Update app.py

app.py

@@ -9,11 +9,11 @@ from tensorflow.keras.preprocessing.text import Tokenizer
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 
 # 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
 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}
@@ -81,26 +81,26 @@ def crawURL(url):
 def process_api(text):
     # Vectorize the text data
     processed_text = vectorizer.transform([text])
-    sequence = tokenizer.texts_to_sequences([text])
-    padded_sequence = pad_sequences(sequence, maxlen=1000, padding='post')
+    # sequence = tokenizer.texts_to_sequences([text])
+    # padded_sequence = pad_sequences(sequence, maxlen=1000, padding='post')
     
     # 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)
+    # Seq_Predicted = Seq_model.predict(padded_sequence)
     # predicted_label_index = np.argmax(Seq_Predicted)
 
     # ----------- Debug Logs -----------
     logistic_debug = decodedLabel(int(Logistic_Predicted[0]))
     svc_debug = decodedLabel(int(SVM_Predicted[0]))
-    predicted_label_index = np.argmax(Seq_Predicted)
+    # predicted_label_index = np.argmax(Seq_Predicted)
     print('Logistic', int(Logistic_Predicted[0]), logistic_debug)
     print('SVM', int(SVM_Predicted[0]), svc_debug)
     
     return {
             'Logistic_Predicted':decodedLabel(int(Logistic_Predicted[0])),
             'SVM_Predicted': decodedLabel(int(SVM_Predicted[0])),
-            'LSTM': decodedLabel(int(predicted_label_index)),
+            # 'LSTM': decodedLabel(int(predicted_label_index)),
             'Article_Content': text
         }
 
@@ -209,7 +209,7 @@ if url:
     st.json({
         "Logistic": result.get("Logistic_Predicted"),
         "SVC": result.get("SVM_Predicted"),
-        "LSTM": result.get("LSTM")
+        # "LSTM": result.get("LSTM")
     })
     
 st.divider()  # 👈 Draws a horizontal rule