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app.py

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+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")
+
+    

best_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a99ab614495b9f07e64fdba73e22846b5baf5fa8f16689fb8cbed82028e11b84
+size 20850712