Update app.py

app.py

@@ -3,16 +3,14 @@ import numpy as np
 from sklearn.linear_model import LinearRegression
 import matplotlib.pyplot as plt
 
-# Define a function for linear regression
+# Function for linear regression
 def perform_linear_regression(X, Y):
     X = np.array(X).reshape(-1, 1)  # Reshape for sklearn
     Y = np.array(Y)
     
-    # Initialize and fit the linear model
     model = LinearRegression()
     model.fit(X, Y)
     
-    # Get the slope and intercept
     slope = model.coef_[0]
     intercept = model.intercept_
     
@@ -23,42 +21,41 @@ st.title("Linear Regression App")
 
 st.subheader("Input vectors X and Y")
 
-# Input fields for vectors X and Y
-x_values = [st.number_input(f"X[{i}]", value=0.0, key=f'x{i}') for i in range(3)]
-y_values = [st.number_input(f"Y[{i}]", value=0.0, key=f'y{i}') for i in range(3)]
+# Create two columns
+col1, col2 = st.columns(2)
+
+# Input fields for vectors X and Y in the first column
+with col1:
+    st.write("Enter values for X and Y:")
+
+    x_values = [st.number_input(f"X[{i}]", value=0.0, key=f'x{i}') for i in range(3)]
+    y_values = [st.number_input(f"Y[{i}]", value=0.0, key=f'y{i}') for i in range(3)]
 
 # Check if both vectors are filled
 if len(x_values) == 3 and len(y_values) == 3:
     slope, intercept = perform_linear_regression(x_values, y_values)
     
-    # Rounded values for display
     slope = round(slope, 2)
     intercept = round(intercept, 2)
-        
-    # 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')
     
-
-
-    # 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')
-
-
-
-    plt.xlabel("X")
-    plt.ylabel("Y")
-    plt.legend()
-    plt.title("Linear Regression Visualization")
-
-    # Display the plot using Streamlit's pyplot function
-    st.pyplot(plt)
-
+    # Display the slope and intercept at the top of the window
+    st.write(f"Slope: {slope}")
+    st.write(f"Intercept: {intercept}")
+    
+    # Display graph in the second column
+    with col2:
+        plt.figure(figsize=(8, 6))
+        plt.scatter(x_values, y_values, color='red', label='Data Points')
+        
+        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')
+        
+        plt.xlabel("X")
+        plt.ylabel("Y")
+        plt.legend()
+        plt.title("Linear Regression Visualization")
+        
+        st.pyplot(plt)
 else:
     st.write("Please provide 3 values for both vectors X and Y.")