Update app.py

app.py

@@ -13,7 +13,7 @@ vectorizer = joblib.load("vectorizer.joblib") # global vocabulary (used for Logi
 
 # Decode label function
 # {'business': 0, 'entertainment': 1, 'health': 2, 'politics': 3, 'sport': 4}
-def categorize(input_number):
+def decodedLabel(input_number):
     print('receive label encoded', input_number)
     categories = {
       0: 'Business',
@@ -85,15 +85,16 @@ def process_api(text):
     SVM_Predicted = SVM_model.predict(processed_text).tolist() # SVC Model
     # Seq_Predicted = Seq_model.predict(padded_sequence)
     # predicted_label_index = np.argmax(Seq_Predicted)
-    
-    test1 = categorize(int(Logistic_Predicted[0]))
-    test2 = categorize(int(SVM_Predicted[0]))
-    print('Logistic', int(Logistic_Predicted[0]), test1)
-    print('SVM', int(SVM_Predicted[0]), test2)
+
+    # ----------- Debug Logs -----------
+    logistic_debug = decodedLabel(int(Logistic_Predicted[0]))
+    svc_debug = decodedLabel(int(SVM_Predicted[0]))
+    print('Logistic', int(Logistic_Predicted[0]), logistic_debug)
+    print('SVM', int(SVM_Predicted[0]), svc_debug)
     
     return {
-            'Logistic_Predicted':int(Logistic_Predicted[0]),
-            'SVM_Predicted': int(SVM_Predicted[0]),
+            'Logistic_Predicted':decodedLabel(int(Logistic_Predicted[0])),
+            'SVM_Predicted': decodedLabel(int(SVM_Predicted[0])),
             'Article_Content': text
         }
 
@@ -116,4 +117,7 @@ if url:
     result = categorize(url)
     article_content = result.get('Article_Content')
     st.text_area("Article Content", value=article_content, height=400) # render the article content as textarea element
-    st.json(result)
+    st.json({
+        "Logistic": result.get("Logistic_Predicted"),
+        "SVC": result.get("SVM_Predicted")
+    })