import gradio as gr
import pickle
import numpy as np
from tensorflow import keras
from tensorflow.keras.models import load_model

# Load the model and scalers
model1 = load_model('/home/user/app/model.h5')
scaler_x = pickle.load(open('/home/user/app/scaler.pkl', 'rb'))
scaler_y = pickle.load(open('/home/user/app/scaler_y.pkl', 'rb'))

def SPT_fee(stress, N):
    x = np.array([stress, N])
    x = np.reshape(x, (1, 2))
    x_scale = scaler_x.transform(x)
    y_scale = model1.predict(x_scale)
    y_scale = np.reshape(y_scale, (1, 1))
    y = scaler_y.inverse_transform(y_scale)  # state parameter
    # calculate friction angle
    fee = 34.821 - 27.512 * y
    return f"Friction angle: {fee[0,0]:.2f} degrees"

# Create Gradio interface
iface = gr.Interface(
    fn=SPT_fee,
    inputs=[
        gr.Number(label="Stress (kPa)", value=100),
        gr.Number(label="N value", value=3)
    ],
    outputs="text",
    title="SPT Friction Angle Calculator of Bangkok Sand (Youwai and Wongsala,2024) ",
    description="Calculate friction angle based on stress and N value."
)

# Launch the interface
iface.launch()