File size: 1,607 Bytes
8a6b3d6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 |
import gradio as gr
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
# Define Gradio interface
interface = gr.Interface(fn=predict,
inputs=gr.Dataframe(type="numpy", row_count=1, col_count=12,
headers=['course overview', 'reading file', 'abstract materiale',
'concrete material', 'visual materials', 'self-assessment',
'exercises submit', 'quiz submitted', 'playing', 'paused',
'unstarted', 'buffering']),
outputs=gr.JSON(),
live=True)
# Launch the interface
interface.launch(share=True) |