Update app.py

app.py

@@ -0,0 +1,48 @@
+# app.py
+import gradio as gr
+from evo_transformer import EvoTransformer
+import matplotlib.pyplot as plt
+
+def run_evolution(generations):
+    evo = EvoTransformer()
+    evo.evolve(generations)
+    history = evo.get_history()
+    results = evo.evaluate()
+
+    # Format history for output
+    trait_logs = ""
+    for i, config in enumerate(history):
+        trait_logs += f"Generation {i}: {config}\n"
+
+    # Plot number of attention heads over generations
+    heads = [conf["attention_heads"] for conf in history]
+    layers = [conf["layers"] for conf in history]
+    ffn = [conf["ffn_dim"] for conf in history]
+
+    fig, ax = plt.subplots()
+    ax.plot(range(len(heads)), heads, label="Attention Heads")
+    ax.plot(range(len(layers)), layers, label="Layers")
+    ax.plot(range(len(ffn)), ffn, label="FFN Dim")
+    ax.set_title("Evolution of Traits")
+    ax.set_xlabel("Generation")
+    ax.set_ylabel("Value")
+    ax.legend()
+
+    return trait_logs, results["accuracy"], round(results["params"], 2), fig
+
+# Gradio UI
+demo = gr.Interface(
+    fn=run_evolution,
+    inputs=gr.Slider(minimum=1, maximum=10, step=1, label="Generations"),
+    outputs=[
+        gr.Textbox(label="Evolution History"),
+        gr.Number(label="Simulated Accuracy"),
+        gr.Number(label="Estimated Parameters (M)"),
+        gr.Plot(label="Trait Evolution Plot"),
+    ],
+    title="🧬 EvoTransformer Demo",
+    description="An evolving Transformer that mutates architecture traits during training. Watch the architecture change in real time!"
+)
+
+if __name__ == "__main__":
+    demo.launch()