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| import pandas as pd | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.metrics import accuracy_score, classification_report | |
| import gradio as gr | |
| # 1) Read dataset directly from the same folder as app.py | |
| df = pd.read_csv("german_credit.csv") | |
| # 2) Use Credit Amount as the only independent variable | |
| X = df[["Credit Amount"]] | |
| y = df["Creditability"].astype(int) | |
| # 3) Split data and train a simple logistic regression model | |
| X_train, X_test, y_train, y_test = train_test_split( | |
| X, | |
| y, | |
| test_size=0.20, | |
| random_state=42, | |
| stratify=y | |
| ) | |
| model = LogisticRegression() | |
| model.fit(X_train, y_train) | |
| # 4) Evaluate model | |
| y_pred = model.predict(X_test) | |
| accuracy = accuracy_score(y_test, y_pred) | |
| print(f"Model Accuracy: {accuracy:.2%}") | |
| print("\nClassification Report:") | |
| print(classification_report(y_test, y_pred, zero_division=0)) | |
| print( | |
| f"\nCredit Amount range in dataset: " | |
| f"{X['Credit Amount'].min():,.0f} to {X['Credit Amount'].max():,.0f}" | |
| ) | |
| # 5) Prediction function | |
| def predict_creditability(credit_amount): | |
| credit_amount = float(credit_amount) | |
| input_data = pd.DataFrame( | |
| {"Credit Amount": [credit_amount]} | |
| ) | |
| prediction = int(model.predict(input_data)[0]) | |
| probability = model.predict_proba(input_data)[0] | |
| label = ( | |
| "1 - Creditworthy" | |
| if prediction == 1 | |
| else "0 - Not Creditworthy" | |
| ) | |
| return ( | |
| label, | |
| f"Probability of Creditability = 1: {probability[1]:.2%}", | |
| f"Probability of Creditability = 0: {probability[0]:.2%}" | |
| ) | |
| # 6) Gradio web application | |
| demo = gr.Interface( | |
| fn=predict_creditability, | |
| inputs=gr.Number( | |
| label="Credit Amount", | |
| value=2500 | |
| ), | |
| outputs=[ | |
| gr.Textbox(label="Predicted Creditability"), | |
| gr.Textbox(label="Probability: Creditability = 1"), | |
| gr.Textbox(label="Probability: Creditability = 0") | |
| ], | |
| title="Creditability Prediction", | |
| description=( | |
| "Enter a credit amount to predict Creditability (0 or 1). " | |
| "The model uses Credit Amount as the only independent variable " | |
| "and Logistic Regression for binary classification." | |
| ), | |
| examples=[ | |
| [500], | |
| [1500], | |
| [3000], | |
| [5000], | |
| [10000] | |
| ] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch( | |
| server_name="0.0.0.0", | |
| server_port=7860 | |
| ) | |