Update app.py

app.py

@@ -1,100 +1,113 @@
 import gradio as gr
-from inference import evo_chat_predict, get_gpt_response
+from inference import evo_chat_predict, get_gpt_response, get_model_config
 from logger import log_feedback
 import subprocess
+import os
 
-# Global chat history
 chat_history = []
 
-# 🧠 Handle main chat logic
+# 🔁 Handle chat logic
 def chat_fn(user_input, option1, option2, user_vote=None):
     global chat_history
 
-    # Validate
     if not user_input or not option1 or not option2:
-        return "❗ Please enter your question and both options.", chat_history
+        return "❗ Please provide a question and two options.", chat_history, "", ""
 
     options = [option1.strip(), option2.strip()]
-
-    # Evo prediction
     evo_result = evo_chat_predict(chat_history, user_input, options)
-
-    # GPT fallback (background comparison)
     gpt_response = get_gpt_response(user_input)
 
-    # Format response
     evo_msg = (
-    f"### 🤖 Evo\n"
-    f"**Answer:** {evo_result['answer']}  \n"
-    f"**Reasoning:** {evo_result['reasoning']}\n\n"
-    f"---\n"
-    f"### 🧠 GPT-3.5\n"
-    f"{gpt_response}"
-)
+        f"### 🤖 Evo\n"
+        f"**Answer:** {evo_result['answer']}  \n"
+        f"**Reasoning:** {evo_result['reasoning']}\n\n"
+        f"---\n"
+        f"### 🧠 GPT-3.5\n"
+        f"{gpt_response}"
+    )
 
     chat_history.append(f"👤 User: {user_input}")
-    chat_history.append(f"🤖 Evo: {evo_msg}")
+    chat_history.append(f"🤖 Evo: {evo_result['answer']}")
     chat_history.append(f"🧠 GPT: {gpt_response}")
 
-    # Logging for Evo retraining
+    # Log feedback
     log_feedback(
         question=user_input,
         option1=option1,
         option2=option2,
-        context=evo_result['context_used'],
-        evo_output=evo_result['answer'],
+        context=evo_result["context_used"],
+        evo_output=evo_result["answer"],
         gpt_output=gpt_response,
-        evo_reasoning=evo_result['reasoning'],
+        evo_reasoning=evo_result["reasoning"],
         user_preference=user_vote
     )
 
-    return evo_msg, chat_history
+    # Show genome config
+    config = get_model_config()
+    config_str = (
+        f"**Layers:** {config['num_layers']}  \n"
+        f"**Heads:** {config['num_heads']}  \n"
+        f"**FFN Dim:** {config['ffn_dim']}  \n"
+        f"**Memory:** {'Enabled' if config['memory_enabled'] else 'Disabled'}"
+    )
 
-# 🔁 Clear chat state
+    # Load latest genome score
+    log_txt = "No genome log yet."
+    if os.path.exists("genome_log.csv"):
+        with open("genome_log.csv", "r", encoding="utf-8") as f:
+            lines = f.readlines()
+            if len(lines) > 1:
+                last = lines[-1].strip().split(",")
+                log_txt = f"🧬 Genome ID: {last[0]} | Accuracy: {last[-1]}"
+
+    return evo_msg, chat_history, config_str, log_txt
+
+# 🔁 Clear everything
 def clear_fn():
     global chat_history
     chat_history = []
-    return "", "", "", None, []
+    return "", "", "", None, [], "", ""
 
-# 📈 Live retrain
+# 📈 Retrain
 def retrain_model():
     try:
-        result = subprocess.run(
-            ["python", "retrain_from_feedback.py"],
-            check=True,
-            capture_output=True,
-            text=True
-        )
-        print(result.stdout)  # Log to terminal
+        subprocess.run(["python", "retrain_from_feedback.py"], check=True)
         return "✅ Evo retrained successfully."
-    except subprocess.CalledProcessError as e:
-        print("STDOUT:", e.stdout)
-        print("STDERR:", e.stderr)
-        return f"❌ Retraining failed:\n{e.stderr}"
+    except Exception as e:
+        return f"❌ Retraining failed: {str(e)}"
 
+# ⬇️ Download feedback
+def export_feedback():
+    if os.path.exists("feedback_log.csv"):
+        return "feedback_log.csv"
+    return None
 
 # 🌐 Gradio UI
 with gr.Blocks(title="EvoRAG – Real-Time Adaptive Reasoning AI") as demo:
     gr.Markdown("## 🧬 EvoRAG – Real-Time Adaptive Reasoning AI")
-    gr.Markdown("Ask Evo a question, give two options. Evo chooses with reasoning. Compare with GPT. Feedback fuels evolution.")
+    gr.Markdown("Ask Evo a question and give two options. Evo chooses, explains, and evolves. Compare with GPT-3.5.")
 
     with gr.Row():
         with gr.Column(scale=4):
             user_input = gr.Textbox(label="Your Question", lines=2)
             option1 = gr.Textbox(label="Option 1")
             option2 = gr.Textbox(label="Option 2")
-            user_vote = gr.Radio(["Evo", "GPT"], label="🗳️ Who gave the better answer?", info="Optional – improves Evo.")
+            user_vote = gr.Radio(["Evo", "GPT"], label="🗳️ Who gave the better answer?", info="Optional – helps Evo learn.")
             submit = gr.Button("🧠 Ask Evo")
             clear = gr.Button("🔁 Clear")
             retrain = gr.Button("📈 Retrain Evo from Feedback")
+            export = gr.Button("⬇️ Export Feedback CSV")
 
         with gr.Column(scale=6):
             evo_reply = gr.Markdown()
             chat_display = gr.HighlightedText(label="Conversation History")
-
-    submit.click(fn=chat_fn, inputs=[user_input, option1, option2, user_vote],
-                 outputs=[evo_reply, chat_display])
-    clear.click(fn=clear_fn, outputs=[user_input, option1, option2, user_vote, chat_display])
-    retrain.click(fn=retrain_model, outputs=evo_reply)
+            model_info = gr.Markdown(label="🧠 Evo Architecture")
+            genome_log = gr.Markdown(label="📊 Evolution Log")
+
+    submit.click(chat_fn, inputs=[user_input, option1, option2, user_vote],
+                 outputs=[evo_reply, chat_display, model_info, genome_log])
+    clear.click(clear_fn, outputs=[user_input, option1, option2, user_vote, chat_display, model_info, genome_log])
+    retrain.click(retrain_model, outputs=evo_reply)
+    export.click(export_feedback, outputs=[])
 
 demo.launch()