Update app.py

app.py

@@ -13,17 +13,19 @@ model = tf.keras.Sequential([
     tf.keras.layers.Dense(units=1, input_shape=[1])
 ])
 
+# Optimizer selection
+optimizer = st.sidebar.selectbox(
+    "Select optimizer",
+    ('sgd', 'adam', 'rmsprop')
+)
+
 # Compile the model with an optimizer and loss function
-model.compile(optimizer='sgd', loss='mse')
+model.compile(optimizer=optimizer, loss='mse')
 
 # Training data (Celsius to Fahrenheit)
 celsius = np.array([-40, -10, 0, 8, 15, 22, 38], dtype=float)  # Celsius
 fahrenheit = np.array([-40, 14, 32, 46.4, 59, 71.6, 100.4], dtype=float)  # Corresponding Fahrenheit
 
-# Display example input and output
-st.write("Example Celsius values (input):", celsius)
-st.write("Corresponding Fahrenheit values (output):", fahrenheit)
-
 # User input for the Celsius value to predict Fahrenheit
 input_celsius = st.number_input('Enter Celsius value:', value=0.0, format="%.1f")
 
@@ -37,8 +39,8 @@ if st.button('Train Model and Predict Fahrenheit'):
     st.success('Training completed!')
 
     # Make prediction
-    predicted_fahrenheit = model.predict([input_celsius])
-    st.write(f'For input of {input_celsius}°C, the predicted Fahrenheit value is {predicted_fahrenheit[0][0]:.1f}°F')
+    predicted_fahrenheit = model.predict([input_celsius])[0][0]
+    st.write(f'For input of {input_celsius}°C, the predicted Fahrenheit value is {predicted_fahrenheit:.1f}°F')
 
     # Predictions for visualization
     predictions = model.predict(celsius)