Create app.py

app.py

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+import streamlit as st
+import joblib
+import numpy as np
+import string
+
+# Load the pipeline and label mapping
+pipeline = joblib.load("ensemble_pipeline.joblib")
+label_map = joblib.load("label_map.joblib")
+# Create a reverse mapping for nice output
+reverse_label_map = {v: k for k, v in label_map.items()}
+
+# App title and description
+st.title("Password Strength Predictor")
+st.write("""
+Enter a password below to see its predicted strength (Weak, Medium, or Strong)
+using a pre-trained ensemble classifier.
+""")
+
+# Text input for the password
+password = st.text_input("Enter a Password:")
+
+# Optional: Define the same numerical feature function if needed
+def generate_numerical_features(pwd):
+    return np.array([
+        len(pwd),
+        sum(1 for char in pwd if char.islower()) / max(1, len(pwd)),
+        sum(1 for char in pwd if char.isupper()) / max(1, len(pwd)),
+        sum(1 for char in pwd if char.isdigit()) / max(1, len(pwd)),
+        sum(1 for char in pwd if not char.isalnum()) / max(1, len(pwd)),
+        int(any(char in string.punctuation for char in pwd))
+    ])
+
+# Prediction action
+if st.button("Predict Strength"):
+    if password:
+        # Use the pipeline to predict directly from the raw password
+        pred_numeric = pipeline.predict([password])
+        # Convert numeric output to a human-readable label using the reverse mapping
+        pred_label = reverse_label_map.get(pred_numeric[0], "Unknown")
+        st.success(f"Predicted Password Strength: **{pred_label}**")
+    else:
+        st.warning("Please enter a password to get a prediction.")