Update app.py

app.py

@@ -9,11 +9,17 @@ from sklearn.linear_model import LogisticRegression
 try:
     # --- Load and inference code ---
     with open('password_model.pkl', 'rb') as f:
-      model = pickle.load(f)
+        model = pickle.load(f)
 
     with open('password_vectorizer.pkl', 'rb') as f:
-      vectorizer = pickle.load(f)
+        vectorizer = pickle.load(f)
 
+except Exception as e:
+    print(f"Error loading model/vectorizer: {e}")
+    model = None
+    vectorizer = None
+
+# Function to extract features
 def extract_features(password):
     features = {}
     features['length'] = len(password)
@@ -23,45 +29,48 @@ def extract_features(password):
     features['special'] = sum(1 for c in password if not c.isalnum())
     return features
 
-def predict_password_strength(password, vectorizer, model): # Add vectorizer and model as arguments
-    # Extract features from the input password
-    features = extract_features(password)
-
-    # Transform the input password using the trained vectorizer
-    password_vectorized = vectorizer.transform([password])
-    password_vectorized = hstack((password_vectorized, np.array(list(features.values())).reshape(1, -1)))
+# Function to predict password strength
+def predict_password_strength(password):
+    if not model or not vectorizer:
+        return ["", "", "Model or vectorizer not loaded correctly"]
 
-    text="No password analyzed."
+    try:
+        # Extract features from the input password
+        features = extract_features(password)
 
-    # Make a prediction using the trained model
-    prediction = model.predict(password_vectorized)[0]
-    if prediction === 0:
-        text = "Password is very weak."
-    elif prediction === 1:
-        text = "Password is weak."
-    elif prediction === 2:
-        text = "Password is average."
-    elif prediction === 3:
-        text = "Password is strong."
-    elif prediction === 4:
-        text = "Password is very strong."
+        # Transform the input password using the trained vectorizer
+        password_vectorized = vectorizer.transform([password])
+        password_vectorized = hstack((password_vectorized, np.array(list(features.values())).reshape(1, -1)))
 
-    return password, prediction, text
+        # Make a prediction using the trained model
+        prediction = model.predict(password_vectorized)[0]
+        if prediction == 0:
+            text = "Password is very weak."
+        elif prediction == 1:
+            text = "Password is weak."
+        elif prediction == 2:
+            text = "Password is average."
+        elif prediction == 3:
+            text = "Password is strong."
+        elif prediction == 4:
+            text = "Password is very strong."
+        else:
+            text = "Unknown strength level."
 
+        return [password, prediction, text]
     except Exception as e:
-        return [[f"Error during prediction: {e}", "", ""]]
+        return [password, "", f"Error during prediction: {e}"]
 
+# Gradio Interface
 demo = gr.Interface(
     fn=predict_password_strength,
-    inputs=[gr.Textbox('Hello123', label='Password', info='The password to check the strength of', max_lines=1)],
-    outputs=[
-        gr.Dataframe(
-            row_count=(1, "fixed"),
-            col_count=(3, "fixed"),
-            headers=["Password", "Prediction", "Strength_Text"],
-            label="Password Strength Analysis"
-        )
-    ],
+    inputs=gr.Textbox('Hello123', label='Password', info='The password to check the strength of', max_lines=1),
+    outputs=gr.Dataframe(
+        row_count=(1, "fixed"),
+        col_count=(3, "fixed"),
+        headers=["Password", "Prediction", "Strength_Text"],
+        label="Password Strength Analysis"
+    ),
     title='Helix - Password Strength Analyzer',
     description='A password strength analyzer, trained on over 5 million different passwords.'
 )