Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,49 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+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!")