watchdog.py

import os
import torch
import firebase_admin
from firebase_admin import credentials, firestore
from evo_model import EvoTransformerForClassification, EvoTransformerConfig
from transformers import BertTokenizer

# Initialize Firebase if not already initialized
if not firebase_admin._apps:
    cred = credentials.Certificate("firebase_key.json")
    firebase_admin.initialize_app(cred)

db = firestore.client()

def fetch_training_data(tokenizer):
    logs_ref = db.collection("evo_feedback")
    docs = logs_ref.stream()

    input_ids, attention_masks, labels = [], [], []
    for doc in docs:
        data = doc.to_dict()
        prompt = data.get("prompt", "")
        winner = data.get("winner", "")
        if winner and prompt:
            text = prompt + " [SEP] " + winner
            encoding = tokenizer(
                text,
                truncation=True,
                padding="max_length",
                max_length=128,
                return_tensors="pt"
            )
            input_ids.append(encoding["input_ids"][0])
            attention_masks.append(encoding["attention_mask"][0])
            label = 0 if "1" in winner else 1
            labels.append(label)

    if not input_ids:
        return None, None, None

    return (
        torch.stack(input_ids),
        torch.stack(attention_masks),
        torch.tensor(labels, dtype=torch.long)
    )

def retrain_and_save():
    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
    input_ids, attention_masks, labels = fetch_training_data(tokenizer)

    if input_ids is None or len(input_ids) < 2:
        print("⚠️ Not enough training data.")
        return

    config = EvoTransformerConfig()
    model = EvoTransformerForClassification(config)
    model.train()

    optimizer = torch.optim.Adam(model.parameters(), lr=2e-4)
    loss_fn = torch.nn.CrossEntropyLoss()

    for epoch in range(3):
        optimizer.zero_grad()
        outputs = model(input_ids, attention_mask=attention_masks)
        loss = loss_fn(outputs, labels)
        loss.backward()
        optimizer.step()
        print(f"Epoch {epoch+1}: Loss = {loss.item():.4f}")

    os.makedirs("trained_model", exist_ok=True)
    model.save_pretrained("trained_model")
    print("✅ EvoTransformer retrained and saved to trained_model/")

if __name__ == "__main__":
    retrain_and_save()