| import numpy as np |
| from tensorflow.keras.models import load_model |
| from PIL import Image |
| import streamlit as st |
|
|
| |
| model = load_model('src/dates_classifier_model.h5') |
|
|
| def process_image(img): |
| img = img.resize((224, 224)) |
| img = np.array(img) / 255.0 |
| img = np.expand_dims(img, axis=0) |
| return img |
|
|
| st.title('Hurma Resmi Sınıflandırma') |
| st.write('Bir hurma resmi yükleyin, hangi tür olduğunu tahmin edelim.') |
|
|
| file = st.file_uploader('Bir Resim Seçin', type=['jpg', 'jpeg', 'png']) |
|
|
| if file is not None: |
| img = Image.open(file) |
| st.image(img, caption='Yüklenen Resim', use_column_width=True) |
|
|
| processed_image = process_image(img) |
| prediction = model.predict(processed_image) |
| predicted_class = np.argmax(prediction) |
|
|
| class_names = [ |
| 'Rutab', 'Meneifi', 'Sokari', 'Galaxy', 'Shaishe', |
| 'Medjool', 'Ajwa', 'Nabtat Ali', 'Sugaey' |
| ] |
|
|
| st.write(f'Tahmin Edilen Sınıf: **{class_names[predicted_class]}**') |
|
|
