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# watchdog.py
import firebase_admin
from firebase_admin import credentials, firestore
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
import torch.nn as nn
import torch.optim as optim
from model import EvoTransformer # make sure this is in your project
import time
import datetime
import os
# ✅ Firebase Setup
if not firebase_admin._apps:
cred = credentials.Certificate("evotransformer-firebase-adminsdk-fbsvc-37a4b838aa.json")
firebase_admin.initialize_app(cred)
db = firestore.client()
COLLECTION = "evo_feedback_logs"
LAST_CHECK_FILE = "last_feedback_timestamp.txt"
# ✅ Dataset for training
class EvoDataset(Dataset):
def __init__(self, data):
self.data = data
def __getitem__(self, idx):
item = self.data[idx]
x = f"{item['goal']} [SEP] {item['solution1']} [SEP] {item['solution2']}"
y = 0 if item['correct'] == "Solution 1" else 1
return x, y
def __len__(self):
return len(self.data)
# ✅ Dummy tokenizer (replace with your tokenizer if needed)
def tokenize(text):
return torch.tensor([ord(c) % 128 for c in text[:256]])
# ✅ Fetch new data
def fetch_new_feedback():
if os.path.exists(LAST_CHECK_FILE):
with open(LAST_CHECK_FILE, "r") as f:
last_ts = f.read().strip()
else:
last_ts = "1970-01-01T00:00:00Z"
query = db.collection(COLLECTION).where("timestamp", ">", last_ts)
docs = list(query.stream())
feedbacks = []
latest_ts = last_ts
for doc in docs:
data = doc.to_dict()
if all(k in data for k in ["goal", "sol1", "sol2", "correct"]):
feedbacks.append({
"goal": data["goal"],
"solution1": data["sol1"],
"solution2": data["sol2"],
"correct": data["correct"]
})
latest_ts = max(latest_ts, data.get("timestamp", last_ts))
if feedbacks:
with open(LAST_CHECK_FILE, "w") as f:
f.write(latest_ts)
return feedbacks
# ✅ Train Evo on new data
def train_on_feedback(feedbacks):
if not feedbacks:
print("No new feedback to train on.")
return
print(f"🔁 Retraining on {len(feedbacks)} new examples...")
dataset = EvoDataset(feedbacks)
dataloader = DataLoader(dataset, batch_size=4, shuffle=True)
model = EvoTransformer()
if os.path.exists("trained_model.pt"):
model.load_state_dict(torch.load("trained_model.pt"))
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
model.train()
for epoch in range(3): # quick fine-tuning
total_loss = 0
correct = 0
for inputs, labels in dataloader:
inputs = torch.stack([tokenize(x) for x in inputs])
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
total_loss += loss.item()
correct += (outputs.argmax(dim=1) == labels).sum().item()
acc = correct / len(dataset)
print(f"Epoch {epoch+1}: Loss={total_loss:.4f}, Accuracy={acc:.2%}")
torch.save(model.state_dict(), "trained_model.pt")
print("✅ Updated model saved.")
# ✅ Watch Loop
def watch():
print("🧠 Evo Watchdog started...")
while True:
try:
new_data = fetch_new_feedback()
train_on_feedback(new_data)
except Exception as e:
print(f"⚠️ Error: {str(e)}")
time.sleep(60) # check every 60 seconds
def manual_retrain():
new_data = fetch_new_feedback()
train_on_feedback(new_data)
# Optional: only run loop if executed directly
if __name__ == "__main__":
watch() |