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| import gradio as gr | |
| import joblib | |
| import os | |
| import pandas as pd | |
| # Path to the model scores file | |
| model_scores_file = "report/model_summary_report_6_smote.csv" | |
| # Load model performance metrics from the provided CSV file | |
| if os.path.exists(model_scores_file): | |
| model_scores_df = pd.read_csv(model_scores_file) | |
| required_columns = {'Model Name', 'Model Sensitivity', 'Model Specificity'} | |
| if required_columns.issubset(model_scores_df.columns): | |
| model_performance = model_scores_df.set_index('Model Name')[['Model Sensitivity', 'Model Specificity']].T.to_dict() | |
| else: | |
| raise ValueError(f"The file '{model_scores_file}' must contain the columns: {required_columns}") | |
| else: | |
| raise FileNotFoundError(f"The model scores file '{model_scores_file}' was not found. Please ensure it exists in the 'report/' directory.") | |
| # Dictionary containing the model names and corresponding pickle file names | |
| model_paths = { | |
| 'AdaBoost': 'pjas-thyroid-AdaBoost.pkl', | |
| 'Decision Tree': 'pjas-thyroid-Decision Tree.pkl', | |
| 'Gaussian Naive Bayes': 'pjas-thyroid-Gaussian Naive Bayes.pkl', | |
| 'Gradient Boosting': 'pjas-thyroid-Gradient Boosting.pkl', | |
| 'K-Nearest Neighbors': 'pjas-thyroid-K-Nearest Neighbors.pkl', | |
| 'Logistic Regression': 'pjas-thyroid-Logistic Regression.pkl', | |
| 'Random Forest': 'pjas-thyroid-Random Forest.pkl', | |
| 'Support Vector Machine': 'pjas-thyroid-Support Vector Machine.pkl', | |
| 'XGBoost': 'pjas-thyroid-XGBoost.pkl' | |
| } | |
| # Preload all models at startup | |
| loaded_models = {} | |
| for model_name, pickle_file in model_paths.items(): | |
| model_file_path = os.path.join("model", pickle_file) | |
| if os.path.exists(model_file_path): | |
| try: | |
| loaded_models[model_name] = joblib.load(model_file_path) | |
| except Exception as e: | |
| print(f"Error loading {model_name}: {e}") | |
| else: | |
| print(f"Model file for {model_name} not found.") | |
| def predict_cancer(age, gender, T, N, Focality, Response): | |
| if not (1 <= age <= 100): | |
| return "🔴 **Error:** Age must be between 1 and 100." | |
| gender_val = 0 if gender == "Female" else 1 | |
| response_val = int(Response) | |
| T_val = int(T) | |
| N_val = int(N) | |
| Focality_val = int(Focality) | |
| features = pd.DataFrame({ | |
| 'Age': [age], | |
| 'Gender': [gender_val], | |
| 'T': [T_val], | |
| 'N': [N_val], | |
| 'Focality': [Focality_val], | |
| 'Response': [response_val] | |
| }) | |
| scaler_file = "model/pjas-thyroid-Scaler.pkl" | |
| if not os.path.exists(scaler_file): | |
| return "🔴 **Error:** Scaler file not found. Please contact the administrator." | |
| scaler = joblib.load(scaler_file) | |
| features[['Age']] = scaler.transform(features[['Age']]) | |
| sorted_model_names = sorted( | |
| model_performance.keys(), | |
| key=lambda m: model_performance[m]['Model Sensitivity'], | |
| reverse=True | |
| ) | |
| # HTML table header with colored columns | |
| table_header = """ | |
| <table> | |
| <thead> | |
| <tr> | |
| <th>Model</th> | |
| <th style="color:red;">Recurrence Accuracy (%)</th> | |
| <th style="color:green;">Non-Recurrence Accuracy (%)</th> | |
| <th>Prediction</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| """ | |
| table_rows = [] | |
| can_recur_emoji = "🔴" # "can recur" | |
| cannot_recur_emoji = "🟢" # "cannot-recur" | |
| for model_name in sorted_model_names: | |
| model = loaded_models.get(model_name) | |
| if not model: | |
| row = f"<tr><td>{model_name}</td><td>N/A</td><td>N/A</td><td>Error: Model not loaded</td></tr>" | |
