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import streamlit as st
import numpy as np
from PIL import Image
from keras.models import load_model
from sklearn.preprocessing import LabelEncoder
import pickle
# das trainierte Modell und den LabelEncoder laden
model = load_model('dog_breed_classifier.h5')
with open('label_encoder.pkl', 'rb') as f:
label_encoder = pickle.load(f)
def predict_breed(image, model, label_encoder):
image = image.resize((128, 128))
image = np.expand_dims(np.array(image), axis=0)
predictions = model.predict(image)
predicted_breed = label_encoder.inverse_transform([np.argmax(predictions)])
return predicted_breed[0]
# Streamlit App Titel
st.title("DogID - Finde die Rasse eines Hundes heraus!")
# Bild hochladen
uploaded_file = st.file_uploader("Füge hier deinen Freund auf vier Beinen ein", type="jpg")
if uploaded_file is not None:
# Bild anzeigen
image = Image.open(uploaded_file)
st.image(image, caption='Dein hochgeladenes Bild.', use_column_width=True)
# Vorhersage treffen
breed = predict_breed(image, model, label_encoder)
st.write(f'Toll - Dieser Hund ist ein {breed}!') |