Update app.py

app.py

@@ -59,83 +59,105 @@ with gr.Blocks(theme=gr.themes.Base(), title="EvoRAG - Smarter Than GPT?") as de
 demo.launch()'''
 
 import gradio as gr
-import torch
 import os
 from inference import get_evo_response, get_gpt_response
 from logger import log_feedback
+import csv
+import subprocess
+
+# Helper to load Hall of Fame
+def load_hall_of_fame():
+    entries = []
+    if os.path.exists("feedback_log.csv"):
+        with open("feedback_log.csv", newline='', encoding='utf-8') as f:
+            reader = csv.DictReader(f)
+            for row in reader:
+                try:
+                    score = float(row.get("feedback", "0"))
+                    if "👍" in row["feedback"] or score > 0.85:
+                        entries.append(row)
+                except:
+                    continue
+    return entries[-10:][::-1]  # last 10, reverse order
+
+def handle_query(question, option1, option2, context):
+    options = [option1, option2]
+    evo_answer, evo_reasoning, evo_score, evo_context = get_evo_response(question, options, context)
+    gpt_answer = get_gpt_response(question, context)
+    return (
+        f"Answer: {evo_answer} (Confidence: {evo_score:.2f})\n\nReasoning: {evo_reasoning}\n\nContext used: {evo_context[:400]}...",
+        gpt_answer,
+        f"{question} | {context} | {evo_answer}"
+    )
 
-# ⬇️ Evo Model Stats
-EVO_PARAMS = "~28M Parameters"
-EVO_HARDWARE = "Running on CPU (Colab/Space)"
-EVO_VERSION = "EvoRAG v2.2 – Adaptive Reasoning"
+def handle_feedback(feedback_text, question, option1, option2, context, evo_output):
+    is_helpful = "👍" in feedback_text
+    log_feedback(question, context, evo_output, feedback_text)
+    return "✅ Feedback logged and Evo will improve."
+
+def trigger_retraining():
+    try:
+        subprocess.run(["python3", "retrain.py"], check=True)
+        return "✅ Evo retrained from recent feedback!"
+    except Exception as e:
+        return f"❌ Retrain failed: {str(e)}"
+
+def render_hof():
+    entries = load_hall_of_fame()
+    if not entries:
+        return "No Hall of Fame entries yet. Submit feedback!"
+    result = "\n\n".join(
+        [
+            f"🏆 **Q:** {e['question']}\n**A:** {e['evo_answer']}\n**Feedback:** {e['feedback']}\n**Context:** {e['context'][:200]}..."
+            for e in entries
+        ]
+    )
+    return result
 
-# ⬇️ Feedback Logger Wrapper
-FEEDBACK_LOG_PATH = "feedback_log.csv"
-os.makedirs(os.path.dirname(FEEDBACK_LOG_PATH), exist_ok=True) if os.path.dirname(FEEDBACK_LOG_PATH) else None
+description = """
+# 🧠 EvoRAG – Adaptive Reasoning AI
 
-def handle_feedback(is_correct, question, option1, option2, context, evo_output):
-    feedback = "👍" if is_correct else "👎"
-    log_feedback(question, context, evo_output, feedback)
-    return "✅ Feedback recorded. Evo will learn from this." if is_correct else "✅ Feedback noted."
+**What is Evo?**  
+EvoTransformer is a lightweight, evolving neural network with ~28M parameters.  
+It learns from feedback, adapts over time, and reasons using both web and context data.  
 
-def run_comparison(question, option1, option2, context):
-    options = [option1.strip(), option2.strip()]
-    evo_ans, evo_reason, evo_conf, evo_ctx = get_evo_response(question, options, context)
-    gpt_ans = get_gpt_response(question, context)
-
-    evo_output = f"Evo's Suggestion: ✅ {evo_ans}\n\nWhy? {evo_reason}\n\nContext Used: {evo_ctx[:400]}..."
-    gpt_output = f"GPT-3.5's Suggestion: {gpt_ans}"
-    return evo_output, gpt_output, evo_ans
-
-# ⬇️ Interface
-with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue")) as demo:
-    with gr.Column():
-        gr.Markdown(f"""
-        <div style='padding: 1em; border-radius: 12px; background: linear-gradient(90deg, #f0f4ff, #eef2fa); border: 1px solid #ccc;'>
-            <h1 style='font-size: 2em; font-weight: 800;'>🧠 EvoRAG – General-Purpose Adaptive AI</h1>
-            <p><b>{EVO_VERSION}</b></p>
-            <p>Trained on reasoning tasks. Live learning from feedback. Combines architecture evolution and retrieval-augmented generation.</p>
-            <ul>
-                <li><b>Parameters:</b> {EVO_PARAMS}</li>
-                <li><b>Hardware:</b> {EVO_HARDWARE}</li>
-                <li><b>Live Feedback:</b> Logs every correction to evolve smarter.</li>
-                <li><b>Compare:</b> Evo vs GPT-3.5 on the same question.</li>
-            </ul>
-            <p style='font-style: italic; font-size: 0.9em;'>Built for ethical, explainable, and adaptive intelligence.</p>
-        </div>
-        """)
+**Why Evo?**  
+Unlike GPT, Evo evolves from your interaction — architecture, weights, and logic. It's efficient, smart, and improvable on the fly.
 
