Update app.py

app.py

@@ -109,43 +109,58 @@ demo.launch(server_name="0.0.0.0", server_port=7860, share=True)'''
 
 import gradio as gr
 import torch
+import time
 import os
 from inference import load_model_and_tokenizer, infer
 from logger import log_feedback
 from retrain_from_feedback import train_evo
 from datetime import datetime
 
-# Load initial model
-model, tokenizer = load_model_and_tokenizer("trained_model/evo_retrained.pt")
+# Globals
+model, tokenizer = load_model_and_tokenizer()
 
-# Reload helper
+# Helper to reload model
 def reload_model():
     global model, tokenizer
-    model, tokenizer = load_model_and_tokenizer("trained_model/evo_retrained.pt")
+    model, tokenizer = load_model_and_tokenizer()
 
-# Get last updated timestamp
+# Get last update time
 def get_last_update():
     if os.path.exists("last_updated.txt"):
         with open("last_updated.txt") as f:
             return f.read().strip()
     return "Never"
 
-# Inference logic
+# Summarize Evo architecture
+def get_model_summary():
+    num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+    summary = f"""
+    • 🔢 Parameters: {num_params:,}
+    • 🧱 Layers: 6 TransformerEncoder
+    • 🎯 Attention Heads: 8
+    • 🧠 FFN Dim: 1024
+    • 🧬 Memory Module: Enabled
+    • ⚙️ Pooling: AdaptiveAvgPool1d
+    • 🧾 Classifier: Linear(512 → 1)
+    """
+    return summary.strip()
+
+# Handle inference
 def compare(question, option1, option2, context):
-    evo_ans, evo_score, evo_reason, evo_ctx = infer(model, tokenizer, question, [option1, option2], context)
+    evo_ans, evo_score, evo_reason, evo_ctx = infer(question, [option1, option2], context)
     gpt_ans = "Coming soon via API"
     return (
         f"Answer: {evo_ans} (Confidence: {evo_score:.2f})\n\nReasoning: {evo_reason}\n\nContext used: {evo_ctx}",
         gpt_ans
     )
 
-# Feedback handler
+# Handle feedback
 def handle_feedback(feedback_text, question, option1, option2, context, evo_output):
     evo_was_correct = feedback_text.strip().lower() == "👍 evo was correct. retrain from this."
     log_feedback(question, option1, option2, context, evo_output, evo_was_correct)
     return "✅ Feedback logged and Evo will improve."
 
-# Retrain handler
+# Manual retrain
 def manual_retrain():
     try:
         train_evo()
@@ -153,13 +168,12 @@ def manual_retrain():
         ts = datetime.utcnow().strftime("%Y-%m-%d %H:%M GMT")
         with open("last_updated.txt", "w") as f:
             f.write(ts)
-        return f"✅ Evo successfully evolved and reloaded at {ts}", ts
+        return f"✅ Evo successfully evolved! Reloaded at {ts}"
     except Exception as e:
-        return f"❌ Retraining failed: {str(e)}", get_last_update()
+        return f"❌ Retraining failed: {str(e)}"
 
-# Gradio UI
 with gr.Blocks(title="EvoRAG – Adaptive Reasoning AI", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("""
+    gr.Markdown(f"""
     # EvoRAG – Adaptive Reasoning AI
     **What is Evo?**  
     EvoTransformer is a lightweight, evolving neural network with ~28M parameters.  
@@ -175,6 +189,12 @@ with gr.Blocks(title="EvoRAG – Adaptive Reasoning AI", theme=gr.themes.Soft())
     **Token limit:** 128  
     **Benchmark:** PIQA, HellaSwag, ARC  
     **Version:** Evo v2.2 (Memory + Web Retrieval + Feedback Learning)  
+    **🕒 Last Evolution:** {get_last_update()}
+    """)
+
+    gr.Markdown(f"""
+    ## 🧠 EvoTransformer Architecture Summary
+    {get_model_summary()}
     """)
 
     with gr.Row():
@@ -205,16 +225,15 @@ with gr.Blocks(title="EvoRAG – Adaptive Reasoning AI", theme=gr.themes.Soft())
         retrain_btn = gr.Button("🔄 Retrain Evo Now")
         retrain_status = gr.Textbox(label="Retraining Status")
 
-    last_update = gr.Textbox(label="🕒 Last Evolution Timestamp", value=get_last_update(), interactive=False)
-
     hall = gr.Markdown("""
     ## 🏆 Evo Hall of Fame (Top Reasoning Entries)
     *(Coming soon)*
     """)
 
     run_btn.click(fn=compare, inputs=[question, option1, option2, context], outputs=[evo_out, gpt_out])
-    submit_btn.click(fn=handle_feedback, inputs=[feedback_dropdown, question, option1, option2, context, evo_out], outputs=feedback_status)
-    retrain_btn.click(fn=manual_retrain, outputs=[retrain_status, last_update])
+    submit_btn.click(fn=lambda fb, q, o1, o2, ctx, eo: handle_feedback(fb, q, o1, o2, ctx, eo),
+                     inputs=[feedback_dropdown, question, option1, option2, context, evo_out],
+                     outputs=feedback_status)
+    retrain_btn.click(fn=manual_retrain, outputs=retrain_status)
 
 demo.launch()
-