Update app.py

app.py

@@ -10,13 +10,13 @@ from tensorflow.keras.preprocessing.sequence import pad_sequences
 import numpy as np
 
 # load all the models and vectorizer (global vocabulary)
-# Seq_model = load_model("LSTM.h5") # Sequential
+Seq_model = load_model("LSTM.h5") # Sequential
 SVM_model = joblib.load("SVM_Linear_Kernel.joblib") # SVM
 logistic_model = joblib.load("Logistic_Model.joblib") # Logistic
 svm_model = joblib.load('svm_model.joblib')
 
 vectorizer = joblib.load("vectorizer.joblib") # global vocabulary (used for Logistic, SVC)
-# tokenizer = joblib.load("tokenizer.joblib") # used for LSTM
+tokenizer = joblib.load("tokenizer.joblib") # used for LSTM
 
 # Decode label function
 # {'business': 0, 'entertainment': 1, 'health': 2, 'politics': 3, 'sport': 4}
@@ -90,21 +90,23 @@ def process_api(text):
     # Get the predicted result from models
     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)
-
+    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)))
+    #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)))
+    #print(float(np.max(svm_probs)))
+    
     # ----------- Debug Logs -----------
     logistic_debug = decodedLabel(int(Logistic_Predicted[0]))
     svc_debug = decodedLabel(int(SVM_Predicted[0]))
     # predicted_label_index = np.argmax(Seq_Predicted)
-    print('Logistic', int(Logistic_Predicted[0]), logistic_debug)
-    print('SVM', int(SVM_Predicted[0]), svc_debug)
+    #print('Logistic', int(Logistic_Predicted[0]), logistic_debug)
+    #print('SVM', int(SVM_Predicted[0]), svc_debug)
     
     return {
             'predicted_label_logistic': decodedLabel(int(Logistic_Predicted[0])),
@@ -112,7 +114,7 @@ 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)),
+            'LSTM': decodedLabel(int(predicted_label_index)),
             'Article_Content': text
         }
 
@@ -227,7 +229,7 @@ if url:
             "predicted_label": result.get("predicted_label_svm"),
             "probability": result.get("probability_svm")
         },
-        # "LSTM": result.get("LSTM")
+        "LSTM": result.get("LSTM")
     })
     
 st.divider()  # 👈 Draws a horizontal rule