| import streamlit as st
|
| from tensorflow.keras.models import load_model
|
| from PIL import Image
|
| import numpy as np
|
|
|
| model=load_model('hurma_cnn_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("Hurma Resmi Siniflandirma :palm_tree:")
|
| st.write("Hurma cinsini tahmin etmek için hurma resmi ekleyin")
|
|
|
| file=st.file_uploader('Bir Resim Sec',type=['jpg','jpeg','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=["Ajwa", "Galaxy", "Medjool", "Meneifi", "Nabtat Ali", "Rutab", "Shaishe", "Sokari", "Sugaey"]
|
| st.write(class_names[predicted_class])
|
|
|
