Create app.py

app.py

@@ -0,0 +1,42 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from catboost import CatBoostClassifier
+
+# Load the trained model
+@st.cache_resource
+def load_model():
+    model = CatBoostClassifier()
+    model.load_model('model.cbm')  # Ensure you have saved your model as 'model.cbm'
+    return model
+
+def main():
+    st.title('San Francisco Crime Predictor')
+    
+    # Input form
+    st.sidebar.header('Input Parameters')
+    hour = st.sidebar.slider('Hour of Day', 0, 23, 12)
+    month = st.sidebar.slider('Month', 1, 12, 6)
+    day_of_week = st.sidebar.selectbox('Day of Week', 
+                                       ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 
+                                        'Friday', 'Saturday', 'Sunday'])
+    pd_district = st.sidebar.selectbox('Police District',
+                                       ['NORTHERN', 'SOUTHERN', 'MISSION', 'CENTRAL',
+                                        'PARK', 'RICHMOND', 'TARAVAL', 'INGLESIDE',
+                                        'BAYVIEW', 'TENDERLOIN'])
+    x = st.sidebar.number_input('Longitude', value=-122.42)
+    y = st.sidebar.number_input('Latitude', value=37.77)
+    
+    # Encode categorical inputs
+    day_of_week_encoded = pd.Categorical([day_of_week], categories=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']).codes[0]
+    pd_district_encoded = pd.Categorical([pd_district], categories=['NORTHERN', 'SOUTHERN', 'MISSION', 'CENTRAL', 'PARK', 'RICHMOND', 'TARAVAL', 'INGLESIDE', 'BAYVIEW', 'TENDERLOIN']).codes[0]
+    
+    # Make prediction
+    if st.button('Predict Crime Category'):
+        model = load_model()
+        input_data = np.array([[hour, month, day_of_week_encoded, pd_district_encoded, x, y]])
+        prediction = model.predict(input_data)
+        st.write(f'Predicted Crime Category: {prediction[0]}')
+
+if __name__ == '__main__':
+    main()