Update app.py

app.py

@@ -1,115 +1,75 @@
 import gradio as gr
-from evo_transformer import EvoTransformer
-import pandas as pd
 import json
-import tempfile
-import matplotlib.pyplot as plt
-import numpy as np
-
-model = EvoTransformer()
-
-def plot_radar(history):
-    traits = ["layers", "attention_heads", "ffn_dim", "dropout"]
-    labels = traits
-    last = history[-1]
-    values = [
-        last["layers"] / 12,
-        last["attention_heads"] / 12,
-        last["ffn_dim"] / 2048,
-        last["dropout"] / 0.5,
-    ]
-    angles = np.linspace(0, 2 * np.pi, len(traits), endpoint=False).tolist()
-    values += values[:1]
-    angles += angles[:1]
-
-    fig, ax = plt.subplots(figsize=(5,5), subplot_kw=dict(polar=True))
-    ax.plot(angles, values, "o-", linewidth=2)
-    ax.fill(angles, values, alpha=0.25)
-    ax.set_thetagrids(np.degrees(angles[:-1]), labels)
-    ax.set_title("Final Generation Trait Radar")
-    ax.grid(True)
-
-    img_path = tempfile.NamedTemporaryFile(suffix=".png", delete=False).name
-    plt.savefig(img_path)
-    plt.close(fig)
-    return img_path
-
-def evolve_and_display(gens):
-    try:
-        model.evolve(generations=gens)
-        history = model.get_history()
-        eval_result = model.evaluate()
-
-        summary = "\n".join([f"{k}: {v}" for k, v in history[-1].items()])
-        history_txt = [json.dumps(h, indent=2) for h in history]
-        while len(history_txt) < 10:
-            history_txt.append("")
-
-        # Save CSV and JSON
-        df = pd.DataFrame(history)
-        csv_path = tempfile.NamedTemporaryFile(suffix=".csv", delete=False).name
-        json_path = tempfile.NamedTemporaryFile(suffix=".json", delete=False).name
-        df.to_csv(csv_path, index=False)
-        with open(json_path, "w") as jf:
-            json.dump(history, jf, indent=2)
-
-        radar_img = plot_radar(history)
-
-        return (
-            f"{eval_result['accuracy']*100:.2f}%",
-            f"{eval_result['params']:.2f}M params",
-            summary,
-            *history_txt,
-            radar_img,
-            csv_path,
-            json_path,
-        )
-    except Exception as e:
-        print("🚨 ERROR during evolution:", str(e))
-        import traceback
-        traceback.print_exc()
-
-        return (
-            "Error", "Error", "Error",
-            *["Error"] * 10,
-            None, None, None
-        )
+import pandas as pd
+from evo_transformer import EvoTransformer
+from plots import plot_radar
+from diagrams import draw_transformer_diagram
+
+# Initialize EvoTransformer
+evo = EvoTransformer()
+
+def evolve_transformer(generations):
+    evo = EvoTransformer()
+    evo.evolve(generations)
+    history = evo.get_history()
+    final_config = history[-1]
+    eval_result = evo.evaluate()
+
+    radar_path = plot_radar(final_config)
+    diagram_path = draw_transformer_diagram()
+
+    csv_path = "history.csv"
+    json_path = "history.json"
+    pd.DataFrame(history).to_csv(csv_path, index=False)
+    with open(json_path, "w") as f:
+        json.dump(history, f, indent=2)
+
+    # Format config display
+    summary = "\n".join(f"{k}: {v}" for k, v in final_config.items())
+
+    return (
+        f"{eval_result['accuracy']*100:.2f}%",
+        f"{eval_result['params']:.2f}M params",
+        summary,
+        radar_path,
+        diagram_path,
+        csv_path,
+        json_path
+    )
 
-with gr.Blocks() as demo:
-    gr.Markdown("# 🧬 EvoTransformer – Evolving Transformer Architectures")
-    gr.Markdown("Simulate trait mutation and adaptive architecture generation.")
+# Gradio Interface
+with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
+    gr.Markdown("""
+    # 🧬 EvoTransformer – Evolving Transformer Architectures
+    Simulate trait mutation and adaptive architecture generation.
+    """)
 
     with gr.Row():
-        generations = gr.Slider(1, 10, value=3, step=1, label="Number of Generations")
-        evolve_btn = gr.Button("🧬 Evolve Architecture")
+        gens = gr.Slider(1, 10, value=3, step=1, label="Number of Generations")
+        evolve_btn = gr.Button("\ud83e\uddec Evolve Architecture")
 
     with gr.Row():
-        acc_out = gr.Text(label="Simulated Accuracy")
-        param_out = gr.Text(label="Estimated Parameters")
+        acc_out = gr.Textbox(label="Simulated Accuracy")
+        param_out = gr.Textbox(label="Estimated Parameters")
 
     summary_out = gr.Textbox(label="Current Config Summary", lines=5)
 
-    with gr.Accordion("🧬 Evolution History", open=False):
-        hist_outputs = [gr.Textbox(label=f"Gen {i+1} Config", lines=4) for i in range(10)]
+    gr.Markdown("""
+    ## 🧬 Evolution History
+    """)
 
-    radar_plot = gr.Image(label="Final Generation Trait Radar")
+    radar_img = gr.Image(label="Final Generation Trait Radar")
+    diagram_img = gr.Image(label="Transformer Architecture")
 
     with gr.Row():
-        csv_out = gr.File(label="Download CSV History")
-        json_out = gr.File(label="Download JSON History")
+        csv_dl = gr.File(label="Download CSV History")
+        json_dl = gr.File(label="Download JSON History")
 
     evolve_btn.click(
-        evolve_and_display,
-        inputs=[generations],
-        outputs=[
-            acc_out,
-            param_out,
-            summary_out,
-            *hist_outputs,
-            radar_plot,
-            csv_out,
-            json_out
-        ],
+        fn=evolve_transformer,
+        inputs=[gens],
+        outputs=[acc_out, param_out, summary_out, radar_img, diagram_img, csv_dl, json_dl]
     )
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()