init decoded label def

app.py

@@ -11,6 +11,20 @@ logistic_model = joblib.load("Logistic_Model.joblib") # Logistic
 vectorizer = joblib.load("vectorizer.joblib") # global vocabulary (used for Logistic, SVC)
 tokenizer = joblib.load("tokenizer.joblib") # used for LSTM
 
+# Decode label function
+# {'business': 0, 'entertainment': 1, 'health': 2, 'politics': 3, 'sport': 4}
+def categorize(input_number):
+    categories = {
+      0: 'Business',
+      1: 'Entertainment',
+      2: 'Health',
+      3: 'Politics',
+      4: 'Sport'
+    }
+    result = categories.get(input_number)
+    print('decoded result', result)
+    return result
+
 # Web Crawler function
 def crawURL(url):
     # Fetch the URL content
@@ -66,14 +80,13 @@ def process_api(text):
     # padded_sequence = pad_sequences(sequence, maxlen=1000, padding='post')
     
     # Get the predicted result from models
-    Logistic_Predicted = logistic_model.predict(processed_text).tolist()
-    SVM_Predicted = SVM_model.predict(processed_text).tolist()
+    Logistic_Predicted = logistic_model.predict(processed_text).tolist() # Logistic Model
+    SVM_Predicted = SVM_model.predict(processed_text).tolist() # SVC Model
     # Seq_Predicted = Seq_model.predict(padded_sequence)
-
     # predicted_label_index = np.argmax(Seq_Predicted)
     return {
-            'SVM_Predicted': int(SVM_Predicted[0]),
-            'Logistic_Predicted': int(Logistic_Predicted[0])
+            'Logistic_Predicted': categorize(int(Logistic_Predicted[0]))
+            'SVM_Predicted': categorize(int(SVM_Predicted[0])),
             'Article_Content': text,
         }