Update app.py

app.py

@@ -1,90 +1,75 @@
 import gradio as gr
+from inference import evo_chat_predict, get_model_config, get_system_stats, get_gpt_response
+import pandas as pd
+import csv
 import os
-from inference import evo_infer, gpt_infer
-from retrain_from_feedback import train_evo
-from logger import log_feedback
+from datetime import datetime
 
-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")
+feedback_log = []
 
-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>
+with gr.Blocks(theme=gr.themes.Base(), css="body { background-color: #0f0f0f; color: #f5f5f5; }") as demo:
+    with gr.Column():
+        gr.HTML("""
+        <div style="padding: 10px; border-radius: 12px; background: #1f1f2e; color: #fff; font-size: 16px; margin-bottom: 12px;">
+          <b>Why Evo?</b> 🚀 Evo is not just another AI. It evolves. It learns from you. It adapts its architecture live based on feedback. No retraining labs, no frozen weights. This is <u>live reasoning meets evolution</u>. <span style="color:#88ffcc">Built to outperform, built to survive.</span>
         </div>
-    """)
+        """)
 
     with gr.Row():
         with gr.Column():
-            question.render()
-            option1.render()
-            option2.render()
-            choice.render()
+            query = 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")
+            feedback = gr.Radio(["Evo", "GPT"], label="🧠 Who was better?", info="Optional – fuels evolution", interactive=True)
+            evo_btn = gr.Button("⚡ Ask Evo", elem_id="evo-btn")
+            retrain_btn = gr.Button("🔁 Retrain Evo", elem_id="retrain-btn")
+            clear_btn = gr.Button("🧹 Clear")
+            export_btn = gr.Button("📤 Export Feedback CSV")
 
         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()
+            evo_stats = gr.Textbox(label="📊 Evo Stats", interactive=False)
+            evo_box = gr.Textbox(label="🧠 Evo", interactive=False)
+            gpt_box = gr.Textbox(label="🤖 GPT-3.5", interactive=False)
+            status_box = gr.Textbox(label="🔵 Status", interactive=False)
 
-    with gr.Row():
-        evo_out.render()
-        gpt_out.render()
+        convo = gr.Dataframe(headers=["Question", "Option 1", "Option 2", "Answer", "Confidence", "Reasoning", "Context"], interactive=False, wrap=True, label="📜 Conversation History")
 
-    with gr.Row():
-        retrain_status = gr.Textbox(label="🔁 Status", interactive=False)
+    def ask_evo(q, opt1, opt2, hist):
+        result = evo_chat_predict(hist, q, [opt1, opt2])
+        evo_text = f"Answer: {result['answer']} (Confidence: {result['confidence']})\n\nReasoning: {result['reasoning']}"
+        evo_box.update(value=evo_text)
+        gpt_box.update(value=get_gpt_response(q))
+        stats = get_model_config()
+        evo_stats.update(value=f"Layers: {stats['num_layers']} | Heads: {stats['num_heads']} | FFN: {stats['ffn_dim']} | Memory: {stats['memory_enabled']} | Phase: V2.2")
+        return evo_text, result['context_used'], result['answer'], result['confidence'], result['reasoning'], result['context_used']
+
+    def retrain_evo():
+        status_box.update(value="Retraining Evo... (simulated)")
+
+    def clear_fields():
+        query.update(value="")
+        option1.update(value="")
+        option2.update(value="")
+        evo_box.update(value="")
+        gpt_box.update(value="")
+        status_box.update(value="")
 
-    with gr.Accordion("📚 Conversation History", open=True):
-        history.render()
+    def log_feedback_to_csv():
+        if feedback_log:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            filepath = f"feedback_{timestamp}.csv"
+            with open(filepath, "w", newline="") as f:
+                writer = csv.writer(f)
+                writer.writerow(["Question", "Option 1", "Option 2", "Answer", "Confidence", "Reasoning", "Context"])
+                writer.writerows(feedback_log)
+            status_box.update(value=f"✅ Feedback exported to {filepath}")
+        else:
+            status_box.update(value="⚠️ No feedback to export.")
 
-    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])
+    evo_btn.click(fn=ask_evo, inputs=[query, option1, option2, convo], outputs=[evo_box, gpt_box, convo])
+    retrain_btn.click(fn=retrain_evo, inputs=[], outputs=[status_box])
+    clear_btn.click(fn=clear_fields, inputs=[], outputs=[])
+    export_btn.click(fn=log_feedback_to_csv, inputs=[], outputs=[status_box])
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))
+    demo.launch()