Update app.py

app.py

@@ -279,12 +279,14 @@ def fetch_news(company_name):
         st.error(f"Error fetching news: {str(e)}")
         return []
 
-def get_table_download_link(df):
+def get_table_download_link(df, filename, text):
+    """Generates a link to download the given dataframe as a CSV file."""
     csv = df.to_csv(index=False)
     b64 = base64.b64encode(csv.encode()).decode()
-    href = f'<a href="data:file/csv;base64,{b64}" download="stock_data.csv">Download CSV File</a>'
+    href = f'<a href="data:file/csv;base64,{b64}" download="{filename}">{text}</a>'
     return href
 
+
 def main():
     st.set_page_config(page_title="Stock Price Predictor", layout="wide")
     st.title("Advanced Stock Price Predictor using Prophet")
@@ -316,7 +318,13 @@ def test_model():
                 st.subheader("Stock Data Information")
                 st.write(data.info())
                 st.write(data.describe())
-                st.dataframe(data.head())
+                
+                # Display interactive dataframe
+                st.subheader("Stock Data Preview")
+                st.dataframe(data.head(100), use_container_width=True)
+                
+                # Provide download link for full dataset
+                st.markdown(get_table_download_link(data, f"{ticker}_stock_data.csv", "Download full stock data CSV"), unsafe_allow_html=True)
 
                 split_index = int(len(data) * (1 - test_split))
                 train_data = data.iloc[:split_index]
@@ -344,7 +352,12 @@ def test_model():
                             # Cross-validation results
                             st.subheader("Cross-Validation Results")
                             cv_results = predictor.cross_validate_model()
-                            st.dataframe(cv_results)
+                            
+                            # Display interactive dataframe
+                            st.dataframe(cv_results, use_container_width=True)
+                            
+                            # Provide download link for cross-validation results
+                            st.markdown(get_table_download_link(cv_results, f"{ticker}_cv_results.csv", "Download cross-validation results CSV"), unsafe_allow_html=True)
 
                             # Calculate and display average metrics
                             avg_mse = cv_results['mse'].mean()
@@ -355,12 +368,19 @@ def test_model():
                             st.write(f"Average RMSE: {avg_rmse:.4f}")
                             st.write(f"Average MAPE: {avg_mape:.4f}")
 
-                            # Feature importance
-                            st.subheader("Feature Importance")
-                            feature_importance = pd.DataFrame(predictor.model.params['regressor_coefficients'].items(), columns=['Feature', 'Importance'])
-                            feature_importance = feature_importance.sort_values('Importance', ascending=False)
-                            fig = px.bar(feature_importance, x='Feature', y='Importance', title='Feature Importance')
-                            st.plotly_chart(fig, use_container_width=True)
+                            # Display predictions
+                            st.subheader("Predictions")
+                            predictions_df = pd.DataFrame({
+                                'Date': test_pred['ds'],
+                                'Predicted': test_pred['yhat'],
+                                'Lower Bound': test_pred['yhat_lower'],
+                                'Upper Bound': test_pred['yhat_upper']
+                            })
+                            st.dataframe(predictions_df, use_container_width=True)
+                            
+                            # Provide download link for predictions
+                            st.markdown(get_table_download_link(predictions_df, f"{ticker}_predictions.csv", "Download predictions CSV"), unsafe_allow_html=True)
+
                         else:
                             st.error("Failed to evaluate the model. The evaluation metrics are None.")
                     else:
@@ -368,7 +388,7 @@ def test_model():
                 else:
                     st.error("Failed to train the Prophet model. Please try a different dataset.")
             else:
-                st.error("Failed to fetch stock data. Please try again.")
+                st.error("Failed to fetch stock data. Please try again."
 
 def predict_stock_prices():
     st.header("Predict Stock Prices with Enhanced Model")