Update app.py

app.py

@@ -15,7 +15,7 @@ data2 = pd.read_csv('tesla.csv')
 datasets = {'Google': data1, 'Tesla': data2}
 
 # Get the user's dataset selection
-selected_dataset = st.selectbox('Select Stock', list(datasets.keys()))
+selected_dataset = st.selectbox('Select Dataset', list(datasets.keys()))
 
 # Retrieve the selected dataset
 selected_data = datasets[selected_dataset]
@@ -55,5 +55,46 @@ axes[2].set_xlabel('Date')
 axes[2].set_ylabel('Percentage Change')
 axes[2].set_title(f'{selected_dataset} Daily Percentage Change in Stock Prices')
 
+# Add a submit button
+if st.button('Submit'):
+    # Get the updated dataset selection
+    selected_dataset = st.selectbox('Select Dataset', list(datasets.keys()))
+
+    # Retrieve the updated dataset
+    selected_data = datasets[selected_dataset]
+
+    # Prepare the data for prediction
+    X = np.arange(len(selected_data)).reshape(-1, 1)
+    y = selected_data['Close']
+
+    # Train the linear regression model
+    model.fit(X, y)
+
+    # Predict the stock prices
+    predictions = model.predict(X)
+
+    # Clear the existing plots
+    for ax in axes:
+        ax.clear()
+
+    # Update the plots with the new dataset
+    axes[0].plot(selected_data['Date'], y, label='Actual')
+    axes[0].plot(selected_data['Date'], predictions, label='Predicted')
+    axes[0].set_xlabel('Date')
+    axes[0].set_ylabel('Stock Price')
+    axes[0].set_title(f'{selected_dataset} Stock Price Prediction')
+    axes[0].legend()
+
+    axes[1].plot(selected_data['Date'], selected_data['Volume'])
+    axes[1].set_xlabel('Date')
+    axes[1].set_ylabel('Volume')
+    axes[1].set_title(f'{selected_dataset} Volume of Trades')
+
+    daily_returns = selected_data['Close'].pct_change() * 100
+    axes[2].plot(selected_data['Date'], daily_returns)
+    axes[2].set_xlabel('Date')
+    axes[2].set_ylabel('Percentage Change')
+    axes[2].set_title(f'{selected_dataset} Daily Percentage Change in Stock Prices')
+
 # Display the graphs in Streamlit
 st.pyplot(fig)