import gradio as gr
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

def hazard_predictor(gender, age, bmi, diabetes_type, spb, dbp, hba1c, smoking, marital_status):
    # Dummy hazard function with a limit of 0.95 (95%)
    hazard_ratio = min((age / 50) * (bmi / 25) * (spb / 120) * (dbp / 80), 0.92)
    return {'Hazard Ratio': hazard_ratio}

def plot_chart(hazard_ratio, age):
    age_range = np.linspace(18, 100, 500)
    hazard_ratio_line = []

    for a in age_range:
        if a <= age:
            hazard_ratio_line.append(a / age * hazard_ratio)
        else:
            hazard_ratio_line.append(hazard_ratio)

    # Create a dataframe for Seaborn plotting
    data = {'Age': age_range, 'Hazard Ratio': hazard_ratio_line}
    df = pd.DataFrame(data)

    # Create the Seaborn line plot
    sns_plot = sns.lineplot(x="Age", y="Hazard Ratio", data=df)

    # Set plot labels and limits
    sns_plot.set(xlabel='Age', ylabel='Hazard Ratio', ylim=(0, 1))

    # Save the plot as an image
    chart_filepath = "chart.png"
    plt.savefig(chart_filepath, dpi=150)
    plt.close()  # Close the plot to free up the memory
    return chart_filepath

def output_function(gender, age, bmi, diabetes_type, spb, dbp, hba1c, smoking, marital_status):
    hazard_ratio = hazard_predictor(gender, age, bmi, diabetes_type, spb, dbp, hba1c, smoking, marital_status)['Hazard Ratio']
    output_label = f"Hazard Ratio: {hazard_ratio}"
    chart_filepath = plot_chart(hazard_ratio, age)
    return output_label, chart_filepath

    # Save the chart as an image
    chart_filepath = "chart.png"
    chart.savefig(chart_filepath, dpi=150)

    return output_label, chart_filepath


# Gradio user interface components
gender_radio = gr.inputs.Radio(choices=['Female', 'Male'], label="Gender")

age_slider = gr.inputs.Slider(minimum=18, maximum=100, step=1, default=50, label="Age")

bmi_slider = gr.inputs.Slider(minimum=15, maximum=50, step=0.1, default=25, label="BMI")

diabetes_type_radio = gr.inputs.Radio(choices=['Type 1', 'Type 2', 'Gestational'], label="Diabetes Type")

spb_slider = gr.inputs.Slider(minimum=90, maximum=200, step=1, default=120, label="Systolic Blood Pressure (SPB)")

dbp_slider = gr.inputs.Slider(minimum=60, maximum=120, step=1, default=80, label="Diastolic Blood Pressure (DBP)")

hba1c_slider = gr.inputs.Slider(minimum=100, maximum=400, step=1, default=200, label="Hemoglobin A1c value (mg/dL)")

smoking_radio = gr.inputs.Radio(choices=['Non-smoker', 'Smoker'], label="Smoking History")

marital_status_dropdown = gr.inputs.Dropdown(choices=['Single', 'Married', 'Widowed', 'Divorced'], label="Marital Status")

output_text = gr.outputs.Textbox(label="Hazard Ratio")
output_chart = gr.outputs.Image(type='filepath')

# Creating Gradio interface
interface = gr.Interface(fn=output_function,
                         inputs=[gender_radio, age_slider, bmi_slider, diabetes_type_radio, spb_slider, dbp_slider, hba1c_slider, smoking_radio, marital_status_dropdown],
                         outputs=[output_text, output_chart],
                         title="Diabetes Cox Proportional Hazard Predictor",
                         submit_button="Predict")

interface.launch(debug=True)