Update app.py

app.py

@@ -18,7 +18,6 @@ def perform_linear_regression(X, Y):
 
 # Streamlit interface
 st.title("Linear Regression App")
-
 st.subheader("Input vectors X and Y")
 
 # Create two columns
@@ -26,22 +25,27 @@ 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_input = st.text_input("X values (comma-separated)", "0.0, 0.0, 0.0")
+    y_input = st.text_input("Y values (comma-separated)", "0.0, 0.0, 0.0")
 
-    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)]
+# Parse the input strings into lists of floats
+try:
+    x_values = [float(val.strip()) for val in x_input.split(",")]
+    y_values = [float(val.strip()) for val in y_input.split(",")]
+except ValueError:
+    st.write("Please provide valid numeric inputs.")
+    x_values, y_values = [], []
 
-# Check if both vectors are filled
+# Proceed only if both vectors are valid and have 3 elements each
 if len(x_values) == 3 and len(y_values) == 3:
     slope, intercept = perform_linear_regression(x_values, y_values)
     
     slope = round(slope, 2)
     intercept = round(intercept, 2)
-    
-    # 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))
@@ -58,4 +62,4 @@ if len(x_values) == 3 and len(y_values) == 3:
         
         st.pyplot(plt)
 else:
-    st.write("Please provide 3 values for both vectors X and Y.")
+    st.write("Please provide valid inputs with 3 values each.")