Update app.py

app.py

@@ -3,20 +3,17 @@ import streamlit as st
 import json
 import requests
 from bs4 import BeautifulSoup
-#from . import SVM_Linear_Model
-#import Logistic_Model
-#from . import vectorizer
-# 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
+# Seq_model = load_model("LSTM.h5") # Sequential
+SVM_Linear_model = joblib.load("SVM_Linear_Kernel") # SVM
 logistic_model = joblib.load("Logistic_Model.joblib") # Logistic
-vectorizer = joblib.load("vectorizer.joblib") # global vocabulary
-# tokenizer = joblib.load(tokenizer)
+vectorizer = joblib.load("vectorizer.joblib") # global vocabulary (used for Logistic, SVC)
+tokenizer = joblib.load("tokenizer.joblib") # used for LSTM
 
+# Web Crawler function
 def crawURL(url):
-    # Fetch the sitemap
+    # Fetch the URL content
     response = requests.get(url)
     # Parse the sitemap HTML
     soup = BeautifulSoup(response.content, 'html.parser')
@@ -61,21 +58,23 @@ def crawURL(url):
         print(f"Failed to crawl page: {url}, Error: {str(e)}")
         return null
 
+# Predict for text category using Models
 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')
+    
     # Get the predicted result from models
-    # Seq_Predicted = Seq_model.predict(padded_sequence)
-    # SVM_Predicted = SVM_model.predict(processed_text).tolist()
     Logistic_Predicted = logistic_model.predict(processed_text).tolist()
+    SVM_Predicted = SVM_model.predict(processed_text).tolist()
+    # Seq_Predicted = Seq_model.predict(padded_sequence)
 
     # 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])
+            'Article_Content': text,
         }
 
 # Using Model to handle and return Category Route
@@ -93,10 +92,9 @@ def categorize(url):
             return {"error_message": error.message}
         else:
             return {"error_message": error}
-        
 
+# Main App
 url = st.text_input("enter your CNN's URL here")
-
 if url:
     result = categorize(url)
     st.json(result)