import gradio as gr
import numpy as np
import pickle
import pandas as pd
from sklearn.preprocessing import StandardScaler

# Define the questions and their possible answers
questions = {
    'age': {
        'type': 'number',
        'label': 'Age'
    },
    'A2': {
        'type': 'radio',
        'label': 'How would you rate your health generally?',
        'choices': ['Very poor', 'Poor', 'Average', 'Well', 'Very well', "Don't know"],
        'values': [0, 1, 1, 2, 3, -1]
    },
    'A1': {
        'type': 'radio',
        'label': 'How content or discontent do you presently feel about your life in general?',
        'choices': ['Neither nor', 'Content', 'Very discontent', 'Very content', 'Discontent', "Don't know", 'Refuse to answer'],
        'values': [0, 1, 2, 3, 4, 5, 6]
    },
    'M6': {
        'type': 'number',
        'label': 'In the past year, how much have you spent on medicine, nutritional supplement, vitamins (in local currency)?'
    },
    'M7': {
        'type': 'number',
        'label': 'In the past year, how much have you spent on gambling, lottery, pools, poker (in local currency)?'
    },
    'E5_a': {
        'type': 'radio',
        'label': 'In the past year, have you consulted other practitioners or therapists than your doctor?',
        'choices': ['No', 'Yes', "Don't know"],
        'values': [0, 1, -1]
    },
    'education': {
        'type': 'radio',
        'label': 'Education Level',
        'choices': ['Completed secondary school or more (eksamensskole)', 'Completed compulsory school (folkeskole, 9 years)'],
        'values': [0, 1]
    },
    'F10': {
        'type': 'radio',
        'label': 'Have you been dismissed from your job within the past 4 years?',
        'choices': ['No', 'Yes', "Don't know"],
        'values': [0, 1, -1]
    },
    'E3': {
        'type': 'radio',
        'label': 'How often are you in pain?',
        'choices': ['Never', 'Rarely', 'Sometimes', 'Often', 'Always', "Don't know"],
        'values': [0, 1, 2, 3, 4, -1]
    },
    'B3': {
        'type': 'radio',
        'label': 'Can you walk 100 meters (328 ft) on flat terrain?',
        'choices': ['Not at all', 'With much difficulty', 'With some difficulty', 'Without difficulty'],
        'values': [3, 0, 1, 2]
    },
    'B4': {
        'type': 'radio',
        'label': 'Can you climb up and down 12 steps?',
        'choices': ['Not at all', 'With much difficulty', 'With some difficulty', 'Without difficulty'],
        'values': [3, 0, 1, 2]
    },
    'E1': {
        'type': 'number',
        'label': 'How tall are you? (in centimeters)'
    },
    'E2': {
        'type': 'number',
        'label': 'How much do you weigh? (in kilos)'
    },
    'B1': {
        'type': 'radio',
        'label': 'Can you see with or without glasses?',
        'choices': ['Not at all', 'With much difficulty', 'With some difficulty', 'Without difficulty'],
        'values': [3, 0, 1, 2]
    },
    'B2': {
        'type': 'radio',
        'label': 'How well do you hear with or without a hearing aid?',
        'choices': ['Not at all', 'With much difficulty', 'With some difficulty', 'Without difficulty'],
        'values': [3, 0, 1, 2]
    },
    'E11': {
        'type': 'number',
        'label': 'How many hours a week do you normally exercise so you become warm and lose your breath?'
    },
    'B5': {
        'type': 'radio',
        'label': 'Can you concentrate and remember recent occurrences, agreements, consumed meals?',
        'choices': ['Not at all', 'With much difficulty', 'With some difficulty', 'Without difficulty'],
        'values': [3, 0, 1, 2]
    },
    'F15': {
        'type': 'radio',
        'label': 'On a scale from 0-10, where 0 signifies very low and 10 signifies very high, how content are you generally with your job?',
        'choices': ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', "Don't know", 'Refuse to answer'],
        'values': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -1, -1.5]
    },
    'B6': {
        'type': 'radio',
        'label': 'Can you eat on your own?',
        'choices': ['Not at all', 'With much difficulty', 'With some difficulty', 'Without difficulty'],
        'values': [3, 0, 1, 2]
    },
    'J14': {
        'type': 'radio',
        'label': 'Not counting online newspapers, how often have you read a newspaper in the past year?',
        'choices': ['Never', 'Rarely', 'Sometimes', 'Often', 'Always', "Don't know"],
        'values': [0, 1, 2, 3, 4, -1]
    },
    'J15': {
        'type': 'radio',
        'label': 'In the past year, how often have you discussed politics with colleagues, friends?',
        'choices': ['Never', 'Rarely', 'Sometimes', 'Often', 'Always', "Don't know"],
        'values': [0, 1, 2, 3, 4, -1]
    },
    'J17': {
        'type': 'radio',
        'label': 'In the past year, how often have you been abroad on holiday or family visit?',
        'choices': ['Never', 'Rarely', 'Sometimes', 'Often', 'Always', "Don't know"],
        'values': [0, 1, 2, 3, 4, -1]
    },
    'K2': {
        'type': 'radio',
        'label': 'How often do you use the Internet for shopping, banking transactions or paying bills?',
        'choices': ['Never', 'Once a month', 'Less frequently', 'Several times a month', 'Once a week', 'Several times a week', 'Daily'],
        'values': [0, 2, 1, 4, 3, 5, 6]
    },
    'J1': {
        'type': 'radio',
        'label': 'In the past year, how often have you spent time with your children and/or parents?',
        'choices': ['Never', 'Once a month', 'Less frequently', 'Several times a month', 'Once a week', 'Several times a week', 'Daily'],
        'values': [0, 2, 1, 4, 3, 5, 6]
    },
    'J2': {
        'type': 'radio',
        'label': 'In the past year, how often have you spent time with other relatives?',
        'choices': ['Never', 'Once a month', 'Less frequently', 'Several times a month', 'Once a week', 'Several times a week', 'Daily', "Don't know", 'Refuse to answer'],
        'values': [0, 2, 1, 4, 3, 5, 6, -1, -1]
    },
    'J4': {
        'type': 'radio',
        'label': 'In the past year, how often have you spent time with acquaintances?',
        'choices': ['Never', 'Once a month', 'Less frequently', 'Several times a month', 'Once a week', 'Several times a week', 'Daily', "Don't know", 'Refuse to answer'],
        'values': [0, 2, 1, 4, 3, 5, 6, -1, -1]
    },
    'E12': {
        'type': 'radio',
        'label': 'How often do you drink alcohol?',
        'choices': ['Never', 'Less frequently', 'Less than once a month, but several times a year', 'Once a month', 'Once every fortnight', 'Once a week', '2-3 times a week', 'Almost every day', 'Every day', "Don't know"],
        'values': [0, 1, 4, 3, 5, 6, 7, 8, 9, -1]
    },
    'E6': {
        'type': 'radio',
        'label': 'Have you ever smoked cigarettes, cigars, cheroot or pipe?',
        'choices': ['No', 'Yes', 'Refuse to answer'],
        'values': [0, 1, -1]
    },
    'B8': {
        'type': 'radio',
        'label': 'Can you do the shopping?',
        'choices': ['Not at all', 'With much difficulty', 'With some difficulty', 'Without difficulty', "Don't know"],
        'values': [3, 0, 1, 2, -1]
    },
    'B9': {
        'type': 'radio',
        'label': 'Can you do household chores such as cleaning and cooking?',
        'choices': ['Not at all', 'With much difficulty', 'With some difficulty', 'Without difficulty', "Don't know"],
        'values': [3, 0, 1, 2, -1]
    }
}

