Create Prompt2Example.py

pages/Prompt2Example.py

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+import streamlit as st
+import tensorflow as tf
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Generate synthetic data
+def generate_data(num_points=1000):
+    np.random.seed(0)
+    X = np.random.randn(num_points, 2)
+    y = (X[:, 0]**2 + X[:, 1]**2 < 1).astype(int)
+    return X, y
+
+# Create the model
+def create_model(input_shape, learning_rate, activation, hidden_layers):
+    model = tf.keras.Sequential()
+    model.add(tf.keras.layers.InputLayer(input_shape=input_shape))
+    for neurons in hidden_layers:
+        model.add(tf.keras.layers.Dense(neurons, activation=activation))
+    model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
+    
+    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),
+                  loss='binary_crossentropy',
+                  metrics=['accuracy'])
+    return model
+
+# Plot decision boundary
+def plot_decision_boundary(model, X, y):
+    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
+    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
+    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
+                         np.arange(y_min, y_max, 0.1))
+    grid = np.c_[xx.ravel(), yy.ravel()]
+    probs = model.predict(grid).reshape(xx.shape)
+    
+    plt.contourf(xx, yy, probs, alpha=0.8)
+    plt.scatter(X[:, 0], X[:, 1], c=y, edgecolor='k', marker='o')
+    st.pyplot(plt.gcf())
+
+def main():
+    st.title("Interactive Neural Network Training")
+    
+    # Sidebar inputs
+    learning_rate = st.sidebar.slider("Learning rate", 0.001, 0.1, 0.03)
+    activation = st.sidebar.selectbox("Activation function", ["relu", "tanh", "sigmoid"])
+    num_hidden_layers = st.sidebar.slider("Number of hidden layers", 1, 5, 2)
+    neurons_per_layer = st.sidebar.slider("Neurons per layer", 1, 10, 4)
+    batch_size = st.sidebar.slider("Batch size", 1, 100, 10)
+    num_epochs = st.sidebar.slider("Number of epochs", 1, 1000, 100)
+    
+    hidden_layers = [neurons_per_layer] * num_hidden_layers
+    
+    X, y = generate_data()
+    
+    model = create_model(input_shape=(2,), learning_rate=learning_rate, activation=activation, hidden_layers=hidden_layers)
+    model.fit(X, y, epochs=num_epochs, batch_size=batch_size, validation_split=0.5, verbose=0)
+    
+    st.write("Training complete")
+    
+    fig, ax = plt.subplots()
+    plot_decision_boundary(model, X, y)
+    st.pyplot(fig)
+
+if __name__ == "__main__":
+    main()