Update app.py

app.py

@@ -4,29 +4,40 @@ from sklearn.model_selection import train_test_split
 from sklearn.metrics import r2_score
 import gradio as gr
 
-# Load dataset
-df = pd.read_csv("test_score_prediction_dataset.csv")
+# -------- Initial Training --------
+def train_model(csv_path="test_score_prediction_dataset.csv"):
+    df = pd.read_csv(csv_path)
+    X = df[["AP", "Honors", "GPA_Points", "Credits_Earned"]]
+    y = df["Predicted_Test_Score"]
 
-# Features and target
-X = df[["AP", "Honors", "GPA_Points", "Credits_Earned"]]
-y = df["Predicted_Test_Score"]
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.2, random_state=42
+    )
+
+    model = RandomForestRegressor(n_estimators=100, random_state=42)
+    model.fit(X_train, y_train)
+
+    train_r2 = r2_score(y_train, model.predict(X_train))
+    test_r2 = r2_score(y_test, model.predict(X_test))
 
-# Train/test split
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-model = RandomForestRegressor(n_estimators=100, random_state=42)
-model.fit(X_train, y_train)
+    return model, round(train_r2, 3), round(test_r2, 3)
 
-# Accuracy
-train_r2 = r2_score(y_train, model.predict(X_train))
-test_r2 = r2_score(y_test, model.predict(X_test))
+# Global model
+model, train_r2, test_r2 = train_model()
 
-# Prediction function
+# -------- 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)
+    return round(prediction, 2), train_r2, test_r2
 
-# Gradio UI
+# -------- Retraining with Uploaded CSV --------
+def retrain_with_csv(file):
+    global model, train_r2, test_r2
+    model, train_r2, test_r2 = train_model(file.name)
+    return f"✅ Model retrained successfully!\nTraining R²: {train_r2}, Testing R²: {test_r2}"
+
+# -------- Gradio UI --------
 with gr.Blocks() as demo:
     gr.Markdown("# 📊 Test Score Predictor")
 
@@ -47,4 +58,10 @@ with gr.Blocks() as demo:
         outputs=[output_score, output_train, output_test]
     )
 
+    gr.Markdown("### 🔄 Upload New Dataset to Retrain")
+    file_upload = gr.File(file_types=[".csv"], label="Upload CSV")
+    retrain_output = gr.Textbox(label="Retrain Status")
+    file_upload.upload(retrain_with_csv, inputs=file_upload, outputs=retrain_output)
+
 demo.launch()
+