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# A simple Linear Regression example with TensorFlow
import tensorflow as tf
import numpy as np
import streamlit as st
import matplotlib.pyplot as plt
# Define the model
model = tf.keras.Sequential([
tf.keras.layers.Dense(units=1, input_shape=[1])
])
# 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)
# Streamlit UI
st.title('Simple Linear Regression with TensorFlow')
# 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)
# Button to train the model and make prediction
if st.button('Train Model and Predict'):
with st.spinner('Training...'):
model.fit(xs, ys, 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]}')
# Predictions for visualization
predictions = model.predict(xs)
# Plotting
plt.scatter(xs, ys, label='Actual')
plt.plot(xs, predictions, color='red', label='Predicted')
plt.xlabel('Input Feature')
plt.ylabel('Output Value')
plt.legend()
st.pyplot(plt)
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