Update app.py

app.py

@@ -1,6 +1,7 @@
 import streamlit as st
 import numpy as np
 from sklearn.linear_model import LinearRegression
+import matplotlib.pyplot as plt
 
 # Define a function for linear regression
 def perform_linear_regression(X, Y):
@@ -34,8 +35,26 @@ if len(x_values) == 3 and len(y_values) == 3:
     slope = round(slope, 2)
     intercept = round(intercept, 2)
 
-    # Display the results with custom styling
-    st.markdown(f"<span style='font-family: Times New Roman; font-size: 16px; color: blue;'>Slope: {slope}</span>", unsafe_allow_html=True)
-    st.markdown(f"<span style='font-family: Times New Roman; font-size: 16px; color: blue;'>Intercept: {intercept}</span>", unsafe_allow_html=True)
+    # Create a scatter plot of the data points
+    plt.figure(figsize=(8, 6))
+    plt.scatter(x_values, y_values, color='red', label='Data Points')
+
+    # Plot the regression line
+    x_range = np.linspace(min(x_values), max(x_values), 100)
+    y_range = slope * x_range + intercept
+    plt.plot(x_range, y_range, color='blue', label='Regression Line')
+
+    # Add labels for slope and intercept
+    plt.text(min(x_values), min(y_range), f"Slope: {slope}", fontsize=12, color='blue')
+    plt.text(min(x_values), min(y_range) - 0.5, f"Intercept: {intercept}", fontsize=12, color='blue')
+
+    plt.xlabel("X")
+    plt.ylabel("Y")
+    plt.legend()
+    plt.title("Linear Regression Visualization")
+
+    # Display the plot using Streamlit's pyplot function
+    st.pyplot(plt)
+
 else:
     st.write("Please provide 3 values for both vectors X and Y.")