Update app.py

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 from datetime import date
 import yfinance as yf
-from prophet import Prophet  # Changed from fbprophet to prophet
+from prophet import Prophet
 from prophet.plot import plot_plotly
 from plotly import graph_objs as go
 
@@ -10,11 +10,11 @@ START = "2015-01-01"
 TODAY = date.today().strftime("%Y-%m-%d")
 
 # Streamlit app title
-st.title('Stock Forecast App')
+st.title('Stock & Cryptocurrency Forecast App')
 
-# Stock selection
-stocks = ('GOOG', 'AAPL', 'MSFT', 'GME')
-selected_stock = st.selectbox('Select dataset for prediction', stocks)
+# Stock and cryptocurrency selection
+assets = ('GOOG', 'AAPL', 'MSFT', 'GME', 'BTC-USD', 'ETH-USD')  # Added BTC and ETH
+selected_asset = st.selectbox('Select dataset for prediction', assets)
 
 # Years of prediction slider
 n_years = st.slider('Years of prediction:', 1, 4)
@@ -22,25 +22,29 @@ period = n_years * 365
 
 @st.cache_data
 def load_data(ticker):
-    """Load stock data from Yahoo Finance."""
+    """Load stock or cryptocurrency data from Yahoo Finance."""
     data = yf.download(ticker, START, TODAY)
     data.reset_index(inplace=True)
     return data
 
 # Load data and show loading state
 data_load_state = st.text('Loading data...')
-data = load_data(selected_stock)
+data = load_data(selected_asset)
 data_load_state.text('Loading data... done!')
 
 # Display raw data
 st.subheader('Raw data')
 st.write(data.tail())
 
+# Ensure 'Close' prices are numeric and handle any missing values
+data['Close'] = pd.to_numeric(data['Close'], errors='coerce')
+data.dropna(subset=['Close'], inplace=True)
+
 # Plot raw data function
 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.add_trace(go.Scatter(x=data['Date'], y=data['Open'], name="Open Price"))
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['Close'], name="Close Price"))
     fig.layout.update(title_text='Time Series Data with Rangeslider', xaxis_rangeslider_visible=True)
     st.plotly_chart(fig)
 
@@ -61,7 +65,7 @@ forecast = m.predict(future)
 
 # Show forecast data and plot forecast
 st.subheader('Forecast data')
-st.write(forecast.tail())
+st.write(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())
 st.write(f'Forecast plot for {n_years} years')
 
 fig1 = plot_plotly(m, forecast)