Update app.py

app.py

@@ -37,9 +37,13 @@ data_load_state.text('Loading data... done!')
 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)
+# Check if 'Close' column exists before converting
+if 'Close' in data.columns:
+    # 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)
+else:
+    st.error("The 'Close' column is missing from the data. Please check the selected asset.")
 
 # Plot raw data function
 def plot_raw_data():
@@ -49,40 +53,44 @@ def plot_raw_data():
     fig.layout.update(title_text='Time Series Data with Rangeslider', xaxis_rangeslider_visible=True)
     st.plotly_chart(fig)
 
-# Call the plotting function
-plot_raw_data()
+# Call the plotting function if there's enough data
+if not data.empty:
+    plot_raw_data()
 
-# Prepare data for Prophet model
-df_train = data[['Date', 'Close']]
-df_train = df_train.rename(columns={"Date": "ds", "Close": "y"})
+    # Prepare data for Prophet model
+    df_train = data[['Date', 'Close']]
+    df_train = df_train.rename(columns={"Date": "ds", "Close": "y"})
 
-# Create and fit the Prophet model
-m = Prophet()
-m.fit(df_train)
+    # Create and fit the Prophet model
+    m = Prophet()
+    m.fit(df_train)
 
-# Create future dataframe and make predictions
-future = m.make_future_dataframe(periods=period)
-forecast = m.predict(future)
+    # Create future dataframe and make predictions
+    future = m.make_future_dataframe(periods=period)
+    forecast = m.predict(future)
 
-# Show forecast data and plot forecast
-st.subheader('Forecast Data')
-st.write(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())
-st.write(f'Forecast plot for the next {n_years} years')
+    # Show forecast data and plot forecast
+    st.subheader('Forecast Data')
+    st.write(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())
+    st.write(f'Forecast plot for the next {n_years} years')
 
-fig1 = plot_plotly(m, forecast)
-st.plotly_chart(fig1)
+    fig1 = plot_plotly(m, forecast)
+    st.plotly_chart(fig1)
 
-# Show forecast components
-st.subheader("Forecast Components")
-fig2 = m.plot_components(forecast)
-st.plotly_chart(fig2)
+    # Show forecast components
+    st.subheader("Forecast Components")
+    fig2 = m.plot_components(forecast)
+    st.plotly_chart(fig2)
 
-# Additional Insights: Displaying key metrics
-st.subheader("Key Metrics")
-latest_data = forecast.iloc[-1]
-st.write(f"Predicted Price: ${latest_data['yhat']:.2f}")
-st.write(f"Lower Bound: ${latest_data['yhat_lower']:.2f}")
-st.write(f"Upper Bound: ${latest_data['yhat_upper']:.2f}")
+    # Additional Insights: Displaying key metrics
+    st.subheader("Key Metrics")
+    latest_data = forecast.iloc[-1]
+    st.write(f"Predicted Price: ${latest_data['yhat']:.2f}")
+    st.write(f"Lower Bound: ${latest_data['yhat_lower']:.2f}")
+    st.write(f"Upper Bound: ${latest_data['yhat_upper']:.2f}")
+
+else:
+    st.error("No valid data available to make predictions.")
 
 # User Guidance Section
 st.sidebar.header("User Guidance")