Create app.py

app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Include your SNN code here, for brevity I'll only show the Gradio interface part.
+
+def run_snn(X, epochs, batch_size, l2_lambda, patience):
+    # Your SNN initialization and training code here
+    snn = SwarmNeuralNetwork(layer_sizes=[1, 32, 16, 8, 1],
+                             fractal_methods=[sierpinski_fractal, mandelbrot_fractal, julia_fractal, julia_fractal])
+    snn.train(X, y, epochs=epochs, batch_size=batch_size, l2_lambda=l2_lambda, patience=patience)
+    y_pred = snn.forward(X, training=False)
+    fractal_outputs = snn.apply_fractals(X)
+    return y_pred, fractal_outputs
+
+def plot_results(X, y, y_pred, fractal_outputs):
+    fig, axs = plt.subplots(2, 2, figsize=(15, 10))
+    
+    axs[0, 0].plot(X, y, label='True')
+    axs[0, 0].plot(X, y_pred, label='Predicted')
+    axs[0, 0].legend()
+    axs[0, 0].set_title('True vs Predicted')
+    
+    x, y = fractal_outputs[0]
+    axs[0, 1].plot(x, y)
+    axs[0, 1].set_title('Sierpinski Fractal Output')
+    
+    axs[1, 0].plot(X, fractal_outputs[1])
+    axs[1, 0].set_title('Mandelbrot Fractal Output')
+    
+    axs[1, 1].plot(X, fractal_outputs[2])
+    axs[1, 1].set_title('Julia Fractal Output')
+
+    return fig
+
+def main_interface(epochs, batch_size, l2_lambda, patience):
+    X = np.linspace(0, 10, 1000).reshape(-1, 1)
+    y = np.sin(X).reshape(-1, 1)
+    X = (X - X.min()) / (X.max() - X.min())
+    y = (y - y.min()) / (y.max() - y.min())
+    
+    y_pred, fractal_outputs = run_snn(X, epochs, batch_size, l2_lambda, patience)
+    fig = plot_results(X, y, y_pred, fractal_outputs)
+    
+    return fig
+
+gr.Interface(
+    fn=main_interface,
+    inputs=[
+        gr.inputs.Slider(1, 10000, default=5000, label="Epochs"),
+        gr.inputs.Slider(1, 100, default=32, label="Batch Size"),
+        gr.inputs.Slider(0.0001, 0.1, default=0.00001, label="L2 Lambda"),
+        gr.inputs.Slider(1, 1000, default=50, label="Patience")
+    ],
+    outputs=gr.outputs.Plot()
+).launch()