Update app.py

app.py

@@ -39,9 +39,19 @@ st.write(data.tail())
 
 # Check if 'Close' column exists before converting
 if 'Close' in data.columns:
+    # Check data types of the DataFrame
+    st.write("Data Types:")
+    st.write(data.dtypes)
+
+    # Display unique values in the 'Close' column for debugging
+    st.write("Unique values in 'Close' column:")
+    st.write(data['Close'].unique())
+
     # Ensure 'Close' prices are numeric and handle any missing values
     try:
+        # Convert to numeric with coercion for invalid parsing
         data['Close'] = pd.to_numeric(data['Close'], errors='coerce')
+        # Drop rows with NaN values in 'Close'
         data.dropna(subset=['Close'], inplace=True)
     except Exception as e:
         st.error(f"Error converting 'Close' prices to numeric: {e}")
@@ -67,31 +77,36 @@ if not data.empty and 'Close' in data.columns:
 
     # 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)
-
-    # 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)
-
-    # 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}")
+    
+    try:
+        m.fit(df_train)
+        
+        # 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')
+
+        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)
+
+        # 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}")
+
+    except Exception as e:
+        st.error(f"Error fitting model: {e}")
 
 else:
     st.error("No valid data available to make predictions.")