Create watchdog.py

watchdog.py

@@ -0,0 +1,122 @@
+# 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
+
+if __name__ == "__main__":
+    watch()