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| import logging | |
| import gradio as gr | |
| import pandas as pd | |
| from autogluon.tabular import TabularPredictor | |
| # Set up logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| # Load the AutoGluon model from the Hugging Face Hub | |
| MODEL_ID = "aai540-group3/diabetes-readmission" | |
| predictor = TabularPredictor.load(MODEL_ID) | |
| # Define constants for the Gradio interface | |
| AGE_RANGE = (0, 100) | |
| TIME_IN_HOSPITAL_RANGE = (1, 14) | |
| NUM_PROCEDURES_RANGE = (0, 10) | |
| NUM_MEDICATIONS_RANGE = (0, 20) | |
| NUMBER_DIAGNOSES_RANGE = (1, 10) | |
| READMITTED_CHOICES = ["<30", ">30", "NO"] | |
| # Define the inference function | |
| def predict( | |
| age, time_in_hospital, num_procedures, num_medications, number_diagnoses, | |
| metformin, repaglinide, nateglinide, chlorpropamide, glimepiride, | |
| glipizide, glyburide, pioglitazone, rosiglitazone, acarbose, insulin, | |
| readmitted | |
| ): | |
| # Create a DataFrame from the input features | |
| input_data = pd.DataFrame([{ | |
| "age": age, | |
| "time_in_hospital": time_in_hospital, | |
| "num_procedures": num_procedures, | |
| "num_medications": num_medications, | |
| "number_diagnoses": number_diagnoses, | |
| "metformin": int(metformin), | |
| "repaglinide": int(repaginide), | |
| "nateglinide": int(nateglinide), | |
| "chlorpropamide": int(chlorpropamide), | |
| "glimepiride": int(glimepiride), | |
| "glipizide": int(glipizide), | |
| "glyburide": int(glyburide), | |
| "pioglitazone": int(pioglitazone), | |
| "rosiglitazone": int(rosiglitazone), | |
| "acarbose": int(acarbose), | |
| "insulin": int(insulin), | |
| "readmitted": readmitted | |
| }]) | |
| try: | |
| # Make a prediction using the AutoGluon predictor | |
| prediction = predictor.predict(input_data) | |
| logger.info(f"Prediction received: {prediction}") | |
| return f"<h1 style='font-size: 48px; color: green;'>Prediction: {prediction.iloc[0]}</h1>" | |
| except Exception as e: | |
| logger.error(f"Error in prediction: {e}") | |
| return "<h1 style='font-size: 48px; color: red;'>Error in prediction</h1>" | |
| # Create Gradio interface | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=[ | |
| gr.Slider(minimum=AGE_RANGE[0], maximum=AGE_RANGE[1], label="Age"), | |
| gr.Slider(minimum=TIME_IN_HOSPITAL_RANGE[0], maximum=TIME_IN_HOSPITAL_RANGE[1], label="Time in Hospital (days)"), | |
| gr.Slider(minimum=NUM_PROCEDURES_RANGE[0], maximum=NUM_PROCEDURES_RANGE[1], label="Number of Procedures"), | |
| gr.Slider(minimum=NUM_MEDICATIONS_RANGE[0], maximum=NUM_MEDICATIONS_RANGE[1], label="Number of Medications"), | |
| gr.Slider(minimum=NUMBER_DIAGNOSES_RANGE[0], maximum=NUMBER_DIAGNOSES_RANGE[1], label="Number of Diagnoses"), | |
| gr.Checkbox(label="Metformin"), | |
| gr.Checkbox(label="Repaglinide"), | |
| gr.Checkbox(label="Nateglinide"), | |
| gr.Checkbox(label="Chlorpropamide"), | |
| gr.Checkbox(label="Glimepiride"), | |
| gr.Checkbox(label="Glipizide"), | |
| gr.Checkbox(label="Glyburide"), | |
| gr.Checkbox(label="Pioglitazone"), | |
| gr.Checkbox(label="Rosiglitazone"), | |
| gr.Checkbox(label="Acarbose"), | |
| gr.Checkbox(label="Insulin"), | |
| gr.Radio(choices=READMITTED_CHOICES, label="Readmitted") | |
| ], | |
| outputs=gr.HTML(label="Prediction"), | |
| title="Diabetes Readmission Prediction", | |
| description="Enter patient data to predict the likelihood of readmission." | |
| ) | |
| # Launch the Gradio app | |
| if __name__ == "__main__": | |
| iface.launch() |