import streamlit as st
import numpy as np
import json
from PIL import Image
import tensorflow as tf

from tensorflow.keras.models import load_model
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing import image as keras_image

# === Fungsi untuk preprocessing gambar ===
def preprocess_image_inference(image_file, target_size=(224, 224)):
    img = Image.open(image_file).convert('RGB')
    img = img.resize(target_size)
    img_array = keras_image.img_to_array(img)
    img_array = preprocess_input(img_array)
    img_array = np.expand_dims(img_array, axis=0)
    return img_array

# === Load model dan class names ===
model = load_model('model_inf.h5')

with open("class_names.json", "r") as f:
    class_names = json.load(f)

def run():
    st.title("Computer Vision-Based Vehicle Recognition")
    st.write("### *Upload gambar yang ingin diprediksi:")

    # Upload file form
    uploaded_file = st.file_uploader("Upload gambar", type=["jpg", "jpeg", "png"])

    if uploaded_file is not None:
        # Preprocess dan prediksi
        image_array = preprocess_image_inference(uploaded_file)
        pred = model.predict(image_array)
        predicted_index = np.argmax(pred, axis=1)[0]
        predicted_label = class_names[predicted_index]

        # Tampilkan hasil
        st.image(uploaded_file, caption=f"Predicted: {predicted_label}", use_column_width=True)
        st.success(f"✅ Prediksi: **{predicted_label}** (class index: {predicted_index})")

if __name__ == '__main__':
    run()