'''import gradio as gr
from inference import get_evo_response, get_gpt_response
from logger import log_feedback

# Default demo
default_question = "What should I do if my house is on fire?"
default_option1 = "I hide under the bed"
default_option2 = "I run for dear life"
default_context = ""

def run_comparison(question, option1, option2, context):
    options = [option1.strip(), option2.strip()]
    evo_answer, evo_reasoning, evo_conf, evo_context = get_evo_response(question, options, context)
    gpt_answer = get_gpt_response(question, context)

    evo_output = f"""**✅ Evo's Suggestion:** {evo_answer}\n\n**Why?** {evo_reasoning}\n\n**Context Used:** {evo_context[:500]}..."""
    gpt_output = f"""**🤖 GPT-3.5's Suggestion:**\n\n{gpt_answer}"""

    return evo_output, gpt_output

def handle_feedback(evo_was_correct, question, option1, option2, context, evo_output):
    if evo_was_correct is not None:
        log_feedback(question, option1, option2, context, evo_output, evo_was_correct)
        return "✅ Feedback recorded. Evo will retrain based on this soon!"
    return "⚠️ Please select feedback before submitting."

with gr.Blocks(theme=gr.themes.Base(), title="EvoRAG - Smarter Than GPT?") as demo:
    gr.Markdown("## 🧠 EvoRAG")
    gr.Markdown("**General-Purpose Adaptive AI that Thinks, Reads, and Evolves — Powered by Real-Time Web Search**")
    gr.Markdown("> Compare reasoning between Evo (which learns) and GPT-3.5 (which doesn't). You decide who wins.")

    with gr.Row():
        with gr.Column():
            question = gr.Textbox(label="❓ Your Question", placeholder="e.g. What should I do if my house is on fire?", value=default_question)
            option1 = gr.Textbox(label="🔹 Option 1", placeholder="e.g. I hide under the bed", value=default_option1)
            option2 = gr.Textbox(label="🔸 Option 2", placeholder="e.g. I run for dear life", value=default_option2)
            context = gr.Textbox(label="📂 Extra Notes or Context (Optional)", placeholder="Paste news, user context, or background information", lines=4, value=default_context)
            compare_btn = gr.Button("🚀 Think & Compare")

        with gr.Column():
            evo_out = gr.Markdown(label="🧬 EvoRAG's Response")
            gpt_out = gr.Markdown(label="🤖 GPT-3.5's Suggestion")

    compare_btn.click(fn=run_comparison, inputs=[question, option1, option2, context], outputs=[evo_out, gpt_out])

    gr.Markdown("---")
    gr.Markdown("### 🧠 Help Evo Get Smarter – Give Feedback")
    feedback = gr.Radio(["👍 Evo was correct. Retrain from this.", "👎 Evo was wrong."], label="What did you think of Evo's answer?")
    submit_feedback = gr.Button("📬 Submit Feedback / Retrain Evo")
    feedback_status = gr.Textbox(label="", interactive=False)

    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_out],
        outputs=[feedback_status]
    )

demo.launch()'''

import gradio as gr
import os
from inference import get_evo_response, get_gpt_response
from logger import log_feedback
import csv
import subprocess

# 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:
                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)
    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, option1, option2, context, evo_output, is_helpful)
    return "✅ Feedback logged. Evo will improve."

# Trigger retrain (placeholder command)
def retrain_evo():
    try:
        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"⚠️ 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!"
    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 – Real-Time Adaptive Reasoning AI

**What is Evo?**  
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
"""

# Build Interface
with gr.Blocks(title="EvoRAG – Evo vs GPT Reasoning") as demo:
    gr.Markdown(description)

    with gr.Row():
        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 relevant info, article, user context", lines=3)

    submit_btn = gr.Button("🔍 Run Comparison")

    with gr.Row():
        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 better?",
        value=None
    )
    submit_feedback = gr.Button("📬 Submit Feedback")
    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_btn.click(fn=retrain_evo, inputs=[], outputs=[retrain_status])

demo.launch(server_name="0.0.0.0", server_port=7860, share=True)