Update app.py

app.py

@@ -1,61 +1,36 @@
-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.
-""")
+from flask import Flask, request, jsonify
+import joblib
+import pandas as pd
+
+app = Flask(__name__)
+
+# Load models and scaler
+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"),
+}
+scaler = joblib.load("scaler.joblib")
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    try:
+        # Parse input data from JSON
+        input_data = request.json
+        df = pd.DataFrame([input_data])
+
+        # Scale the data
+        df_scaled = scaler.transform(df)
+
+        # Make predictions for all target variables
+        predictions = {}
+        for target, model in models.items():
+            predictions[target] = model.predict(df_scaled)[0]
+
+        return jsonify({"success": True, "predictions": predictions})
+    except Exception as e:
+        return jsonify({"success": False, "error": str(e)})
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=8000)