Update app.py

app.py

@@ -8,14 +8,25 @@ if not os.path.exists("trained_model/config.json"):
 else:
     print("✅ EvoTransformer already initialized.")
 
-    
 import gradio as gr
 import random
-
 from inference import generate_response
 from logger import log_user_feedback
 from dashboard import update_dashboard_plot
 from watchdog import retrain_model
+from init_model import load_model
+
+# === Load model to extract architecture
+model = load_model()
+
+def get_architecture_summary(model):
+    summary = {
+        "Layers": getattr(model, "num_layers", "N/A"),
+        "Attention Heads": getattr(model, "num_heads", "N/A"),
+        "FFN Dim": getattr(model, "ffn_dim", "N/A"),
+        "Memory Enabled": getattr(model, "use_memory", "N/A"),
+    }
+    return "\n".join(f"{k}: {v}" for k, v in summary.items())
 
 # 🎲 Random examples
 examples = [
@@ -120,5 +131,8 @@ with gr.Blocks(title="EvoTransformer v2.1 – Compare Options and Learn") as dem
 
     retrain_button.click(fn=retrain_model, inputs=[], outputs=[retrain_status])
 
+    with gr.Accordion("🧬 EvoTransformer Architecture", open=False):
+        arch_box = gr.Textbox(label="Model Configuration", value=get_architecture_summary(model), lines=5, interactive=False)
+
 if __name__ == "__main__":
     demo.launch(share=True)