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# A simple Linear Regression example for Celsius to Fahrenheit conversion with TensorFlow
import tensorflow as tf
import numpy as np
import streamlit as st
import matplotlib.pyplot as plt
# Streamlit UI
st.title('Celsius to Fahrenheit Conversion with TensorFlow')
# Define the model with a different weight initializer
model = tf.keras.Sequential([
tf.keras.layers.Dense(units=1, input_shape=[1], kernel_initializer='he_normal')
])
# Set a suitable learning rate and number of epochs
learning_rate = 0.01
epochs = 500
# Compile the model with the Adam optimizer and loss function
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
model.compile(optimizer=optimizer, loss='mse')
# Training data (Celsius to Fahrenheit)
celsius = np.array([-40, -10, 0, 8, 15, 22, 38], dtype=float)
fahrenheit = np.array([-40, 14, 32, 46.4, 59, 71.6, 100.4], dtype=float)
# User input for the Celsius value to predict Fahrenheit
input_celsius = st.number_input('Enter Celsius value:', value=0.0, format="%.1f")
# Button to train the model and make prediction
if st.button('Train Model and Predict Fahrenheit'):
with st.spinner('Training...'):
# Fit the model
model.fit(celsius, fahrenheit, epochs=epochs, batch_size=4)
st.success('Training completed!')
# Make prediction
predicted_fahrenheit = model.predict([input_celsius])[0][0]
actual_fahrenheit = input_celsius * 9/5 + 32
st.write(f'For input of {input_celsius}°C, the predicted Fahrenheit value is {predicted_fahrenheit:.1f}°F')
st.write(f'Actual Fahrenheit value (by formula) is {actual_fahrenheit:.1f}°F')
# Predictions for visualization
predictions = model.predict(celsius)
# Plotting
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)
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