Update app.py

app.py

@@ -1,4 +1,4 @@
-# A simple Linear Regression example with TensorFlow
+# A simple Linear Regression example for Celsius to Fahrenheit conversion with TensorFlow
 
 import tensorflow as tf
 import numpy as np
@@ -6,7 +6,7 @@ import streamlit as st
 import matplotlib.pyplot as plt
 
 # Streamlit UI
-st.title('Simple Linear Regression with TensorFlow')
+st.title('Celsius to Fahrenheit Conversion with TensorFlow')
 
 # Define the model
 model = tf.keras.Sequential([
@@ -16,37 +16,37 @@ model = tf.keras.Sequential([
 # Compile the model with an optimizer and loss function
 model.compile(optimizer='sgd', loss='mse')
 
-# Training data
-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)
+# 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 input (xs):", xs)
-st.write("Example output (ys):", ys)
+st.write("Example Celsius values (input):", celsius)
+st.write("Corresponding Fahrenheit values (output):", fahrenheit)
 
-# 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 the Celsius value to predict Fahrenheit
+input_celsius = st.number_input('Enter Celsius value:', value=0.0, format="%.1f")
 
 # User input for epochs
 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'):
+if st.button('Train Model and Predict Fahrenheit'):
     with st.spinner('Training...'):
-        model.fit(xs, ys, epochs=epochs)
+        model.fit(celsius, fahrenheit, epochs=epochs)
     st.success('Training completed!')
 
     # Make prediction
-    prediction = model.predict([input_value])
-    st.write(f'For input {input_value}, the prediction is {prediction[0][0]}')
+    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')
 
     # Predictions for visualization
-    predictions = model.predict(xs)
+    predictions = model.predict(celsius)
 
     # Plotting
-    plt.scatter(xs, ys, label='Actual')
-    plt.plot(xs, predictions, color='red', label='Predicted')
-    plt.xlabel('Input Feature')
-    plt.ylabel('Output Value')
+    plt.scatter(celsius, fahrenheit, label='Actual Conversion')
+    plt.plot(celsius, predictions, color='red', label='Predicted Conversion')
+    plt.xlabel('Celsius')
+    plt.ylabel('Fahrenheit')
     plt.legend()
     st.pyplot(plt)