Update app.py

app.py

@@ -11,11 +11,6 @@ from stable_baselines3 import PPO
 app = FastAPI(title="Transaction Classifier API", description="API for classifying banking transactions.")
 print("FastAPI app initialized...")
 
-model = None
-tokenizer = None
-le_category = None
-le_subcategory = None
-ppo_model = None
 max_len = 20
 
 class HierarchicalPrediction(tf.keras.layers.Layer):
@@ -63,35 +58,25 @@ class HierarchicalPrediction(tf.keras.layers.Layer):
 tf.keras.utils.get_custom_objects()['HierarchicalPrediction'] = HierarchicalPrediction
 
 def load_resources():
-    global model, tokenizer, le_category, le_subcategory
-    if model is None:
-        print("Loading BiLSTM model...")
-        model = tf.keras.models.load_model('model.h5', custom_objects={'HierarchicalPrediction': HierarchicalPrediction})
-        print("BiLSTM model loaded.")
-    if tokenizer is None:
-        print("Loading tokenizer...")
-        with open('tokenizer.pkl', 'rb') as f:
-            tokenizer = pickle.load(f)
-        print("Tokenizer loaded.")
-    if le_category is None:
-        print("Loading category label encoder...")
-        with open('le_category.pkl', 'rb') as f:
-            le_category = pickle.load(f)
-        print("Category label encoder loaded.")
-    if le_subcategory is None:
-        print("Loading subcategory label encoder...")
-        with open('le_subcategory.pkl', 'rb') as f:
-            le_subcategory = pickle.load(f)
-        print("Subcategory label encoder loaded.")
+    print("Loading BiLSTM model...")
+    model = tf.keras.models.load_model('model.h5', custom_objects={'HierarchicalPrediction': HierarchicalPrediction})
+    print("Loading tokenizer...")
+    with open('tokenizer.pkl', 'rb') as f:
+        tokenizer = pickle.load(f)
+    print("Loading category label encoder...")
+    with open('le_category.pkl', 'rb') as f:
+        le_category = pickle.load(f)
+    print("Loading subcategory label encoder...")
+    with open('le_subcategory.pkl', 'rb') as f:
+        le_subcategory = pickle.load(f)
+    print(f"Num categories: {len(le_category.classes_)}, Num subcategories: {len(le_subcategory.classes_)}")
     return model, tokenizer, le_category, le_subcategory
 
 def load_ppo_model():
-    global ppo_model
-    if ppo_model is None:
-        print("Loading PPO model...")
-        ppo_model = PPO.load('ppo_finetuned_model')
-        print("PPO model loaded.")
-    return ppo_model
+    print("Loading PPO model...")
+    ppo = PPO.load('ppo_finetuned_model')
+    print("PPO model loaded.")
+    return ppo
 
 class TransactionRequest(BaseModel):
     description: str
@@ -120,7 +105,7 @@ async def predict(request: TransactionRequest):
         if request.use_rl:
             ppo = load_ppo_model()
             obs = pad[0]
-            action, _ = ppo.predict(obs, deterministic=True)  # Force deterministic
+            action, _ = ppo.predict(obs)  # Match Colab: no deterministic=True
             print(f"RL Action: {action}, Observation: {obs}")
             cat_idx = action // num_subcategories
             subcat_idx = action % num_subcategories