import gradio as gr
import numpy as np
import joblib
import tensorflow as tf
from huggingface_hub import hf_hub_download

# 1) 허브에서 모델·스케일러 파일 다운로드
model_path = hf_hub_download(repo_id="dklee2013/rc-column-predictor",
                             filename="model0526.keras")
sx_path    = hf_hub_download(repo_id="dklee2013/rc-column-predictor",
                             filename="sx.save")
sy_path    = hf_hub_download(repo_id="dklee2013/rc-column-predictor",
                             filename="sy.save")

model    = tf.keras.models.load_model(model_path)
scaler_X = joblib.load(sx_path)
scaler_y = joblib.load(sy_path)

def predict(f_ck, f_yk, N_Ed, M_Edz, M_Edy, cover, rebar_dia, dc_ratio):
    # 1차 예측
    X1 = np.array([f_ck, f_yk, N_Ed, M_Edz, M_Edy, cover, rebar_dia]).reshape(1,-1)
    Y1 = scaler_y.inverse_transform(model.predict(scaler_X.transform(X1)))
    a  = Y1[0,5]

    # 모멘트 보정
    M_Edz_sc = M_Edz * (1/dc_ratio)**a
    M_Edy_sc = M_Edy * (1/dc_ratio)**a

    # 2차 예측
    X2 = np.array([f_ck, f_yk, N_Ed, M_Edz_sc, M_Edy_sc, cover, rebar_dia]).reshape(1,-1)
    Y2 = scaler_y.inverse_transform(model.predict(scaler_X.transform(X2)))

    # 결과
    b_pred = int(round(Y2[0,0]/50)*50)
    h_pred = int(round(Y2[0,1]/50)*50)
    rr     = float(Y2[0,2])
    rx     = int(round(Y2[0,3]))
    ry     = int(round(Y2[0,4]))
    z1_a   = float(Y2[0,5])
    z2_N   = float(Y2[0,6])
    z3_Mz  = float(Y2[0,7])
    z4_My  = float(Y2[0,8])
    SF     = (M_Edz / z3_Mz)**z1_a + (M_Edy / z4_My)**z1_a

    return {
      "b_pred": b_pred,
      "h_pred": h_pred,
      "reinforcement_ratio": rr,
      "rebar_x": rx,
      "rebar_y": ry,
      "exponent_a": round(z1_a,4),
      "N_Rd": round(z2_N,2),
      "M_Rdz": round(z3_Mz,2),
      "M_Rdy": round(z4_My,2),
      "SF": round(SF,4)
    }

# 3) Gradio UI 정의
inputs = [
  gr.Number(label="f_ck (MPa)", value=40),
  gr.Number(label="f_yk (MPa)", value=500),
  gr.Number(label="N_Ed (kN)", value=500),
  gr.Number(label="M_Edz (kN·m)", value=100),
  gr.Number(label="M_Edy (kN·m)", value=150),
  gr.Number(label="cover (mm)", value=40),
  gr.Number(label="rebar_dia (mm)", value=25),
  gr.Number(label="dc_ratio", value=0.9, precision=2)
]
outputs = {
  "b_pred": gr.Number(label="b_pred (mm)"),
  "h_pred": gr.Number(label="h_pred (mm)"),
  "reinforcement_ratio": gr.Number(label="reinforcement_ratio"),
  "rebar_x": gr.Number(label="rebar_x (개)"),
  "rebar_y": gr.Number(label="rebar_y (개)"),
  "exponent_a": gr.Number(label="exponent a"),
  "N_Rd": gr.Number(label="N_Rd"),
  "M_Rdz": gr.Number(label="M_Rdz"),
  "M_Rdy": gr.Number(label="M_Rdy"),
  "SF": gr.Number(label="Safety Factor")
}

demo = gr.Interface(fn=predict,
                    inputs=inputs,
                    outputs=outputs,
                    title="🔧 RC 기둥 단면 예측기",
                    description="Eurocode 2 기반 ML 모델을 이용한 RC 기둥 단면 예측")

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