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fae0453 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | import streamlit as st
import joblib
import numpy as np
# Load the saved model and encoders
xgb_model = joblib.load('xgb_model.joblib')
label_encoder_X = joblib.load('label_encoder_X.joblib')
label_encoder_y = joblib.load('label_encoder_y.joblib')
# Define the Streamlit app
def main():
st.title("Injury Type Prediction")
# Get user input for features
age = st.number_input("Age", min_value=0, max_value=120, step=1)
sex = st.number_input("Sex", min_value=1, max_value=3)
present_city = st.text_input("Present City")
# Transform categorical inputs using encoders
# Validate user input for present city
if present_city not in label_encoder_X.classes_:
st.warning("Unrecognized city. Please enter a valid city.")
return
# Transform inputs
present_city_encoded = label_encoder_X.transform([present_city])[0]
# Prepare input data
input_data = np.array([[age, sex, present_city_encoded]])
# Make predictions
if st.button("Predict"):
# Use the trained model to make predictions
prediction = xgb_model.predict(input_data)
# Decode the prediction
predicted_injury_type = label_encoder_y.inverse_transform(prediction)
if(predicted_injury_type[0]==2):
st.write("Predicted Injury Type: Fatal")
elif(predicted_injury_type[0]==3):
st.write("Predicted Injury Type: Minor")
elif(predicted_injury_type[0]==1):
st.write("Predicted Injury Type: Grievous")
elif(predicted_injury_type==4):
st.write("Predicted Injury Type: Not Applicable")
else:
st.write("Predicted Injury Type: Abused")
if __name__ == "__main__":
main()
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