Create app.py

app.py

@@ -0,0 +1,73 @@
+import gradio as gr
+import pandas as pd
+import joblib
+
+# Load model & columns
+model = joblib.load("student_gpa_model.pkl")
+columns = joblib.load("columns.pkl")
+
+def predict_gpa(
+    Age,
+    Gender,
+    Ethnicity,
+    ParentalEducation,
+    StudyTimeWeekly,
+    Absences,
+    Tutoring,
+    ParentalSupport,
+    Extracurricular,
+    Sports,
+    Music,
+    Volunteering
+):
+    data = {
+        "Age": Age,
+        "Gender": Gender,
+        "Ethnicity": Ethnicity,
+        "ParentalEducation": ParentalEducation,
+        "StudyTimeWeekly": StudyTimeWeekly,
+        "Absences": Absences,
+        "Tutoring": Tutoring,
+        "ParentalSupport": ParentalSupport,
+        "Extracurricular": Extracurricular,
+        "Sports": Sports,
+        "Music": Music,
+        "Volunteering": Volunteering,
+    }
+
+    df = pd.DataFrame([data])
+    df = pd.get_dummies(df)
+    df = df.reindex(columns=columns, fill_value=0)
+
+    prediction = model.predict(df)[0]
+    return round(float(prediction), 2)
+
+app = gr.Interface(
+    fn=predict_gpa,
+    inputs=[
+        gr.Number(label="Age"),
+        gr.Dropdown(["Male", "Female"], label="Gender"),
+        gr.Dropdown(
+            ["Group A", "Group B", "Group C", "Group D", "Group E"],
+            label="Ethnicity"
+        ),
+        gr.Dropdown(
+            ["High School", "Associate", "Bachelor", "Master"],
+            label="Parental Education"
+        ),
+        gr.Number(label="Weekly Study Time (hours)"),
+        gr.Number(label="Absences"),
+        gr.Dropdown(["Yes", "No"], label="Tutoring"),
+        gr.Dropdown(["Low", "Medium", "High"], label="Parental Support"),
+        gr.Dropdown(["Yes", "No"], label="Extracurricular"),
+        gr.Dropdown(["Yes", "No"], label="Sports"),
+        gr.Dropdown(["Yes", "No"], label="Music"),
+        gr.Dropdown(["Yes", "No"], label="Volunteering"),
+    ],
+    outputs=gr.Number(label="Predicted GPA"),
+    title="Student GPA Predictor",
+    description="ML model to predict student GPA based on academic & lifestyle factors"
+)
+
+if __name__ == "__main__":
+    app.launch(server_name="0.0.0.0", server_port=7860)