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import joblib
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import pandas as pd
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import gradio as gr
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scaler = joblib.load("models/scaler.joblib")
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models = {
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"processing": joblib.load("models/svm_model_processing.joblib"),
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"perception": joblib.load("models/svm_model_perception.joblib"),
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"input": joblib.load("models/svm_model_input.joblib"),
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"understanding": joblib.load("models/svm_model_understanding.joblib")
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}
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def predict(course_overview, reading_file, abstract_materiale, concrete_material, visual_materials,
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self_assessment, exercises_submit, quiz_submitted, playing, paused, unstarted, buffering):
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try:
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input_data = {
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"course_overview": [course_overview],
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"reading_file": [reading_file],
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"abstract_materiale": [abstract_materiale],
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"concrete_material": [concrete_material],
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"visual_materials": [visual_materials],
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"self_assessment": [self_assessment],
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"exercises_submit": [exercises_submit],
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"quiz_submitted": [quiz_submitted],
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"playing": [playing],
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"paused": [paused],
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"unstarted": [unstarted],
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"buffering": [buffering]
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}
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input_df = pd.DataFrame(input_data)
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input_scaled = scaler.transform(input_df)
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predictions = {}
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for target, model in models.items():
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pred = model.predict(input_scaled)
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predictions[target] = int(pred[0])
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return predictions
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except Exception as e:
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return {"error": str(e)}
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iface = gr.Interface(
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fn=predict,
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inputs=[
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gr.inputs.Number(label="Course Overview"),
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gr.inputs.Number(label="Reading File"),
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gr.inputs.Number(label="Abstract Materiale"),
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gr.inputs.Number(label="Concrete Material"),
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gr.inputs.Number(label="Visual Materials"),
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gr.inputs.Number(label="Self Assessment"),
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gr.inputs.Number(label="Exercises Submit"),
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gr.inputs.Number(label="Quiz Submitted"),
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gr.inputs.Number(label="Playing"),
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gr.inputs.Number(label="Paused"),
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gr.inputs.Number(label="Unstarted"),
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gr.inputs.Number(label="Buffering")
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],
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outputs="json",
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title="SVM Multi-Target Prediction",
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description="Enter the feature values to get predictions for processing, perception, input, and understanding."
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)
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if __name__ == "__main__":
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iface.launch()
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