import streamlit as st
from tensorflow.keras.models import load_model
import numpy as np
from PIL import Image
import cv2
from tensorflow.keras.preprocessing.image import img_to_array, load_img

@st.cache_data()
def load():
    model_path = "best_model.h5"
    model = load_model(model_path, compile=False)
    return model

# Chargement du model
model = load()


def predict(upload):

    img = Image.open(upload)
    img = np.asarray(img)
    img_resize = cv2.resize(img, (224, 224))
    img_resize = np.expand_dims(img_resize, axis=0)
    pred = model.predict(img_resize)

    rec = pred[0][0]

    return rec




st.title("Poubelle Intelligente")

upload = st.file_uploader("Chargez l'image de votre objet",
                           type=['png', 'jpeg', 'jpg'])

c1, c2 = st.columns(2)

if upload:
    rec = predict(upload)
    prob_recyclable = rec * 100      
    prob_organic = (1-rec)*100

    c1.image(Image.open(upload))
    if prob_recyclable > 50:
        c2.write(f"Je suis certain à {prob_recyclable:.2f} % que l'objet est recyclable")
    else:
        c2.write(f"Je suis certain à {prob_organic:.2f} % que l'objet n'est pas recyclable")