Update app.py

app.py

@@ -65,99 +65,119 @@ from logger import log_feedback
 import csv
 import subprocess
 
-# Helper to load Hall of Fame
+# Load Hall of Fame entries
 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
+                if row.get("evo_was_correct", "").lower() == "yes":
+                    entries.append(row)
+    return entries[-10:][::-1]  # last 10, reversed
 
+# Process question & get answers
 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_display = (
+        f"✅ Evo's Suggestion: **{evo_answer}**\n\n"
+        f"Why? {evo_reasoning}\n\n"
+        f"Context Used (truncated): {evo_context[:400]}..."
     )
+    gpt_display = f"{gpt_answer}"
+    evo_output_summary = f"{question} | {context} | {evo_answer}"
+    return evo_display, gpt_display, evo_output_summary
 
+# Feedback handler
 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."
+    log_feedback(question, option1, option2, context, evo_output, is_helpful)
+    return "✅ Feedback logged. Evo will improve."
 
-def trigger_retraining():
+# Trigger retrain (placeholder command)
+def retrain_evo():
     try:
-        subprocess.run(["python3", "retrain.py"], check=True)
-        return "✅ Evo retrained from recent feedback!"
+        result = subprocess.run(["python3", "watchdog.py"], capture_output=True, text=True, timeout=60)
+        return f"🔁 Retraining started:\n{result.stdout[:300]}"
     except Exception as e:
-        return f"❌ Retrain failed: {str(e)}"
+        return f"⚠️ Error starting retraining: {str(e)}"
 
+# Render Hall of Fame
 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
+    return "\n\n".join([
+        f"🏆 **Q:** {e['question']}\n**Evo A:** {e['evo_output']}\n**Feedback:** ✅\n**Context:** {e['context'][:200]}..."
+        for e in entries
+    ])
 
+# Header / Description
 description = """
-# 🧠 EvoRAG – Adaptive Reasoning AI
+# 🧠 EvoRAG – Real-Time Adaptive Reasoning AI
 
 **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.  
-
-**Why Evo?**  
-Unlike GPT, Evo evolves from your interaction — architecture, weights, and logic. It's efficient, smart, and improvable on the fly.
-
-**Hardware**: Trained on Google Colab CPU/GPU.  
-**Token limit**: 128.  
-**Benchmark**: PIQA, HellaSwag, ARC  
-**Version**: Evo v2.2 (Memory + Web Retrieval + Feedback Learning)
+EvoTransformer is a lightweight, evolving transformer (~28M params).  
+It adapts on the fly, learns from feedback, uses live web + user context to reason.
+
+**Why Evo over GPT?**  
+✅ Evolves from human input  
+✅ Architecturally updatable  
+✅ Transparent and fine-tunable  
+✅ Efficient on modest hardware
+
+**Hardware:** Google Colab CPU/GPU  
+**Max Tokens per input:** 128  
+**Benchmarks:** PIQA, HellaSwag, ARC  
+**Version:** Evo v2.2 — Memory + Retrieval + Feedback Learning
 """
 
-with gr.Blocks(title="EvoRAG") as demo:
+# Build Interface
+with gr.Blocks(title="EvoRAG – Evo vs GPT Reasoning") as demo:
     gr.Markdown(description)
+
     with gr.Row():
-        question = gr.Textbox(label="📝 Ask anything", placeholder="e.g., What’s the best way to escape a house fire?")
+        question = gr.Textbox(label="📝 Ask any question", placeholder="e.g., What’s the best way to escape a house fire?")
+
     with gr.Row():
         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)
+
+    context = gr.Textbox(label="📂 Optional Context", placeholder="Paste relevant info, article, user context", lines=3)
 
     submit_btn = gr.Button("🔍 Run Comparison")
+
     with gr.Row():
-        evo_output = gr.Textbox(label="🧠 EvoRAG's Reasoned Answer", lines=6)
-        gpt_output = gr.Textbox(label="🤖 GPT-3.5's Suggestion", lines=6)
+        evo_output = gr.Markdown(label="🧠 EvoRAG's Reasoned Answer")
+        gpt_output = gr.Markdown(label="🤖 GPT-3.5's Suggestion")
 
-    feedback = gr.Radio(["👍 Evo was correct. Retrain from this.", "👎 Evo was wrong. Don't retrain."], label="Was Evo’s answer useful?", value=None)
+    feedback = gr.Radio(
+        ["👍 Evo was correct. Retrain from this.", "👎 Evo was wrong. Don't retrain."],
+        label="Was Evo’s answer better?",
+        value=None
+    )
     submit_feedback = gr.Button("📬 Submit Feedback")
-    retrain_button = gr.Button("🔄 Retrain Evo Now")
     feedback_status = gr.Textbox(label="Feedback Status", interactive=False)
 
     with gr.Accordion("🏆 Evo Hall of Fame (Top Reasoning Entries)", open=False):
         hof_display = gr.Markdown(render_hof())
 
+    with gr.Accordion("🔄 Live Evo Retraining (Manual Trigger)", open=False):
+        retrain_btn = gr.Button("Retrain Evo from Feedback Now")
+        retrain_status = gr.Textbox(label="Retrain Status", interactive=False)
+
+    # Events
     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, 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])
+
+    retrain_btn.click(fn=retrain_evo, inputs=[], outputs=[retrain_status])
 
 demo.launch(server_name="0.0.0.0", server_port=7860, share=True)
+