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

# تحميل الموديل
stack_model = joblib.load("water_quality_model.pkl")

def predict_water(ph, hardness, solids, chloramines, sulfate,
                  conductivity, organic_carbon, trihalomethanes, turbidity,
                  cost_per_sample=10):

    ph_hardness = ph * hardness
    solids_turbidity = solids / (turbidity + 1)
    features = np.array([[ph, hardness, solids, chloramines, sulfate,
                          conductivity, organic_carbon, trihalomethanes,
                          turbidity, ph_hardness, solids_turbidity]])

    prob = stack_model.predict_proba(features)[0][1]
    pred_class = "Safe" if prob >= 0.5 else "Not Safe"

    color = "green" if pred_class=="Safe" else "red"
    result_text = f"<span style='color:{color}; font-size:24px'>Water is {pred_class} (Confidence: {prob*100:.1f}%)</span>"

    treatment_cost = cost_per_sample if pred_class=="Not Safe" else 0
    econ_text = f"<span style='font-size:20px'>Estimated Treatment Cost: {treatment_cost} EGP</span>"

    return result_text, econ_text

interface = gr.Interface(
    fn=predict_water,
    inputs=[
        gr.Slider(0,14, step=0.1, label="pH"),
        gr.Slider(0,500, step=1, label="Hardness"),
        gr.Slider(0,1000, step=1, label="Solids"),
        gr.Slider(0,20, step=0.1, label="Chloramines"),
        gr.Slider(0,500, step=1, label="Sulfate"),
        gr.Slider(0,1500, step=1, label="Conductivity"),
        gr.Slider(0,20, step=0.1, label="Organic Carbon"),
        gr.Slider(0,150, step=0.1, label="Trihalomethanes"),
        gr.Slider(0,10, step=0.1, label="Turbidity"),
        gr.Number(value=10, label="Cost per Sample (EGP)")
    ],
    outputs=[
        gr.HTML(label="Prediction Result"),
        gr.HTML(label="Economic Impact")
    ],
    title="Water Quality Prediction with Economic Impact",
    description="Enter water properties to predict water safety and estimated treatment cost."
)

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