Update app.py

app.py

@@ -1,56 +1,25 @@
 import pandas as pd
-import gradio as gr
 from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestRegressor
-import joblib
-import os
+from sklearn.metrics import r2_score
 
-# --- Load Dataset ---
-# Make sure you upload your CSV into the Space with the same name
-df = pd.read_csv("course_history_cleaned.csv")
+# ----------------------------
+# Load dataset
+CSV_PATH = "test_score_prediction_dataset.csv"
+df = pd.read_csv(CSV_PATH)
 
-# Convert True/False into integers for modeling
-df["AP"] = df["AP"].astype(int)
-df["Honors"] = df["Honors"].astype(int)
+# Features and target
+X = df[["AP", "Honors", "GPA_Points", "Credits_Earned"]]
+y = df["Predicted_Test_Score"]
 
-# --- Features & Target ---
-X = df[["AP", "Honors", "Credits_Attempted", "Credits_Earned"]]
-y = df["Weighted_GPA_Points"]
+# Train/test split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
-# Train-test split
-X_train, X_test, y_train, y_test = train_test_split(
-    X, y, test_size=0.2, random_state=42
-)
-
-# --- Train Model (Random Forest as best baseline) ---
+# ----------------------------
+# Train model (Random Forest)
 model = RandomForestRegressor(n_estimators=100, random_state=42)
 model.fit(X_train, y_train)
 
-# Save model for persistence
-joblib.dump(model, "gpa_model.pkl")
-
-# Reload model (useful when restarting Space)
-model = joblib.load("gpa_model.pkl")
-
-# --- Prediction Function ---
-def predict(ap, honors, credits_attempted, credits_earned):
-    features = [[int(ap), int(honors), float(credits_attempted), float(credits_earned)]]
-    prediction = model.predict(features)[0]
-    return round(prediction, 2)
-
-# --- Gradio UI ---
-demo = gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Checkbox(label="AP"),
-        gr.Checkbox(label="Honors"),
-        gr.Number(label="Credits Attempted"),
-        gr.Number(label="Credits Earned")
-    ],
-    outputs=gr.Number(label="Predicted GPA Points"),
-    title="GPA Prediction Model",
-    description="Enter course details to predict GPA points (weighted)."
-)
-
-if __name__ == "__main__":
-    demo.launch()
+# Predict and evaluate
+y_pred = model.predict(X_test)
+print("R² Score:", r2_score(y_test, y_pred))