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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()
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