Update app.py

app.py

@@ -1,17 +1,17 @@
 import gradio as gr
-from inference import evo_chat_predict, get_gpt_response, get_model_config, get_system_stats
+from inference import evo_chat_predict, get_gpt_response, get_model_config
 from logger import log_feedback
-import subprocess
-import os
+import subprocess, os
 
 chat_history = []
 
-# 🔁 Handle chat logic
+# 🔁 Core logic
+
 def chat_fn(user_input, option1, option2, user_vote=None):
     global chat_history
 
     if not user_input or not option1 or not option2:
-        return "❗ Please provide a question and two options.", chat_history, "", "", ""
+        return "❗ Provide a question and two options.", chat_history, "", ""
 
     options = [option1.strip(), option2.strip()]
     evo_result = evo_chat_predict(chat_history, user_input, options)
@@ -19,8 +19,9 @@ def chat_fn(user_input, option1, option2, user_vote=None):
 
     evo_msg = (
         f"### 🤖 Evo\n"
-        f"**Answer:** {evo_result['answer']}  \n"
-        f"**Reasoning:** {evo_result['reasoning']}\n\n"
+        f"**Answer:** {evo_result['answer']}\n"
+        f"**Reasoning:** {evo_result['reasoning']}\n"
+        f"**Confidence:** {evo_result['confidence']}\n"
         f"---\n"
         f"### 🧠 GPT-3.5\n"
         f"{gpt_response}"
@@ -30,7 +31,6 @@ def chat_fn(user_input, option1, option2, user_vote=None):
     chat_history.append(f"🤖 Evo: {evo_result['answer']}")
     chat_history.append(f"🧠 GPT: {gpt_response}")
 
-    # Log feedback
     log_feedback(
         question=user_input,
         option1=option1,
@@ -42,17 +42,13 @@ def chat_fn(user_input, option1, option2, user_vote=None):
         user_preference=user_vote
     )
 
-    # 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'}"
+        f"**Layers:** {config['num_layers']} | **Heads:** {config['num_heads']} | "
+        f"**FFN:** {config['ffn_dim']} | **Memory:** {'✅' if config['memory_enabled'] else '❌'}"
     )
 
-    # Load latest genome score
-    log_txt = "No genome log yet."
+    log_txt = "Genome log not found."
     if os.path.exists("genome_log.csv"):
         with open("genome_log.csv", "r", encoding="utf-8") as f:
             lines = f.readlines()
@@ -60,65 +56,54 @@ def chat_fn(user_input, option1, option2, user_vote=None):
                 last = lines[-1].strip().split(",")
                 log_txt = f"🧬 Genome ID: {last[0]} | Accuracy: {last[-1]}"
 
-    # System stats
-    sys = get_system_stats()
-    sys_str = (
-        f"**Device:** {sys['device']} ({sys['platform']})  \n"
-        f"**CPU Usage:** {sys['cpu_usage_percent']}%  \n"
-        f"**RAM:** {sys['memory_used_gb']} GB / {sys['memory_total_gb']} GB  \n"
-        f"**GPU:** {sys['gpu_name']}  \n"
-        f"**GPU Memory:** {sys['gpu_memory_used_gb']} GB / {sys['gpu_memory_total_gb']} GB"
-    )
+    return evo_msg, chat_history, config_str, log_txt
 
-    return evo_msg, chat_history, config_str, log_txt, sys_str
 
-# 🔁 Clear everything
 def clear_fn():
     global chat_history
     chat_history = []
-    return "", "", "", None, [], "", "", ""
+    return "", "", "", None, [], "", ""
+
 
-# 📈 Retrain
 def retrain_model():
     try:
         subprocess.run(["python", "retrain_from_feedback.py"], check=True)
-        return "✅ Evo retrained successfully."
+        return "✅ Evo retrained."
     except Exception as e:
-        return f"❌ Retraining failed: {str(e)}"
+        return f"❌ Retrain error: {str(e)}"
+
 
-# ⬇️ Download feedback
 def export_feedback():
-    if os.path.exists("feedback_log.csv"):
-        return "feedback_log.csv"
-    return None
+    return "feedback_log.csv" if os.path.exists("feedback_log.csv") else 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 and give two options. Evo chooses, explains, and evolves. Compare with GPT-3.5.")
+# 🚀 UI Launch
+with gr.Blocks(title="EvoRAG – Real-Time Reasoning AI") as demo:
+    gr.Markdown("## 🧠 EvoRAG – Real-Time Reasoning AI")
+    gr.Markdown("Built Different. Learns Live. Evolves from You.")
 
     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 – 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")
+            user_vote = gr.Radio(["Evo", "GPT"], label="🗳️ Who was better?", info="Optional – fuels evolution")
+            submit = gr.Button("Ask Evo")
+            clear = gr.Button("Clear")
+            retrain = gr.Button("Retrain Evo")
+            export = gr.Button("Export Feedback")
 
         with gr.Column(scale=6):
             evo_reply = gr.Markdown()
             chat_display = gr.HighlightedText(label="Conversation History")
             model_info = gr.Markdown(label="🧠 Evo Architecture")
-            genome_log = gr.Markdown(label="📊 Evolution Log")
-            sys_stats = gr.Markdown(label="📟 System Stats")
+            genome_log = gr.Markdown(label="📊 Last Evolution")
 
     submit.click(chat_fn, inputs=[user_input, option1, option2, user_vote],
-                 outputs=[evo_reply, chat_display, model_info, genome_log, sys_stats])
-    clear.click(clear_fn, outputs=[user_input, option1, option2, user_vote, chat_display, model_info, genome_log, sys_stats])
+                 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()
+if __name__ == "__main__":
+    demo.launch()