src/streamlit_app.py

import streamlit as st
import numpy as np
import onnxruntime as ort
import os

# 1. Navigasi Path agar bisa menemukan model.onnx di luar folder src
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
MODEL_PATH = os.path.join(BASE_DIR, "..", "model.onnx")

# 2. Nilai Scaler Manual
MEAN = np.array([568.64435, -826.0645, 466.6756, -989.498, 246.6452, -352.4072, 177.49335, -168.12985, 148.05485, -322.05495, 193.01935, -424.63435, 605.80795, -755.47005, 553.1577, -790.659])
SCALE = np.array([117.07176288, 124.34028285, 207.7301335, 221.50159863, 63.38759671, 59.32603297, 21.54848616, 11.90921866, 68.10403763, 76.06642249, 92.40615659, 105.37879602, 61.45968896, 70.74213457, 82.39041528, 95.09934868])

st.title("🚜 Beta Front Fork Prediction")

# 3. Form Input
st.subheader("Input Fitur")
feature_names = [
    "pipe_r_front_max", "pipe_r_front_min", "pipe_l_front_max", "pipe_l_front_min",
    "pipe_r_rear_max", "pipe_r_rear_min", "pipe_l_rear_max", "pipe_l_rear_min",
    "bridge_r_front_max", "bridge_r_front_min", "bridge_l_front_max", "bridge_l_front_min",
    "bridge_r_rear_max", "bridge_r_rear_min", "bridge_l_rear_max", "bridge_l_rear_min"
]

input_data = []
col1, col2 = st.columns(2)
for i, name in enumerate(feature_names):
    with col1 if i < 8 else col2:
        val = st.number_input(name, value=0.0)
        input_data.append(val)

# 4. Prediksi
if st.button("Predict", type="primary"):
    if os.path.exists(MODEL_PATH):
        # Preprocessing
        x = (np.array(input_data) - MEAN) / SCALE
        x = x.astype(np.float32).reshape(1, 16)
        
        # Inference
        session = ort.InferenceSession(MODEL_PATH)
        input_name = session.get_inputs()[0].name
        output = session.run(None, {input_name: x})[0]
        
        # Hasil
        res1, res2 = st.columns(2)
        res1.metric("Load Angle", f"{output[0][0]:.4f}")
        res2.metric("Target Load", f"{output[0][1]:.4f}")
    else:
        st.error("File model.onnx tidak ditemukan!")