app1.py

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]}")