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| import gradio as gr | |
| import pandas as pd | |
| import joblib | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| import shap | |
| import numpy as np | |
| from io import BytesIO | |
| # ------------------------------- | |
| # Load Model | |
| # ------------------------------- | |
| model = joblib.load("model/performance_pipeline.pkl") | |
| categorical_features = ['school','sex','address','famsize','Pstatus','Mjob','Fjob','reason','guardian', | |
| 'schoolsup','famsup','paid','activities','nursery','higher','internet','romantic','dataset'] | |
| numeric_features = ['age','Medu','Fedu','traveltime','studytime','failures','famrel','freetime', | |
| 'goout','Dalc','Walc','health','absences','G1','G2','G3'] | |
| # ------------------------------- | |
| # Single Prediction Function | |
| # ------------------------------- | |
| def single_prediction(*inputs): | |
| # Map inputs to dataframe | |
| data = dict(zip(categorical_features + numeric_features, inputs)) | |
| df = pd.DataFrame([data]) | |
| if 'dataset' not in df.columns: | |
| df['dataset'] = 'student_mat' | |
| pred = model.predict(df)[0] | |
| return f"Predicted Performance: {pred}" | |
| # ------------------------------- | |
| # Batch Prediction Function | |
| # ------------------------------- | |
| def batch_prediction(file): | |
| df = pd.read_csv(file.name) | |
| if 'dataset' not in df.columns: | |
| df['dataset'] = 'student_mat' | |
| preds = model.predict(df) | |
| df["Prediction"] = preds | |
| # Plot counts | |
| pred_counts = df["Prediction"].value_counts() | |
| fig, ax = plt.subplots() | |
| sns.barplot(x=pred_counts.index, y=pred_counts.values, palette="coolwarm", ax=ax) | |
| ax.set_ylabel("Count") | |
| # Save plot to buffer | |
| buf = BytesIO() | |
| plt.savefig(buf, format="png") | |
| buf.seek(0) | |
| return df.head(), buf | |
| # ------------------------------- | |
| # Gradio Interfaces | |
| # ------------------------------- | |
| # Single prediction UI | |
| single_inputs = [] | |
| for col in categorical_features: | |
| if col == 'school': | |
| single_inputs.append(gr.Dropdown(["GP", "MS"], label=col)) | |
| elif col == 'address': | |
| single_inputs.append(gr.Dropdown(["U", "R"], label=col)) | |
| elif col == 'famsize': | |
| single_inputs.append(gr.Dropdown(["GT3", "LE3"], label=col)) | |
| elif col == 'Pstatus': | |
| single_inputs.append(gr.Dropdown(["T", "A"], label=col)) | |
| elif col in ['Mjob','Fjob']: | |
| single_inputs.append(gr.Dropdown(["teacher","health","services","at_home","other"], label=col)) | |
| elif col == 'reason': | |
| single_inputs.append(gr.Dropdown(["home","reputation","course","other"], label=col)) | |
| elif col == 'guardian': | |
| single_inputs.append(gr.Dropdown(["mother","father","other"], label=col)) | |
| elif col in ['schoolsup','famsup','paid','activities','nursery','higher','internet','romantic']: | |
| single_inputs.append(gr.Dropdown(["yes", "no"], label=col)) | |
| else: | |
| single_inputs.append(gr.Textbox(label=col)) | |
| for col in numeric_features: | |
| single_inputs.append(gr.Number(label=col)) | |
| single_demo = gr.Interface( | |
| fn=single_prediction, | |
| inputs=single_inputs, | |
| outputs="text", | |
| title="🎓 StudentPass - Single Prediction" | |
| ) | |
| # Batch prediction UI | |
| batch_demo = gr.Interface( | |
| fn=batch_prediction, | |
| inputs=gr.File(label="Upload CSV"), | |
| outputs=[gr.Dataframe(), gr.Image(type="pil")], | |
| title="🎓 StudentPass - Batch Prediction" | |
| ) | |
| # Combine into Tabs | |
| demo = gr.TabbedInterface([single_demo, batch_demo], ["Single Prediction", "Batch Prediction"]) | |
| if __name__ == "__main__": | |
| demo.launch() | |