import re
import pandas as pd
import numpy as np
import gradio as gr
import warnings
import time
# Suppress warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
# Load model
def load_model(model_path):
    return pd.read_pickle(model_path)

model = load_model("hear_disease_detection.pkl")

def predict_heart_failure(age, blood_pressure, cholesterol, fasting_blood_sugar, max_heart_rate, old_peak,
                          gender, chest_pain_type, ecg, exercise_angina, st_slope):
    # Check for missing inputs
    if age is None or blood_pressure is None or cholesterol is None or max_heart_rate is None or old_peak is None:
        return "Please enter all required numerical values."

    if gender is None or chest_pain_type is None or ecg is None or exercise_angina is None or st_slope is None:
        return "Please select all required options."

    # Convert inputs
    patient_fasting_blood_sugar = 1 if fasting_blood_sugar == "Greater Than 120 mg/dl" else 0
    patient_gender = [1] if gender == "Male" else [0]

    patient_chest_pain_type = [0, 0, 0]
    if chest_pain_type == "Typical Angina":
        patient_chest_pain_type = [0, 0, 1]
    elif chest_pain_type == "Atypical Angina":
        patient_chest_pain_type = [1, 0, 0]
    elif chest_pain_type == "Non-anginal Pain":
        patient_chest_pain_type = [0, 1, 0]

    patient_ecg = [1, 0] if ecg == "Normal" else [0, 1] if ecg == "ST" else [0, 0]
    patient_exercise_angina = [1] if exercise_angina == "Yes" else [0]
    patient_slope = [0, 0] if st_slope == "Down" else [1, 0] if st_slope == "Flat" else [0, 1]

    # Prepare input for the model
    new_data = [age, blood_pressure, cholesterol, patient_fasting_blood_sugar, max_heart_rate, old_peak]
    new_data.extend(patient_gender)
    new_data.extend(patient_chest_pain_type)
    new_data.extend(patient_ecg)
    new_data.extend(patient_exercise_angina)
    new_data.extend(patient_slope)

    # Prediction
    predicted_value = model.predict([new_data])[0]
    prediction_prop = np.round(model.predict_proba([new_data]) * 100)

    prediction = (
        "Based on the information provided, you are not a heart patient."
        if predicted_value == 0 else
        "Based on the information provided, you are at risk of heart disease."
    )
    
    # Return individual outputs
    return prediction, prediction_prop[0, 0], prediction_prop[0, 1]

# Gradio interface
inputs = [
    gr.Slider(minimum=1, maximum=90, value=48, label="Age"),
    gr.Slider(minimum=0, maximum=200, value=140, label="Resting Blood Pressure"),
    gr.Slider(minimum=0, maximum=510, value=228, label="Cholesterol"),
    gr.Radio(["Greater Than 120 mg/dl", "Less Than 120 mg/dl"], label="Fasting Blood Sugar"),
    gr.Slider(minimum=0, maximum=200, value=100, label="Max Heart Rate"),
    gr.Slider(minimum=-3.0, maximum=4.5, value=2.5, label="Old Peak"),
    gr.Radio(["Male", "Female"], label="Gender"),
    gr.Radio(["Typical Angina", "Atypical Angina", "Non-anginal Pain", "Asymptomatic"], label="Chest Pain Type"),
    gr.Radio(["Normal", "ST", "LVH"], label="ECG"),
    gr.Radio(["Yes", "No"], label="Exercise Angina"),
    gr.Radio(["Up", "Flat", "Down"], label="ST Slope"),
]

outputs = [
    gr.Textbox(label="Prediction"),
]

# Launch the Gradio app
gr.Interface(fn=predict_heart_failure, inputs=inputs, outputs=outputs, 
             title="Heart Failure Prediction", 
             description="Enter the details to predict heart disease.").launch()