app.py

import gradio as gr
import pandas as pd
import joblib

import matplotlib

matplotlib.use('TkAgg')


def predict(gender, bmi, asthma, glucose, pulse):

    gender_char = 1
    if gender == "Female":
        gender_char = 0

    asthma_val = False
    if asthma is True:
        asthma_val = 1

    pred_data = {
        "id": 0,
        "rcount": train_data['rcount'].mean(),
        "gender": gender_char,
        "dialysisrenalendstage": False,
        "asthma": asthma_val,
        "irondef": False,
        "pneum": False,
        "substancedependence": False,
        "psychologicaldisordermajor": False,
        "depress": False,
        "psychother": False,
        "fibrosisandother": False,
        "malnutrition": False,
        "hemo": False,
        "hematocrit": False,
        "neutrophils": False,
        "sodium": train_data['sodium'].mean(),
        "glucose": glucose,
        "bloodureanitro": train_data['bloodureanitro'].mean(),
        "creatinine": train_data['creatinine'].mean(),
        "bmi": bmi,
        "pulse": pulse,
        "respiration": train_data['respiration'].mean(),
        "secondarydiagnosisnonicd9": 0,
        "facid": 0
    }

    features = ['id', 'rcount', 'gender', 'dialysisrenalendstage', 'asthma', 'irondef', 'pneum', 'substancedependence',
                'psychologicaldisordermajor', 'depress', 'psychother', 'fibrosisandother', 'malnutrition', 'hemo',
                'hematocrit', 'neutrophils', 'sodium', 'glucose', 'bloodureanitro', 'creatinine', 'bmi', 'pulse',
                'respiration', 'secondarydiagnosisnonicd9', 'facid']

    pred_df = pd.DataFrame([pred_data], columns=features)
    result = "Error in prediction"

    try:
        model = joblib.load('data/model.pkl')
        result = model.predict(pred_df)
    except Exception as e:
        print(f"Fehler beim Laden des Modells: {str(e)}")

    print(result)
    return result


gender = 0
bmi_min, bmi_max, bmi_default = 15, 50, 25
asthma = 0
glucose_min, glucose_max, glucose_default = 30, 190, 85
pulse_min, pulse_max, pulse_default = 40, 70, 180

# Create the interface
iface = gr.Interface(
    fn=predict,
    inputs=[
        gr.components.Radio(["Female", "Male"], label="Gender"),
        gr.components.Slider(minimum=bmi_min, maximum=bmi_max, value=bmi_default, label="BMI"),
        gr.components.Checkbox(label="Asthma"),
        gr.components.Slider(minimum=glucose_min, maximum=glucose_max, value=glucose_default, label="Glucose Level"),
        gr.components.Slider(minimum=pulse_min, maximum=glucose_max, value=pulse_default, label="Pulse")
    ],
    outputs=gr.components.Textbox(label="Prediction of stay length"),
    title="Length of Stay - Predictor",
    description="""Enter the values to get a prediction of your length of stay""",
)

# Launch the interface
iface.launch()