watchdog.py

import torch
import pandas as pd
from evo_model import EvoTransformer, train_evo_transformer
from datasets import load_dataset
import os

def manual_retrain():
    try:
        # Load feedback data from Firestore
        from google.cloud import firestore
        db = firestore.Client.from_service_account_json("firebase_key.json")
        docs = db.collection("evo_feedback_logs").stream()
        data = [doc.to_dict() for doc in docs if "goal" in doc.to_dict()]
        if not data:
            print("No feedback data available.")
            return False

        # Convert to training format
        rows = []
        for d in data:
            question = d["goal"]
            option1 = d["sol1"]
            option2 = d["sol2"]
            correct = d["correct"]
            label = 0 if correct == "Solution 1" else 1
            rows.append((question, option1, option2, label))
        df = pd.DataFrame(rows, columns=["goal", "sol1", "sol2", "label"])

        # Train the Evo model (minimal epochs to simulate update)
        train_evo_transformer(df, epochs=1)

        return True
    except Exception as e:
        print(f"[Retrain Error] {e}")
        return False