| table_rows.append(row) | |
| continue | |
| try: | |
| prediction = model.predict(features) | |
| pred_value = prediction[0] | |
| if pred_value == 1: | |
| pred_text = f"{can_recur_emoji} Can recur" | |
| else: | |
| pred_text = f"{cannot_recur_emoji} Cannot-recur" | |
| sensitivity = model_performance[model_name]['Model Sensitivity'] | |
| specificity = model_performance[model_name]['Model Specificity'] | |
| row = f"<tr><td>{model_name}</td><td>{sensitivity:.1f}%</td><td>{specificity:.1f}%</td><td>{pred_text}</td></tr>" | |
| table_rows.append(row) | |
| except Exception as e: | |
| row = f"<tr><td>{model_name}</td><td>N/A</td><td>N/A</td><td>Error: {str(e)}</td></tr>" | |
| table_rows.append(row) | |
| table_footer = "</tbody></table>" | |
| html_table = table_header + "".join(table_rows) + table_footer | |
| success_message = "<br><br>✅ <strong>Prediction completed successfully.</strong>" | |
| return html_table + success_message | |
| def clear_md(): | |
| return "" | |
| # UI Layout | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Thyroid Cancer Recurrence Predictor") | |
| with gr.Row(): | |
| age_slider = gr.Number( | |
| label="Age", | |
| value=44, | |
| interactive=True, | |
| elem_id="age-box", | |
| step=1 | |
| ) | |
| gender_radio = gr.Radio( | |
| choices=["Female", "Male"], | |
| value="Female", | |
| label="Gender", | |
| interactive=True | |
| ) | |
| with gr.Row(): | |
| T_dropdown = gr.Dropdown( | |
| choices=[ | |
| ("T1a (≤1 cm, confined to the thyroid)", "0"), | |
| ("T1b (>1 cm and ≤2 cm, confined to the thyroid)", "1"), | |
| ("T2 (>2 cm and ≤4 cm, confined to the thyroid)", "2"), | |
| ("T3a (>4 cm, confined to the thyroid)", "3"), | |
| ("T3b (Minimal extrathyroidal extension)", "4"), | |
| ("T4a (Moderate extrathyroidal extension, operable)", "5"), | |
| ("T4b (Extensive extrathyroidal extension, inoperable)", "6") | |
| ], | |
| value="0", | |
| label="T (Tumor Size)", | |
| interactive=True | |
| ) | |
| with gr.Row(): | |
| N_dropdown = gr.Dropdown( | |
| choices=[ | |
| ("N0 (No spread to nearby lymph nodes)", "0"), | |
| ("N1a (Spread to lymph nodes in the neck close to the thyroid)", "1"), | |
| ("N1b (Spread to lymph nodes in the neck farther from the thyroid or upper chest)", "2") | |
| ], | |
| value="0", | |
| label="N (Lymph Node Spread)", | |
| interactive=True | |
| ) | |
| with gr.Row(): | |
| focality_dropdown = gr.Dropdown( | |
| choices=[ | |
| ("Uni-focal (Single focus of thyroid cancer)", "1"), | |
| ("Multi-focal (Multiple foci of thyroid cancer)", "0") | |
| ], | |
| value="1", | |
| label="Focality", | |
| interactive=True | |
| ) | |
| with gr.Row(): | |
| response_dropdown = gr.Dropdown( | |
| choices=[ | |
| ("✅ Excellent Response - Negative imaging studies and Tg < 0.2 ng/mL or stimulated Tg < 1 ng/mL", "0"), | |
| ("❓ Indeterminate Response - Nonspecific findings; Tg potentially low", "1"), | |
| ("⚠️ Biochemical Incomplete - Tg > 1 ng/mL or rising anti-Tg antibody levels", "2"), | |
| ("❌ Structural Incomplete - Identifiable structural disease on imaging", "3") | |
| ], | |
| value="0", | |
| label="Response", | |
| interactive=True | |
| ) | |
| predict_button = gr.Button(value="Predict", variant="primary") | |
| prediction_output = gr.HTML(label="Prediction Results") | |
| predict_button.click(fn=clear_md, outputs=prediction_output) | |
| predict_button.click( | |
| fn=predict_cancer, | |
| inputs=[age_slider, gender_radio, T_dropdown, N_dropdown, focality_dropdown, response_dropdown], | |
| outputs=prediction_output | |
| ) | |
| demo.launch() |