| import streamlit as st | |
| from tensorflow.keras.models import load_model | |
| from PIL import Image | |
| import numpy as np | |
| model=load_model("cifar_10.h5") | |
| def process_image(img): | |
| img=img.resize((32,32)) | |
| img=np.array(img) | |
| img=img/255.0 | |
| img=np.expand_dims(img,axis=0) | |
| return img | |
| st.title("CIFAR 10 resim siniflandirma modeli") | |
| st.write("Resim sec model ne odlugunu tahmin etsin") | |
| file=st.file_uploader("Bir resim sec",type=["jpeg","jpg","png"]) | |
| if file is not None: | |
| img=Image.open(file) | |
| st.image(img,caption="yuklenen resim") | |
| image=process_image(img) | |
| prediction=model.predict(image) | |
| predicted_class=np.argmax(prediction) | |
| class_names={ | |
| 0:"ucak", 1:"otomobil", 2:"kuş", 3:"kedi", 4:"geyik", 5:"köpek", 6:"kurbağa", 7:"at", 8:"gemi", 9:"kamyon" | |
| } | |
| st.write(class_names[predicted_class]) |