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import streamlit as st
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
import cv2
# Eğitilmiş modeli yükle
model = load_model('traffic_classifier.h5')
def process_image(image):
img=np.array(image)
if img.shape[-1] == 4:
img = img[:,:,:3]
img=cv2.resize(img,(30,30))
img=img/255.0
img=np.expand_dims(img,axis=0)
return img
st.title('Traffic Sign Image Classifier')
st.write('Upload a image and model will predict which traffic sign it is.')
file = st.file_uploader('Choose a image...', type=['jpg', 'jpeg', 'png'])
if file is not None:
img = Image.open(file)
st.image(img, caption='Uploaded Image')
image = process_image(img)
prediction = model.predict(image)
predicted_class = np.argmax(prediction)
class_names = [f"Class {i}" for i in range(43)] # 43 sınıf için sınıf isimleri
st.write(f"Predicted class index: {predicted_class}")
st.write(f"Predicted class: {class_names[predicted_class]}") |