app.py

import joblib
import pandas as pd
import gradio as gr

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

def predict(course_overview, reading_file, abstract_materiale, concrete_material, visual_materials,
           self_assessment, exercises_submit, quiz_submitted, playing, paused, unstarted, buffering):
    try:
        input_data = {
            "course overview": [course_overview],
            "reading file": [reading_file],
            "abstract materiale": [abstract_materiale],
            "concrete material": [concrete_material],
            "visual materials": [visual_materials],
            "self-assessment": [self_assessment],
            "exercises submit": [exercises_submit],
            "quiz submitted": [quiz_submitted],
            "playing": [playing],
            "paused": [paused],
            "unstarted": [unstarted],
            "buffering": [buffering]
        }

        input_df = pd.DataFrame(input_data)
        input_scaled = scaler.transform(input_df)

        predictions = {}
        for target, model in models.items():
            pred = model.predict(input_scaled)
            predictions[target] = pred[0]  # Return as is, without converting to int

        return predictions

    except Exception as e:
        return {"error": str(e)}

# Define Gradio interface using the latest syntax
iface = gr.Interface(
    fn=predict,
    inputs=[
        gr.Number(label="Course Overview"),
        gr.Number(label="Reading File"),
        gr.Number(label="Abstract Materiale"),
        gr.Number(label="Concrete Material"),
        gr.Number(label="Visual Materials"),
        gr.Number(label="Self Assessment"),
        gr.Number(label="Exercises Submit"),
        gr.Number(label="Quiz Submitted"),
        gr.Number(label="Playing"),
        gr.Number(label="Paused"),
        gr.Number(label="Unstarted"),
        gr.Number(label="Buffering")
    ],
    outputs=gr.JSON(),
    title="SVM Multi-Target Prediction",
    description="Enter the feature values to get predictions for processing, perception, input, and understanding."
)

if __name__ == "__main__":
    iface.launch()