import streamlit as st
import pickle
from sklearn.ensemble import RandomForestRegressor
import numpy as np

# Load the saved model
with open('rf_model.pkl', 'rb') as file:
    model = pickle.load(file)

# Define the function to make predictions
def make_prediction(model, input_data):
    prediction = model.predict(input_data)
    return prediction

# Define the function to calculate GPA
def calculate_gpa(score):
    if score >= 70:
        return 'A (5 points)'
    elif score >= 60:
        return 'B (4 points)'
    elif score >= 50:
        return 'C (3 points)'
    elif score >= 45:
        return 'D (2 points)'
    else:
        return 'F (0 points)'

# Create the Streamlit app
def main():
    # Set page title and configure layout
    st.set_page_config(page_title="Exam Score Prediction", layout="wide")

    # Add a title and description
    st.title("Exam Score Prediction")
    st.markdown(
        "This app predicts exam scores based on input features such as level, course units, attendance, mid-semester score, and assignments."
    )

    # Create input fields
    col1, col2 = st.columns(2)
    with col1:
        level = st.number_input("Level", min_value=200, max_value=400, step=1)
        course_units = st.number_input("Course Units", min_value=1, max_value=4, step=1)
    with col2:
        attendance = st.slider("Attendance", min_value=1, max_value=10, step=1)
        mid_semester = st.slider("Mid Semester Score", min_value=1, max_value=20, step=1)
        assignments = st.slider("Assignments", min_value=1, max_value=10, step=1)

    # Create input data
    input_data = np.array([[level, course_units, attendance, mid_semester, assignments]])

    # Make predictionS
    if st.button("Predict Exam Score"):
        prediction = make_prediction(model, input_data)
        st.write(f"Predicted Exam Score: {prediction[0]:.2f}")
        
        # Calculate GPA
        gpa = calculate_gpa(prediction[0])
        st.write(f"Predicted GPA: {gpa}")

if __name__ == '__main__':
    main()