Update app.py

app.py

@@ -1,134 +1,90 @@
 import gradio as gr
-from inference import evo_chat_predict, get_gpt_response, get_model_config
-from logger import log_feedback
-import subprocess
 import os
+from inference import evo_infer, gpt_infer
+from retrain_from_feedback import train_evo
+from logger import log_feedback
 
-chat_history = []
-
-# 🔁 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 "❗ Please provide a question and two options.", chat_history, "", "", ""
-
-    options = [option1.strip(), option2.strip()]
-    evo_result = evo_chat_predict(chat_history, user_input, options)
-    gpt_response = get_gpt_response(user_input)
-
-    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}"
-    )
-
-    chat_history.append(f"👤 User: {user_input}")
-    chat_history.append(f"🤖 Evo: {evo_result['answer']}")
-    chat_history.append(f"🧠 GPT: {gpt_response}")
-
-    # Log feedback
-    log_feedback(
-        question=user_input,
-        option1=option1,
-        option2=option2,
-        context=evo_result["context_used"],
-        evo_output=evo_result["answer"],
-        gpt_output=gpt_response,
-        evo_reasoning=evo_result["reasoning"],
-        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'}"
-    )
-
-    # 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, 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 successfully."
-    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
+question = gr.Textbox(label="🧠 Your Question", placeholder="e.g. What should you do if there's a fire?", lines=1)
+option1 = gr.Textbox(label="🅰️ Option 1", placeholder="Enter the first option")
+option2 = gr.Textbox(label="🅱️ Option 2", placeholder="Enter the second option")
+choice = gr.Radio(["Evo", "GPT"], label="🗳️ Who was better?", info="Optional – fuels evolution", type="value")
+
+evo_out = gr.Textbox(label="🤖 Evo", interactive=False)
+gpt_out = gr.Textbox(label="🧠 GPT-3.5", interactive=False)
+history = gr.Textbox(label="📜 Conversation History", interactive=False)
+evo_stats = gr.Textbox(label="📊 Evo Stats", value="Layers: 6 | Heads: 8 | FFN: 1024 | Memory: ✅ | Accuracy: ~64.5% | Phase: v2.2", interactive=False)
+
+def run_inference(q, o1, o2, winner):
+    evo_answer, evo_reasoning = evo_infer(q, o1, o2)
+    gpt_answer = gpt_infer(q, o1, o2)
+    context = f"Question: {q}\nOptions: {o1}, {o2}"
+    log_feedback(q, o1, o2, context, evo_answer, gpt_answer, evo_reasoning, winner)
+    conv_log = f"👤 {q}\n🅰️ {o1} | 🅱️ {o2}\n🤖 Evo: {evo_answer} ({evo_reasoning})\n🧠 GPT: {gpt_answer}"
+    return evo_answer, gpt_answer, conv_log
+
+def clear():
+    return "", "", "", None, "", "", ""
+
+def export_csv():
+    return gr.File("feedback_log.csv")
+
+def retrain():
+    train_evo()
+    return "🔁 Evo model reloaded."
+
+with gr.Blocks(theme=gr.themes.Soft(), css="""
+body { background-color: #f3f6fb; font-family: 'Segoe UI', sans-serif; }
+.gradio-container { max-width: 1024px; margin: auto; }
+.gr-box { box-shadow: 0 4px 16px rgba(0,0,0,0.1); border-radius: 12px; padding: 16px; transition: all 0.3s ease-in-out; }
+.gr-button { border-radius: 8px; font-weight: 600; transition: all 0.2s ease-in-out; }
+.gr-button:hover { transform: scale(1.03); background-color: #e6f2ff; }
+.gr-textbox, .gr-radio { border-radius: 8px; }
+""") as demo:
+
+    gr.Markdown("""
+        <h1 style="font-size: 2.2em;">🧠 EvoRAG – Real-Time Reasoning AI</h1>
+        <p><b>Built Different. Learns Live. Evolves from You.</b></p>
+        <div style="margin-top: 10px; font-size: 0.9em;">
+        <ul>
+          <li>🚀 <b>Why Evo?</b></li>
+          <li>✔️ Learns from your input — evolves in real time</li>
+          <li>✔️ Adaptive architecture (changes #layers, memory, etc.)</li>
+          <li>✔️ Tiny model (~13M–28M params) vs GPT-3.5 (175B)</li>
+          <li>✔️ Runs on CPU or low-end GPUs</li>
+          <li>✔️ Transparent architecture: shows how it thinks</li>
+          <li>✔️ Can be deployed, fine-tuned, and evolved per user/domain</li>
+        </ul>
+        </div>
+    """)
 
-# 🌐 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=2):
-            gr.Markdown("""
-                # 🧠 EvoRAG
-                ### Built Different. Learns Live. Evolves from You.
-            """)
-            gr.Markdown(why_evo_panel(), elem_id="why-evo")
+        with gr.Column():
+            question.render()
+            option1.render()
+            option2.render()
+            choice.render()
+
+        with gr.Column():
+            ask_btn = gr.Button("💡 Ask Evo", variant="primary")
+            retrain_btn = gr.Button("🔁 Retrain Evo")
+            clear_btn = gr.Button("🧹 Clear")
+            export_btn = gr.Button("⬇️ Export Feedback CSV")
+            evo_stats.render()
 
-        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")
-            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():
+        evo_out.render()
+        gpt_out.render()
 
     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="📊 Evolution Log")
-            evo_why = gr.Markdown(label="🔬 Why Evo?")
+        retrain_status = gr.Textbox(label="🔁 Status", interactive=False)
+
+    with gr.Accordion("📚 Conversation History", open=True):
+        history.render()
 
-    submit.click(chat_fn, inputs=[user_input, option1, option2, user_vote],
-                 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=[])
+    ask_btn.click(fn=run_inference, inputs=[question, option1, option2, choice], outputs=[evo_out, gpt_out, history])
+    clear_btn.click(fn=clear, outputs=[question, option1, option2, choice, evo_out, gpt_out, history])
+    export_btn.click(fn=export_csv, outputs=[])
+    retrain_btn.click(fn=retrain, outputs=[retrain_status])
 
-    demo.launch()
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))