Update app.py

app.py

@@ -1,107 +1,57 @@
 import gradio as gr
 from evo_transformer import EvoTransformer
 from plot import plot_radar_chart
-import tempfile
-import json
-import csv
+import io
 from PIL import Image
 
-# Initialize EvoTransformer
 evo = EvoTransformer()
 
 def evolve_model(generations):
-    evo.reset()  # Reset state
-    evo.run_evolution(generations)
+    evo.reset()
+    evo.evolve(generations)
 
-    # Get final config
-    final_config = evo.history[-1]
-    summary = f"layers: {final_config['layers']}\nattention_heads: {final_config['attention_heads']}\n"
-    summary += f"ffn_dim: {final_config['ffn_dim']}\ndropout: {final_config['dropout']}\n"
-    summary += f"memory: {final_config['memory']}"
-
-    # Simulate results
-    accuracy = round(final_config['layers'] * 1.23 + final_config['dropout'] * 10, 2)
-    params = round(final_config['layers'] * final_config['ffn_dim'] * final_config['attention_heads'] / 1000, 2)
-    accuracy_str = f"{accuracy:.2f}%"
-    params_str = f"{params:.2f}M params"
+    best_config = evo.get_best_config()
+    accuracy = f"{best_config['accuracy']:.2f}%"
+    params = f"{best_config['parameters']:.2f}M params"
+    summary = str(best_config['traits'])
 
     # Radar chart
-    radar_img = plot_radar_chart(final_config)
-
-    # History logs
-    gen_configs = []
-    for i, cfg in enumerate(evo.history):
-        gen_configs.append(f"Gen {i+1} Config: {cfg}")
-
-    # Write CSV
-    csv_file = tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode='w', newline='')
-    writer = csv.DictWriter(csv_file, fieldnames=final_config.keys())
-    writer.writeheader()
-    writer.writerows(evo.history)
-    csv_file.close()
+    radar_img = plot_radar_chart(best_config['traits'])
 
-    # Write JSON
-    json_file = tempfile.NamedTemporaryFile(delete=False, suffix=".json", mode='w')
-    json.dump(evo.history, json_file)
-    json_file.close()
+    # Files
+    csv_file = ("evo_history.csv", io.BytesIO(evo.export_csv().encode()))
+    json_file = ("evo_history.json", io.BytesIO(evo.export_json().encode()))
 
-    # Pad history to 10 generations
-    while len(gen_configs) < 10:
-        gen_configs.append("")
+    # History
+    history_text = [f"Gen {i+1} Config: {c['traits']}" for i, c in enumerate(evo.history)]
 
     return (
-        accuracy_str,             # 1
-        params_str,               # 2
-        summary,                  # 3
-        radar_img,                # 4
-        None,                     # 5 (diagram placeholder)
-        gen_configs[0],           # 6
-        gen_configs[1],           # 7
-        gen_configs[2],           # 8
-        gen_configs[3],           # 9
-        gen_configs[4],           # 10
-        gen_configs[5],           # 11
-        gen_configs[6],           # 12
-        gen_configs[7],           # 13
-        gen_configs[8],           # 14
-        gen_configs[9],           # 15
-        csv_file.name,            # 16
-        json_file.name            # 17
+        accuracy, params, summary,
+        radar_img,
+        *history_text,
+        csv_file, json_file
     )
 
-# Gradio UI
-with gr.Blocks(title="🧬 EvoTransformer – Evolving Transformer Architectures") as demo:
-    gr.Markdown("## 🧬 EvoTransformer – Evolving Transformer Architectures\nSimulate trait mutation and adaptive architecture generation.")
-
-    with gr.Row():
-        generations_input = gr.Slider(minimum=1, maximum=10, step=1, value=3, label="Number of Generations")
-        evolve_btn = gr.Button("🧬 Evolve Architecture")
-
-    with gr.Row():
-        accuracy_output = gr.Textbox(label="Simulated Accuracy")
-        params_output = gr.Textbox(label="Estimated Parameters")
-        summary_output = gr.Textbox(label="Current Config Summary")
+with gr.Blocks(title="EvoTransformer – Evolving Transformer Architectures") as demo:
+    gr.Markdown("🧬 **EvoTransformer – Evolving Transformer Architectures**")
+    gr.Markdown("Simulate trait mutation and adaptive architecture generation.")
 
-    gr.Markdown("## 🧬 Evolution History")
-    radar_output = gr.Image(label="Final Generation Trait Radar", type="pil")
-    diagram_output = gr.Image(label="Illustrative Transformer Structure")  # Not used, just placeholder
+    generations = gr.Slider(minimum=1, maximum=20, step=1, label="Number of Generations", value=5)
+    run_btn = gr.Button("🧬 Evolve Architecture")
 
-    with gr.Accordion("Downloadable Files", open=False):
-        csv_output = gr.File(label="Download CSV History")
-        json_output = gr.File(label="Download JSON History")
+    acc = gr.Textbox(label="Simulated Accuracy")
+    params = gr.Textbox(label="Estimated Parameters")
+    summary = gr.Textbox(label="Current Config Summary")
+    radar = gr.Image(label="Final Generation Trait Radar")
 
-    gen_outputs = [gr.Textbox(label=f"Gen {i+1} Config") for i in range(10)]
+    history_outputs = [gr.Textbox(label=f"Gen {i+1} Config") for i in range(10)]
+    csv_download = gr.File(label="Download CSV History")
+    json_download = gr.File(label="Download JSON History")
 
-    evolve_btn.click(
-        evolve_model,
-        inputs=[generations_input],
-        outputs=[
-            accuracy_output, params_output, summary_output,
-            radar_output, diagram_output,
-            *gen_outputs,
-            csv_output, json_output
-        ]
+    run_btn.click(
+        fn=evolve_model,
+        inputs=[generations],
+        outputs=[acc, params, summary, radar] + history_outputs + [csv_download, json_download]
     )
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()