Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+import joblib
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.calibration import CalibratedClassifierCV
+
+def predict_password_strength(model, vectorizer, password_input):
+    password_tfidf = vectorizer.transform([password_input])
+    
+    # Make predictions
+    predicted_proba = model.predict_proba(password_tfidf)
+    predicted_class = int(model.predict(password_tfidf)[0])  # Convert to Python integer
+    output = ''
+    if predicted_class == 0:
+        output = "The password is very weak..."
+    elif predicted_class == 1:
+        output = "The password is average."
+    else:
+        output = "The password is strong. But alas, it is not unbreakable."
+
+    confidence = float(predicted_proba.max())
+
+    return password_input, output, confidence
+
+model = joblib.load("helix-psa.pkl")
+vectorizer = joblib.load("helix-psa-vectorizer.pkl")
+
+demo = gr.Interface(
+    fn=generateNames,
+    inputs=[gr.Textbox('Hello123', label='Password', info='The password to check the strength of', max_lines=1)],
+    outputs=[gr.Dataframe(row_count = (2, "dynamic"), col_count=(3, "fixed"), label="Generated Names", headers=["Password", "Prediction", "Confidence"])],
+    title='Helix - Password Strength Analyzer',
+    description='A password strength analyzer, trained on over 10 million different passwords.'
+)
+
+demo.launch()