Update app.py

app.py

@@ -1,50 +1,45 @@
 import gradio as gr
 from evo_transformer import EvoTransformer
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
 
-# Initialize global EvoTransformer object
-evo = EvoTransformer()
-
-# Define Gradio functions
-def evolve_and_display(generations):
-    evo.evolve(generations)
-    latest_config = evo.config
-    history = evo.get_history()
-    evaluation = evo.evaluate()
-
-    history_table = "\n".join(
-        [f"Gen {i}: {cfg}" for i, cfg in enumerate(history[-generations:])]
-    )
-
+def simulate_evolution(generations):
+    model = EvoTransformer()
+    model.evolve(generations)
+    history = model.get_history()
+    
+    # Evaluation of final model
+    final_result = model.evaluate()
+    
+    # Visualization
+    fig, ax = plt.subplots(figsize=(10, 5))
+    df = pd.DataFrame(history)
+    sns.lineplot(data=df.drop("memory", axis=1), markers=True, ax=ax)
+    ax.set_title("EvoTransformer Trait Evolution")
+    ax.set_ylabel("Value")
+    ax.set_xlabel("Generation")
+    plt.xticks(range(len(df)))
+    plt.tight_layout()
+    
     return (
-        f"### Final Evolved Configuration\n"
-        f"**Layers**: {latest_config['layers']}  \n"
-        f"**Attention Heads**: {latest_config['attention_heads']}  \n"
-        f"**FFN Dimension**: {latest_config['ffn_dim']}  \n"
-        f"**Dropout**: {latest_config['dropout']}  \n"
-        f"**Memory**: {latest_config['memory']}  \n\n"
-        f"### Evaluation\n"
-        f"**Simulated Accuracy**: {evaluation['accuracy']}  \n"
-        f"**Estimated Parameters**: {evaluation['params']}M  \n\n"
-        f"### Trait History (last {generations} generations)\n"
-        f"```\n{history_table}\n```"
+        final_result["accuracy"],
+        round(final_result["params"], 2),
+        fig
     )
 
-# Build Gradio Interface
-with gr.Blocks(title="EvoTransformer Demo") as demo:
-    gr.Markdown("# 🧬 EvoTransformer")
-    gr.Markdown("Simulate in-training architectural evolution of a Transformer model.")
-    
-    with gr.Row():
-        generations_slider = gr.Slider(1, 10, value=3, label="Generations to Evolve")
-        evolve_btn = gr.Button("Evolve")
-    
-    output_box = gr.Markdown()
+# UI
+with gr.Blocks(title="EvoTransformer: Adaptive Architecture Evolution") as demo:
+    gr.Markdown("# 🧬 EvoTransformer Demo")
+    gr.Markdown("Evolving architecture live — inspired by nature. Tune the number of generations below and visualize how traits change during evolution.")
 
-    evolve_btn.click(
-        evolve_and_display,
-        inputs=[generations_slider],
-        outputs=[output_box],
-    )
+    generations = gr.Slider(1, 20, value=5, step=1, label="Number of Generations")
+    btn = gr.Button("Evolve EvoTransformer")
+
+    accuracy = gr.Textbox(label="Estimated Accuracy", interactive=False)
+    params = gr.Textbox(label="Estimated Parameter Count (M)", interactive=False)
+    plot = gr.Plot(label="Architecture Trait Evolution")
+
+    btn.click(fn=simulate_evolution, inputs=generations, outputs=[accuracy, params, plot])
 
-# Launch the app
 demo.launch()