import gradio as gr
import numpy as np
import pandas as pd
import joblib

# Load models and scaler
model_binary = joblib.load("model_binary_cv.pkl")
model_multiclass = joblib.load("model_multiclass_cv.pkl")
scaler = joblib.load("scaler.pkl")
label_enc = joblib.load("label_encoder_multiclass.pkl")

feature_names = [
    'Patient Id', 'Age', 'Total cholesterol', 'HDL', 'LDL', 'VLDL',
    'TRIGLYCERIDES', 'before glycemic control random blood sugar',
    'before glycemic control HbA1c', 'alcohol consumption', 'family_history_diabetes',
    'Gender_FEMALE', 'Gender_MALE', 'dietary habits_non-vegetarian',
    'dietary habits_non-vegetarian ', 'dietary habits_vegetarian',
    'smoking status_no', 'smoking status_yes',
    'family_history_cardiovascular_disease_no',
    'family_history_cardiovascular_disease_yes'
]

def predict_diabetes(
    Age,
    Total_cholesterol,
    HDL,
    LDL,
    VLDL,
    TRIGLYCERIDES,
    before_random_blood_sugar,
    before_HbA1c,
    alcohol_consumption,
    family_history_diabetes,
    Gender,
    dietary_habits,
    smoking_status,
    family_history_cardiovascular_disease
):
    try:
        alcohol_consumption = int(alcohol_consumption)
        family_history_diabetes = int(family_history_diabetes)

        Gender_FEMALE = 1 if Gender == "Female" else 0
        Gender_MALE = 1 if Gender == "Male" else 0

        dietary_non_veg = 1 if dietary_habits == "Non-vegetarian" else 0
        dietary_non_veg_dup = dietary_non_veg
        dietary_veg = 1 if dietary_habits == "Vegetarian" else 0

        smoking_no = 1 if smoking_status == "No" else 0
        smoking_yes = 1 if smoking_status == "Yes" else 0

        family_cvd_no = 1 if family_history_cardiovascular_disease == "No" else 0
        family_cvd_yes = 1 if family_history_cardiovascular_disease == "Yes" else 0

        input_values = [[
            0, Age, Total_cholesterol, HDL, LDL, VLDL, TRIGLYCERIDES,
            before_random_blood_sugar, before_HbA1c, alcohol_consumption, family_history_diabetes,
            Gender_FEMALE, Gender_MALE, dietary_non_veg, dietary_non_veg_dup, dietary_veg,
            smoking_no, smoking_yes, family_cvd_no, family_cvd_yes
        ]]
        input_df = pd.DataFrame(input_values, columns=feature_names)

        input_scaled = scaler.transform(input_df)

        binary_pred = model_binary.predict(input_scaled)[0]
        if binary_pred == 0:
            return "✅ Prediction: No Diabetes Risk (Normal)"

        multi_pred = model_multiclass.predict(input_scaled)[0]
        status = label_enc.inverse_transform([multi_pred])[0]

        status_display = "Prediabetes" if status == "prediabetes" else "Diabetes"
        emoji = "⚠️🟡" if status == "prediabetes" else "🚨🔴"

        return f"{emoji} At Risk: {status_display}"

    except Exception as e:
        return f"❌ Error during prediction: {str(e)}"


with gr.Blocks() as demo:
    gr.Markdown("## 🩺 Diabetes Risk and Type Predictor")
    gr.Markdown(
        """
        **Developed by Dr. Vinod Kumar Yata's research group**  
        School of Allied and Healthcare Sciences, Malla Reddy University, Hyderabad, India  

        ---
        ⚠️ This AI tool is for **research purposes only**.  
        It predicts **diabetes risk** and if at risk, whether it's **prediabetes or diabetes**.  
        Please consult a medical professional for any diagnosis.
        """
    )

    with gr.Row():
        with gr.Column():
            Age = gr.Number(label="Age", value=30)
            Total_cholesterol = gr.Number(label="Total Cholesterol", value=180)
            HDL = gr.Number(label="HDL", value=50)
            LDL = gr.Number(label="LDL", value=100)
            VLDL = gr.Number(label="VLDL", value=20)
            TRIGLYCERIDES = gr.Number(label="TRIGLYCERIDES", value=150)
            before_random_blood_sugar = gr.Number(label="Random Blood Sugar (before control)", value=120)
            before_HbA1c = gr.Number(label="HbA1c (before control)", value=5.5)

        with gr.Column():
            alcohol_consumption = gr.Checkbox(label="Alcohol Consumption (Yes)", value=False)
            family_history_diabetes = gr.Checkbox(label="Family History of Diabetes (Yes)", value=False)
            Gender = gr.Radio(label="Gender", choices=["Female", "Male"], value="Female")
            dietary_habits = gr.Radio(label="Dietary Habits", choices=["Vegetarian", "Non-vegetarian"], value="Vegetarian")
            smoking_status = gr.Radio(label="Smoking Status", choices=["No", "Yes"], value="No")
            family_history_cardiovascular_disease = gr.Radio(label="Family History of Cardiovascular Disease", choices=["No", "Yes"], value="No")

    submit_btn = gr.Button("🧪 Predict")
    output = gr.Textbox(label="Prediction Result")

    submit_btn.click(
        fn=predict_diabetes,
        inputs=[
            Age, Total_cholesterol, HDL, LDL, VLDL, TRIGLYCERIDES,
            before_random_blood_sugar, before_HbA1c,
            alcohol_consumption, family_history_diabetes,
            Gender, dietary_habits, smoking_status,
            family_history_cardiovascular_disease
        ],
        outputs=output
    )

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