Update app.py

app.py

@@ -7,17 +7,17 @@ from tensorflow.keras.models import load_model # load a pre-trained Keras model
 import numpy as np # scientific computing in Python
 import streamlit as st
 
-from . import SVM_Linear_Model
+# from . import SVM_Linear_Model
 from . import Logistic_Model
 from . import vectorizer
-from . import tokenizer
+# from . import tokenizer
 
 # load all the models and vectorizer (global vocabulary)
 # Seq_model = load_model('./LSTM.h5') # Sequential
-SVM_Linear_model = joblib.load(SVM_Linear_Model) # SVM
+# SVM_Linear_model = joblib.load(SVM_Linear_Model) # SVM
 logistic_model = joblib.load(Logistic_Model) # Logistic
 vectorizer = joblib.load(vectorizer) # global vocabulary
-tokenizer = joblib.load(tokenizer)
+# tokenizer = joblib.load(tokenizer)
 
 def crawURL(url):
     print(f"Crawling page: {url}")
@@ -69,17 +69,17 @@ 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
     # Seq_Predicted = Seq_model.predict(padded_sequence)
-    SVM_Predicted = SVM_model.predict(processed_text).tolist()
+    # SVM_Predicted = SVM_model.predict(processed_text).tolist()
     Logistic_Predicted = logistic_model.predict(processed_text).tolist()
 
     # predicted_label_index = np.argmax(Seq_Predicted)
     return {
             'Article_Content': text,
-            'SVM_Predicted': int(SVM_Predicted[0]),
+            # 'SVM_Predicted': int(SVM_Predicted[0]),
             'Logistic_Predicted': int(Logistic_Predicted[0])
         }