app.py

'''import gradio as gr
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("evo_was_correct", "0") == "yes")
                    if "👍" in row.get("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}"
    )

def handle_feedback(feedback_text, question, option1, option2, context, evo_output):
    evo_was_correct = "👍" in feedback_text
    log_feedback(question, option1, option2, context, evo_output, evo_was_correct)
    return "✅ Feedback logged and Evo will improve."

def trigger_retrain():
    try:
        subprocess.run(["python", "retrain_from_feedback.py"], check=True)
        return "🔁 Evo retraining completed."
    except subprocess.CalledProcessError:
        return "❌ Retraining failed. Check logs."

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_output']}\n**Feedback:** {e.get('feedback', 'N/A')}\n**Context:** {e['context'][:200]}..."
            for e in entries
        ]
    )
    return result

description = """
# 🧠 EvoRAG – 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?**  
✅ Evolves from human input  
✅ Architecturally updatable  
✅ Transparent and fine-tunable  
✅ Efficient on modest hardware

**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():
        question = gr.Textbox(label="📝 Ask anything", 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)

    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)

    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")
    feedback_status = gr.Textbox(label="Feedback Status", interactive=False)

    retrain_button = gr.Button("🔄 Retrain Evo Now")
    retrain_status = gr.Textbox(label="Retraining Status", interactive=False)

    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, q, o1, o2, ctx, eo),
        inputs=[feedback, question, option1, option2, context, feedback_status],
        outputs=[feedback_status]
    )
    retrain_button.click(fn=trigger_retrain, inputs=[], outputs=[retrain_status])

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

import gradio as gr
import torch
import time
import os
from inference import load_model_and_tokenizer, infer
from logger import log_feedback
from retrain_from_feedback import train_evo
from datetime import datetime

# Globals
model, tokenizer = load_model_and_tokenizer("trained_model/evo_retrained.pt")

# Helper to reload model
def reload_model():
    global model, tokenizer
    model, tokenizer = load_model_and_tokenizer("trained_model/evo_retrained.pt")

# Get last update time
def get_last_update():
    if os.path.exists("last_updated.txt"):
        with open("last_updated.txt") as f:
            return f.read().strip()
    return "Never"

# Handle inference
def compare(question, option1, option2, context):
    evo_ans, evo_score, evo_reason, evo_ctx = infer(model, tokenizer, question, option1, option2, context)
    gpt_ans = "Coming soon via API"
    return (
        f"Answer: {evo_ans} (Confidence: {evo_score:.2f})\n\nReasoning: {evo_reason}\n\nContext used: {evo_ctx}",
        gpt_ans
    )

# Handle feedback
def handle_feedback(feedback_text, question, option1, option2, context, evo_output):
    evo_was_correct = feedback_text.strip().lower() == "👍 evo was correct. retrain from this."
    log_feedback(question, option1, option2, context, evo_output, evo_was_correct)
    return "✅ Feedback logged and Evo will improve."

# Manual retrain
def manual_retrain():
    try:
        train_evo()
        reload_model()
        ts = datetime.utcnow().strftime("%Y-%m-%d %H:%M GMT")
        with open("last_updated.txt", "w") as f:
            f.write(ts)
        return f"✅ Evo successfully evolved! Reloaded at {ts}"
    except Exception as e:
        return f"❌ Retraining failed: {str(e)}"

with gr.Blocks(title="EvoRAG – Adaptive Reasoning AI", theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # EvoRAG – 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?**  
    ✅ Evolves from human input  
    ✅ Architecturally updatable  
    ✅ Transparent and fine-tunable  
    ✅ Efficient on modest hardware  

    **Hardware:** Trained on Google Colab CPU/GPU  
    **Token limit:** 128  
    **Benchmark:** PIQA, HellaSwag, ARC  
    **Version:** Evo v2.2 (Memory + Web Retrieval + Feedback Learning)  
    **🕒 Last Evolution:** {get_last_update()}
    """)

    with gr.Row():
        question = gr.Textbox(label="Ask anything", placeholder="e.g. What’s the best way to boil water?")

    with gr.Row():
        option1 = gr.Textbox(label="Option A")
        option2 = gr.Textbox(label="Option B")

    context = gr.Textbox(label="📂 Optional Context", lines=2, placeholder="Paste any extra background info here")

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

    with gr.Row():
        evo_out = gr.Textbox(label="🧠 EvoRAG's Reasoned Answer")
        gpt_out = gr.Textbox(label="🤖 GPT-3.5's Suggestion")

    with gr.Row():
        feedback_dropdown = gr.Dropdown([
            "👍 Evo was correct. Retrain from this.",
            "👎 Evo was wrong. Don't retrain."
        ], label="Was Evo’s answer useful?")
        submit_btn = gr.Button("📬 Submit Feedback")

    feedback_status = gr.Textbox(label="Feedback Status")

    with gr.Row():
        retrain_btn = gr.Button("🔄 Retrain Evo Now")
        retrain_status = gr.Textbox(label="Retraining Status")

    hall = gr.Markdown("""
    ## 🏆 Evo Hall of Fame (Top Reasoning Entries)
    *(Coming soon)*
    """)

    run_btn.click(fn=compare, inputs=[question, option1, option2, context], outputs=[evo_out, gpt_out])
    submit_btn.click(fn=lambda fb, q, o1, o2, ctx, eo: handle_feedback(fb, q, o1, o2, ctx, eo),
                     inputs=[feedback_dropdown, question, option1, option2, context, evo_out],
                     outputs=feedback_status)
    retrain_btn.click(fn=manual_retrain, outputs=retrain_status)

demo.launch()