Update watchdog.py

watchdog.py

@@ -1,127 +1,35 @@
-# 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 pandas as pd
+from evo_model import EvoTransformer, train_evo_transformer
+from datasets import load_dataset
 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()
+    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