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import streamlit as st |
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from tensorflow.keras.models import load_model |
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import numpy as np |
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from PIL import Image |
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import cv2 |
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from tensorflow.keras.preprocessing.image import img_to_array, load_img |
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@st.cache_data() |
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def load(): |
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model_path = "best_model.h5" |
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model = load_model(model_path, compile=False) |
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return model |
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model = load() |
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def predict(upload): |
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img = Image.open(upload) |
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img = np.asarray(img) |
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img_resize = cv2.resize(img, (224, 224)) |
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img_resize = np.expand_dims(img_resize, axis=0) |
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pred = model.predict(img_resize) |
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rec = pred[0][0] |
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return rec |
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st.title("Poubelle Intelligente") |
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upload = st.file_uploader("Chargez l'image de votre objet", |
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type=['png', 'jpeg', 'jpg']) |
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c1, c2 = st.columns(2) |
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if upload: |
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rec = predict(upload) |
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prob_recyclable = rec * 100 |
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prob_organic = (1-rec)*100 |
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c1.image(Image.open(upload)) |
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if prob_recyclable > 50: |
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c2.write(f"Je suis certain à {prob_recyclable:.2f} % que l'objet est recyclable") |
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else: |
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c2.write(f"Je suis certain à {prob_organic:.2f} % que l'objet n'est pas recyclable") |
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