remove url parameter

extraction_features.py

@@ -15,7 +15,7 @@ def extract_features(url):
     features = {}
     
     # URL 
-    features['URL'] = url
+    # features['URL'] = url
     
     # URLLength
     features['URLLength'] = len(url)

label_encoders.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c355826d22387c337f5bf8ca6ae18fe881c1b3cc2465772f740fa3c49b726469
-size 17633409
+oid sha256:3db2d4c2f44297075aeec0b104794135b2bae52288f102b21b35fd7b45649d2e
+size 6961649

logistic_regression_model.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8d3509d063e937fc6c03b386580246dfbd60827c871dc6394115bde984e62052
-size 1483
+oid sha256:3a7bc11b6b53b1a34501d639a64111d205d408e50fd5ba09bbfdb8018434e2cf
+size 1451

main.py

@@ -29,7 +29,7 @@ model = joblib.load('logistic_regression_model.pkl')
 label_encoders = joblib.load('label_encoders.pkl')
 # Columns used in the model
 selected_columns = [
-    'URL', 'URLLength', 'Domain', 'DomainLength', 'TLD', 
+    'URLLength', 'Domain', 'DomainLength', 'TLD', 
     'CharContinuationRate', 'TLDLength', 'NoOfSubDomain', 
     'DegitRatioInURL', 'SpacialCharRatioInURL', 'IsHTTPS'
 ]
@@ -91,7 +91,7 @@ async def predict(request: Request, requess: Req = Depends(form_req)):
     dataFrame_features = pd.DataFrame([features])
 
     # Apply features encoding (convert everything into int64)
-    for column in ['URL', 'Domain', 'TLD']:
+    for column in ['Domain', 'TLD']:
         encoder = label_encoders[column]
         dataFrame_features[column] = dataFrame_features[column].apply(lambda x: safe_transform(encoder, x))