app.py

import streamlit as st
import joblib
import numpy as np

# Load the scaler and models
scaler = joblib.load('scaler.joblib')
models = {
    "processing": joblib.load('svm_model_processing.joblib'),
    "perception": joblib.load('svm_model_perception.joblib'),
    "input": joblib.load('svm_model_input.joblib'),
    "understanding": joblib.load('svm_model_understanding.joblib'),
}

# Define the prediction function
def predict(user_input):
    # Ensure the input is in the same order as your model expects
    user_input_array = np.array(user_input).reshape(1, -1)

    # Scale the input using the saved scaler
    user_input_scaled = scaler.transform(user_input_array)

    # Predict outcomes for all target variables
    predictions = {}
    for target, model in models.items():
        prediction = model.predict(user_input_scaled)
        predictions[target] = prediction[0]

    return predictions

# Streamlit UI
st.title("ML Prediction Application")
st.header("Input your data for predictions")

# Create input fields for user input
columns = [
    'Course Overview', 'Reading File', 'Abstract Materiale', 
    'Concrete Material', 'Visual Materials', 'Self-Assessment', 
    'Exercises Submit', 'Quiz Submitted', 'Playing', 'Paused', 
    'Unstarted', 'Buffering'
]

user_input = []
for col in columns:
    value = st.number_input(f"{col}", value=0.0)
    user_input.append(value)

# Button for making predictions
if st.button("Predict"):
    # Ensure proper input and predict
    try:
        predictions = predict(user_input)
        st.subheader("Predictions")
        st.json(predictions)
    except Exception as e:
        st.error(f"An error occurred: {e}")

# Share instructions for deployment
st.markdown("""
    - To run the app, execute `streamlit run app.py` in your terminal.
    - Make sure the `scaler.joblib` and model files are in the same directory as this script.
""")