Update env.py

env.py

@@ -2,6 +2,7 @@ import numpy as np
 import gymnasium as gym
 from gymnasium import spaces
 
+# Labels as requested
 URGENCY_LABELS = ["General", "Billing", "Security Breach"]
 ROUTING_LABELS = ["AI Auto-Reply", "Tech Support", "Legal"]
 RESOLUTION_LABELS = ["Archive", "Draft Reply", "Escalate to Human"]
@@ -9,40 +10,45 @@ RESOLUTION_LABELS = ["Archive", "Draft Reply", "Escalate to Human"]
 class EmailTriageEnv(gym.Env):
     def __init__(self, task="all"):
         super().__init__()
-        # App.py se dataset import karne ki koshish
-        try:
-            from app import EMAIL_DATASET
-            dataset = EMAIL_DATASET
-        except:
-            dataset = []
+        
+        # Hardcoded dataset to avoid import errors
+        self.full_dataset = [
+            {"difficulty": "easy", "description": "Spam promo", "correct_actions": (0, 0, 0)},
+            {"difficulty": "easy", "description": "Routine support", "correct_actions": (0, 1, 1)},
+            {"difficulty": "medium", "description": "Billing dispute", "correct_actions": (1, 2, 2)},
+            {"difficulty": "medium", "description": "Refund request", "correct_actions": (1, 2, 2)},
+            {"difficulty": "hard", "description": "IT password reset phish", "correct_actions": (2, 1, 2)},
+            {"difficulty": "hard", "description": "Ransomware threat", "correct_actions": (2, 2, 2)}
+        ]
 
         if task != "all":
-            self._queue = [e for e in dataset if e.get("difficulty") == task]
+            self._queue = [e for e in self.full_dataset if e.get("difficulty") == task]
         else:
-            self._queue = dataset
+            self._queue = self.full_dataset
             
         self.action_space = spaces.MultiDiscrete([3, 3, 3])
-        self.observation_space = spaces.Box(low=0.0, high=1.0, shape=(31,), dtype=np.float32)
+        self.observation_space = spaces.Box(low=0.0, high=1.0, shape=(10,), dtype=np.float32)
         self._step_idx = 0
 
-    def reset(self):
+    def reset(self, seed=None, options=None):
+        super().reset(seed=seed)
         self._step_idx = 0
-        return np.zeros(31, dtype=np.float32), {}
+        return np.zeros(10, dtype=np.float32), {}
 
     def step(self, action):
         if self._step_idx >= len(self._queue):
-            return np.zeros(31), 0, True, False, {}
+            return np.zeros(10), 0.0, True, False, {}
             
         email = self._queue[self._step_idx]
         correct = email["correct_actions"]
         
+        # Scoring logic
         reward = 1.0 if tuple(action) == tuple(correct) else 0.0
-        # Security missed penalty
         if correct[0] == 2 and action[0] != 2:
-            reward = -2.0
+            reward = -2.0 # Security penalty
 
         self._step_idx += 1
         done = self._step_idx >= len(self._queue)
         
         info = {"raw_reward": reward, "correct_actions": correct}
-        return np.zeros(31), float(reward), done, False, info
+        return np.zeros(10), float(reward), done, False, info