retrain_from_feedback.py

import csv
import os
from datetime import datetime
from retrain_from_feedback import train_evo

# 🔁 Main entry point for feedback-triggered retraining
def retrain_from_feedback(feedback_log):
    # ✅ Check if feedback is present
    if not feedback_log:
        return "⚠️ No feedback data to retrain from."

    # 📝 Write feedback to CSV
    try:
        os.makedirs("feedback", exist_ok=True)
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        filepath = f"feedback/feedback_log.csv"  # also usable for loading
        with open(filepath, "w", newline="", encoding="utf-8") as f:
            writer = csv.writer(f)
            writer.writerow([
                "question", "option1", "option2", "answer",
                "confidence", "reasoning", "context",
                "user_preference", "evo_was_correct", "evo_output"
            ])
            for row in feedback_log:
                question, option1, option2, answer, confidence, reasoning, context = row

                # Simulate Evo being preferred (you can modify this logic later)
                writer.writerow([
                    question, option1, option2, answer,
                    confidence, reasoning, context,
                    "evo", "yes", answer
                ])
    except Exception as e:
        return f"❌ Failed to save feedback: {str(e)}"

    # 🔁 Trigger training
    try:
        train_evo()  # This uses the latest feedback_log.csv
        return "✅ Evo retrained and weights saved."
    except Exception as e:
        return f"❌ Evo training failed: {str(e)}"