watchdog.py

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

from init_model import load_model
from dashboard import evolution_accuracy_plot

# 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 get_architecture_summary(model):
    summary = {
        "Layers": getattr(model.config, "num_layers", "N/A"),
        "Attention Heads": getattr(model.config, "num_heads", "N/A"),
        "FFN Dim": getattr(model.config, "ffn_dim", "N/A"),
        "Memory Enabled": getattr(model.config, "use_memory", "N/A"),
    }
    return "\n".join(f"{k}: {v}" for k, v in summary.items())

def retrain_model():
    try:
        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:
            return "⚠️ Not enough data to retrain.", None, "Please log more feedback first."

        # ✅ Explicitly define architecture details
        config = EvoTransformerConfig(
            hidden_size=384,
            num_layers=6,
            num_labels=2,
            num_heads=6,
            ffn_dim=1024,
            use_memory=False
        )

        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}")

        # Simulate accuracy (placeholder)
        accuracy = 1.0

        # Log evolution accuracy
        log_path = "trained_model/evolution_log.json"
        os.makedirs("trained_model", exist_ok=True)

        if os.path.exists(log_path):
            with open(log_path, "r") as f:
                history = json.load(f)
        else:
            history = []

        history.append({"accuracy": accuracy})

        with open(log_path, "w") as f:
            json.dump(history, f)

        # Save model
        model.save_pretrained("trained_model")
        print("✅ EvoTransformer retrained and saved.")

        # Reload and return dashboard updates
        updated_model = load_model()
        arch_text = get_architecture_summary(updated_model)
        plot = evolution_accuracy_plot()

        return arch_text, plot, "✅ EvoTransformer retrained successfully!"

    except Exception as e:
        print(f"❌ Retraining failed: {e}")
        return "❌ Error", None, f"Retrain failed: {e}"

# Allow direct script run
if __name__ == "__main__":
    retrain_model()