def predict_ncd(*inputs):
    # Convert inputs to appropriate format
    input_dict = {name: value for name, value in zip(questions.keys(), inputs)}
    
    # Encode categorical variables
    for col, value in input_dict.items():
        if col in questions and questions[col]['type'] == 'radio':
            # Find the index of the selected choice
            try:
                choice_index = questions[col]['choices'].index(value)
                # Get the corresponding encoded value
                input_dict[col] = questions[col]['values'][choice_index]
            except ValueError:
                # Handle case where value is not in choices
                input_dict[col] = -1  # default value for unknown/invalid choices
    
    # Create DataFrame with encoded values
    df = pd.DataFrame([input_dict])
    
    # Load the model and scaler
    with open('model.pkl', 'rb') as f:
        model = pickle.load(f)
    with open('scaler.pkl', 'rb') as f:
        scaler = pickle.load(f)
    
    # Ensure column order matches training data
    expected_columns = ['age', 'A2', 'A1', 'M6', 'M7', 'E5_a', 'education', 'F10', 'E3', 
                       'B3', 'B4', 'E1', 'E2', 'B1', 'B2', 'E11', 'B5', 'F15', 'B6', 
                       'J14', 'J15', 'J17', 'K2', 'J1', 'J2', 'J4', 'E12', 'E6', 'B8', 'B9']
    df = df[expected_columns]
    
    # Convert all values to float
    df = df.astype(float)
    
    # Preprocess the input
    X = scaler.transform(df)
    
    # Make prediction
    pred = model.predict_proba(X)[0]
    
    return {
        'No NCD': float(pred[0]),
        'Has NCD': float(pred[1])
    }

# Create Gradio interface
inputs = []
for name, config in questions.items():
    if config['type'] == 'number':
        inputs.append(gr.Number(label=config['label']))
    elif config['type'] == 'radio':
        inputs.append(gr.Radio(choices=config['choices'], label=config['label']))

output = gr.Label(label="Model Prediction:")

iface = gr.Interface(
    fn=predict_ncd,
    inputs=inputs,
    outputs=output,
    title="NCD Risk Prediction Tool",
    description="Enter your health and lifestyle information to assess your risk of Noncommunicable Diseases (NCDs)",
    theme='huggingface'
)

if __name__ == "__main__":
    iface.launch()