| import streamlit as st | |
| from tensorflow.keras.models import load_model | |
| from PIL import Image | |
| import numpy as np | |
| model=load_model('src/grape_disease_model.h5') | |
| def process_image(img): | |
| img=img.resize((170,170)) | |
| img=np.array(img) | |
| img=img/255.0 | |
| img=np.expand_dims(img,axis=0) | |
| return img | |
| st.title("Üzüm yaprağı hastalıkları sınıflandırma") | |
| st.write("Üzüm yaprağı resmini yükle ve model hastalığı tahmin etsin") | |
| file=st.file_uploader('Bir resim sec',type=['jpg','jpeg','png']) | |
| if file is not None: | |
| img=Image.open(file) | |
| st.image(img,caption='Üzüm Yaprağı') | |
| image= process_image(img) | |
| prediction=model.predict(image) | |
| predicted_class=np.argmax(prediction) | |
| class_names=['Black Rot','ESCA','Healthy','Leaf Blight'] | |
| st.write(class_names[predicted_class]) | |