Update app.py

app.py

@@ -1,12 +1,11 @@
 import pandas as pd
-from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import train_test_split
 from sklearn.metrics import r2_score
+import gradio as gr
 
-# ----------------------------
 # Load dataset
-CSV_PATH = "test_score_prediction_dataset.csv"
-df = pd.read_csv(CSV_PATH)
+df = pd.read_csv("test_score_prediction_dataset.csv")
 
 # Features and target
 X = df[["AP", "Honors", "GPA_Points", "Credits_Earned"]]
@@ -14,12 +13,38 @@ y = df["Predicted_Test_Score"]
 
 # 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)
 model = RandomForestRegressor(n_estimators=100, random_state=42)
 model.fit(X_train, y_train)
 
-# Predict and evaluate
-y_pred = model.predict(X_test)
-print("R² Score:", r2_score(y_test, y_pred))
+# Accuracy
+train_r2 = r2_score(y_train, model.predict(X_train))
+test_r2 = r2_score(y_test, model.predict(X_test))
+
+# Prediction function
+def predict_test_score(ap, honors, gpa_points, credits_earned):
+    features = [[ap, honors, gpa_points, credits_earned]]
+    prediction = model.predict(features)[0]
+    return round(prediction, 2), round(train_r2, 3), round(test_r2, 3)
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 📊 Test Score Predictor")
+
+    with gr.Row():
+        ap = gr.Checkbox(label="AP")
+        honors = gr.Checkbox(label="Honors")
+        gpa_points = gr.Number(label="GPA Points")
+        credits_earned = gr.Number(label="Credits Earned")
+
+    output_score = gr.Number(label="Predicted Test Score")
+    output_train = gr.Number(label="Training R² Score")
+    output_test = gr.Number(label="Testing R² Score")
+
+    btn = gr.Button("Predict")
+    btn.click(
+        predict_test_score,
+        inputs=[ap, honors, gpa_points, credits_earned],
+        outputs=[output_score, output_train, output_test]
+    )
+
+demo.launch()