Update app.py

app.py

@@ -92,15 +92,13 @@ 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)
-    print(int(predicted_label_index))
     
     # ----------- Proba -----------
     Logistic_Predicted_proba = logistic_model.predict_proba(processed_text)
-    #print(float(np.max(Logistic_Predicted_proba)))
     svm_new_probs = SVM_model.decision_function(processed_text)
     svm_probs = svm_model.predict_proba(svm_new_probs)
-    #print(float(np.max(svm_probs)))
-    
+
+    predicted_label_index = np.argmax(Seq_Predicted)
     # ----------- Debug Logs -----------
     logistic_debug = decodedLabel(int(Logistic_Predicted[0]))
     svc_debug = decodedLabel(int(SVM_Predicted[0]))
@@ -114,7 +112,10 @@ def process_api(text):
         
             'predicted_label_svm': decodedLabel(int(SVM_Predicted[0])),
             'probability_svm': f"{int(float(np.max(svm_probs))*10000//100)}%",
-            'LSTM': decodedLabel(int(predicted_label_index)),
+        
+            'predicted_label_lstm': decodedLabel(int(predicted_label_index)),
+            'probability_lstm': f"{int(float(np.max(Seq_Predicted))*10000//100)}%",
+        
             'Article_Content': text
         }
 
@@ -229,7 +230,10 @@ if url:
             "predicted_label": result.get("predicted_label_svm"),
             "probability": result.get("probability_svm")
         },
-        "LSTM": result.get("LSTM")
+        "LSTM": {
+            "predicted_label": result.get("predicted_label_lstm"),
+            "probability": result.get("probability_lstmlstm")
+        }
     })
     
 st.divider()  # 👈 Draws a horizontal rule