')
+st.write(forecast.tail())
 
-model = Prophet()
-model.fit(df_train)
-futureModel = model.make_future_dataframe(periods=period)
-forecast = model.predict(futureModel)
+st.subheader('Forecast plot for {} years'.format(n_years))
+fig1 = plot_plotly(m, forecast)
+st.plotly_chart(fig1)
 
-st.subheader("Forecasted Data")
-st.write(forecast.tail())
+st.write("Forecast components")
+fig2 = m.plot_components(forecast)
+st.write(fig2)