ready to test

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
+
+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")

best_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3afdac06a661337838146779626c57c39ee81a70d7641ac5acf3870b01a178b2
+size 20850712

requirements.txt

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+streamlit
+tensorflow
+numpy
+opencv-python
+Pillow