Update app.py

app.py

@@ -3,8 +3,6 @@ import pandas as pd
 import numpy as np
 from sklearn.linear_model import LinearRegression
 import matplotlib.pyplot as plt
-import random
-import string
 
 # Remove the Streamlit default layout
 st.set_page_config(layout="wide")
@@ -16,14 +14,8 @@ data2 = pd.read_csv('tesla.csv')
 # Combine the datasets into a dictionary
 datasets = {'Google': data1, 'Tesla': data2}
 
-# Generate a random string for unique key generation
-def random_string(length):
-    letters = string.ascii_lowercase
-    return ''.join(random.choice(letters) for i in range(length))
-
 # Get the user's dataset selection
-key = f'dataset_selection_{random_string(5)}'
-selected_dataset = st.selectbox('Select Stock', list(datasets.keys()), key=key)
+selected_dataset = st.selectbox('Select Stock', list(datasets.keys()))
 
 # Retrieve the selected dataset
 selected_data = datasets[selected_dataset]
@@ -36,74 +28,38 @@ y = selected_data['Close']
 model = LinearRegression()
 model.fit(X, y)
 
-# Predict the stock prices
-predictions = model.predict(X)
-
-# Create subplots
-fig, axes = plt.subplots(3, 1, figsize=(10, 15))
-
-# Plot 1: Actual and Predicted Stock Prices
-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()
-
-# Plot 2: Volume of Trades
-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')
-
-# Plot 3: Daily Percentage Change in Stock Prices
-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')
+# Define a flag for displaying the graphs
+display_graphs = False
 
-# Add a submit button
+# Create a submit button
 if st.button('Submit'):
-    # Get the updated dataset selection
-    key = f'dataset_selection_{random_string(5)}'
-    selected_dataset = st.selectbox('Select Stock', list(datasets.keys()), key=key)
-
-    # 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)
+    display_graphs = True
 
-    # Clear the existing plots
-    for ax in axes:
-        ax.clear()
+# Display the graphs if the submit button is clicked
+if display_graphs:
+    # Create subplots
+    fig, axes = plt.subplots(3, 1, figsize=(10, 15))
 
-    # Update the plots with the new dataset
+    # Plot 1: Actual and Predicted Stock Prices
     axes[0].plot(selected_data['Date'], y, label='Actual')
-    axes[0].plot(selected_data['Date'], predictions, label='Predicted')
+    axes[0].plot(selected_data['Date'], model.predict(X), 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()
 
+    # Plot 2: Volume of Trades
     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')
 
+    # Plot 3: Daily Percentage Change in Stock Prices
     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)
+    # Display the graphs in Streamlit
+    st.pyplot(fig)