-    with gr.Row():
-        question = gr.Textbox(label="📝 Ask a Question", placeholder="e.g., What should you do in case of a fire?", lines=2)
-    with gr.Row():
-        option1 = gr.Textbox(label="Option A", placeholder="e.g., Hide inside")
-        option2 = gr.Textbox(label="Option B", placeholder="e.g., Run for dear life")
-    context = gr.Textbox(label="📂 Optional Context or Notes", placeholder="Paste any extra info here", lines=2)
+**Hardware**: Trained on Google Colab CPU/GPU.  
+**Token limit**: 128.  
+**Benchmark**: PIQA, HellaSwag, ARC  
+**Version**: Evo v2.2 (Memory + Web Retrieval + Feedback Learning)
+"""
 
+with gr.Blocks(title="EvoRAG") as demo:
+    gr.Markdown(description)
     with gr.Row():
-        evo_out = gr.Textbox(label="🧬 EvoRAG's Reasoned Answer")
-        gpt_out = gr.Textbox(label="🤖 GPT-3.5's Suggestion")
-
-    evo_choice = gr.State()
-
+        question = gr.Textbox(label="📝 Ask anything", placeholder="e.g., What’s the best way to escape a house fire?")
     with gr.Row():
-        run_btn = gr.Button("🔍 Run Comparison")
+        option1 = gr.Textbox(label="Option A", placeholder="e.g., Run outside")
+        option2 = gr.Textbox(label="Option B", placeholder="e.g., Hide under bed")
+    context = gr.Textbox(label="📂 Optional Context", placeholder="Paste any extra background info here", lines=3)
 
+    submit_btn = gr.Button("🔍 Run Comparison")
     with gr.Row():
-        feedback = gr.Radio(
-            ["👍 Evo was correct. Retrain from this.", "👎 Evo was wrong. Improve it."],
-            label="Was Evo’s answer useful?"
-        )
-        submit_feedback = gr.Button("📬 Submit Feedback")
-        feedback_output = gr.Textbox(label="Feedback Status")
+        evo_output = gr.Textbox(label="🧠 EvoRAG's Reasoned Answer", lines=6)
+        gpt_output = gr.Textbox(label="🤖 GPT-3.5's Suggestion", lines=6)
+
+    feedback = gr.Radio(["👍 Evo was correct. Retrain from this.", "👎 Evo was wrong. Don't retrain."], label="Was Evo’s answer useful?", value=None)
+    submit_feedback = gr.Button("📬 Submit Feedback")
+    retrain_button = gr.Button("🔄 Retrain Evo Now")
+    feedback_status = gr.Textbox(label="Feedback Status", interactive=False)
 
-    run_btn.click(fn=run_comparison, inputs=[question, option1, option2, context], outputs=[evo_out, gpt_out, evo_choice])
+    with gr.Accordion("🏆 Evo Hall of Fame (Top Reasoning Entries)", open=False):
+        hof_display = gr.Markdown(render_hof())
 
+    submit_btn.click(fn=handle_query, inputs=[question, option1, option2, context], outputs=[evo_output, gpt_output, feedback_status])
     submit_feedback.click(
-        fn=lambda fb, q, o1, o2, ctx, eo: handle_feedback(fb == "👍 Evo was correct. Retrain from this.", q, o1, o2, ctx, eo),
-        inputs=[feedback, question, option1, option2, context, evo_choice],
-        outputs=[feedback_output]
+        fn=lambda fb, q, o1, o2, ctx, eo: handle_feedback(fb, q, o1, o2, ctx, eo),
+        inputs=[feedback, question, option1, option2, context, feedback_status],
+        outputs=[feedback_status]
     )
+    retrain_button.click(fn=trigger_retraining, inputs=[], outputs=[feedback_status])
 
-demo.launch(ssr=True)
-
+demo.launch(server_name="0.0.0.0", server_port=7860, share=True)