app.py

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

def draw():
    #rectangle sur la prediction
    img = cv2.imread(upload)
    img = cv2.resize(img, (224, 224))
    img = cv2.rectangle(img, (0, 0), (224, 224), (0, 255, 0), 3)
    cv2.imwrite('output.png', img)



st.title("Poubelle Intelligente")

upload = st.file_uploader("Charger Image", type=["pnj", "jpeg", "jpg"])

c1, c2 = st.columns(2)

if upload:
    rec = predict(upload)
    prob_rec = predict(upload) * 100
    prob_org = (1 - rec) * 100
    c1.image(Image.open(upload))
    if prob_rec > 50:
        c2.write(f"Je suis certains à {prob_rec:.2f} % que ceci est recyclable")
    else:
        c2.write(f"Je suis certains à {prob_org:.2f} % que ceci ne soit pas recyclable")