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app.py

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+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.
+""")

requirements.txt

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+joblib
+gradio>=3.0,<4.0
+numpy>=1.21.0
+scikit-learn==1.5.2
+pandas

scaler.joblib

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+oid sha256:2d40f613f3b8b7bd9b51dc6b13631dd07ebdf6b373e41e6c5fd9d7cf20af814d
+size 1431

svm_model_input.joblib

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+oid sha256:424a08119df19f4109d23e0d5f17084f0d3520c6a3c7eb2b137290dbf07e8d41
+size 34539

svm_model_perception.joblib

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+size 30139

svm_model_processing.joblib

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+oid sha256:7f6e8e1f74fb2d1f78144fd1cc820aa3af538459b07429d2e82abb9a3d2e5a2d
+size 68651

svm_model_understanding.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1dac2d4345f1e12fb913694390fa16a77ab781ad209e8433db4dc98d3d132423
+size 53451