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app.py
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app.py
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# Import the necessary libraries
import streamlit as st
from fastapi import FastAPI
import joblib
# Load the pre-trained model
model = joblib.load('student-performance-model.pkl')
# Create the FastAPI app
app = FastAPI()
# Define the endpoint for the model
@app.post("/predict")
def predict(data: List[float]):
'''
This function uses the serves the model to make a prediction
using the input data
'''
prediction = model.predict(data)
return {"prediction": prediction}
# Create the Streamlit app
def main():
'''
This is the principal function used to run the streamlit frontend
'''
st.title("Binary Classifier Model for Student performance")
#add a home page whicha ask to login as admin or user
menu = ["Home", "Login", "SignUp"]
choice = st.sidebar.selectbox("Menu", menu)
st.markdown("Enter the input data below to get a prediction")
# Get the input data from the user
data = st.text_input("Input data (comma-separated):")
data = [float(x) for x in data.split(",")]
# Use the FastAPI app to make a prediction
response = app.post("/predict", data=data)
prediction = response.json()["prediction"]
# Display the result to the user
st.write(f"Prediction: {prediction}")
# Run the Streamlit app
if __name__ == '__main__':
main()