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
 from logger import log_feedback
-import subprocess, os
+import subprocess
+import os
 
 chat_history = []
 
-# 🔁 Core logic
-
+# 🔁 Handle chat 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 "❗ Provide a question and two options.", chat_history, "", ""
+        return "❗ Please provide a question and two options.", chat_history, "", "", ""
 
     options = [option1.strip(), option2.strip()]
     evo_result = evo_chat_predict(chat_history, user_input, options)
@@ -19,9 +19,8 @@ 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"
-        f"**Confidence:** {evo_result['confidence']}\n"
+        f"**Answer:** {evo_result['answer']}  \n"
+        f"**Reasoning:** {evo_result['reasoning']}\n\n"
         f"---\n"
         f"### 🧠 GPT-3.5\n"
         f"{gpt_response}"
@@ -31,6 +30,7 @@ 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,
@@ -42,13 +42,17 @@ 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']} | **Heads:** {config['num_heads']} | "
-        f"**FFN:** {config['ffn_dim']} | **Memory:** {'✅' if config['memory_enabled'] else '❌'}"
+        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'}"
     )
 
-    log_txt = "Genome log not found."
+    # 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()
@@ -56,55 +60,75 @@ 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]}"
 
-    return evo_msg, chat_history, config_str, log_txt
-
+    return evo_msg, chat_history, config_str, log_txt, why_evo_panel()
+
+# 🧠 Static "Why Evo?" panel
+def why_evo_panel():
+    return (
+        "### 🚀 Why Evo?\n"
+        "- Learns from your input — evolves in real time\n"
+        "- Adaptive architecture (changes #layers, memory, etc.)\n"
+        "- Tiny model (~13M–28M params) vs GPT-3.5 (175B)\n"
+        "- Runs on CPU or low-end GPUs\n"
+        "- Transparent architecture: shows how it thinks\n"
+        "- Can be deployed, fine-tuned, and evolved per user/domain"
+    )
 
+# 🔁 Clear everything
 def clear_fn():
     global chat_history
     chat_history = []
     return "", "", "", None, [], "", ""
 
-
+# 📈 Retrain
 def retrain_model():
     try:
         subprocess.run(["python", "retrain_from_feedback.py"], check=True)
-        return "✅ Evo retrained."
+        return "✅ Evo retrained successfully."
     except Exception as e:
-        return f"❌ Retrain error: {str(e)}"
-
+        return f"❌ Retraining failed: {str(e)}"
 
+# ⬇️ Download feedback
 def export_feedback():
-    return "feedback_log.csv" if os.path.exists("feedback_log.csv") else None
-
-
-# 🚀 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.")
+    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", css="body { font-family: 'Segoe UI', sans-serif; background-color: #f8f9fa; }") as demo:
     with gr.Row():
-        with gr.Column(scale=4):
-            user_input = gr.Textbox(label="Your Question", lines=2)
+        with gr.Column(scale=2):
+            gr.Markdown("""
+                # 🧠 EvoRAG
+                ### Built Different. Learns Live. Evolves from You.
+            """)
+            gr.Markdown(why_evo_panel(), elem_id="why-evo")
+
+        with gr.Column(scale=5):
+            user_input = gr.Textbox(label="Your Question", lines=2, placeholder="e.g. What should you do if there's a fire?")
             option1 = gr.Textbox(label="Option 1")
             option2 = gr.Textbox(label="Option 2")
             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.Row():
+                submit = gr.Button("🧠 Ask Evo")
+                clear = gr.Button("🔁 Clear")
+            with gr.Row():
+                retrain = gr.Button("📈 Retrain Evo")
+                export = gr.Button("⬇️ Export Feedback CSV")
 
+    with gr.Row():
         with gr.Column(scale=6):
             evo_reply = gr.Markdown()
             chat_display = gr.HighlightedText(label="Conversation History")
+        with gr.Column(scale=4):
             model_info = gr.Markdown(label="🧠 Evo Architecture")
-            genome_log = gr.Markdown(label="📊 Last Evolution")
+            genome_log = gr.Markdown(label="📊 Evolution Log")
+            evo_why = gr.Markdown(label="🔬 Why Evo?")
 
     submit.click(chat_fn, inputs=[user_input, option1, option2, user_vote],
-                 outputs=[evo_reply, chat_display, model_info, genome_log])
+                 outputs=[evo_reply, chat_display, model_info, genome_log, evo_why])
     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=[gr.File(label="📥 Download Feedback CSV")])
-
+    export.click(export_feedback, outputs=[])
 
-if __name__ == "__main__":
-    demo.launch()
+    demo.launch()