Update app.py

app.py

@@ -5,6 +5,9 @@ import numpy as np
 import streamlit as st
 import matplotlib.pyplot as plt
 
+# Streamlit UI
+st.title('Simple Linear Regression with TensorFlow')
+
 # Define the model
 model = tf.keras.Sequential([
     tf.keras.layers.Dense(units=1, input_shape=[1])
@@ -17,14 +20,15 @@ model.compile(optimizer='sgd', loss='mse')
 xs = np.array([1.0, 2.0, 3.0, 4.0, 5.0], dtype=float)
 ys = np.array([1.5, 2.0, 2.5, 3.0, 3.5], dtype=float)
 
-# Streamlit UI
-st.title('Simple Linear Regression with TensorFlow')
+# Display example input and output
+st.write("Example input (xs):", xs)
+st.write("Example output (ys):", ys)
 
 # User input for the new value to predict
 input_value = st.number_input('Enter your input value:', value=1.0, format="%.1f")
 
 # User input for epochs
-epochs = st.sidebar.slider("Number of epochs", 10, 100, 10)
+epochs = st.sidebar.slider("Number of epochs", 10, 500, 10)
 
 # Button to train the model and make prediction
 if st.button('Train Model and Predict'):