Create app.py

app.py

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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)