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import json
import requests
import numpy as np
import streamlit as st
import tensorflow as tf
from huggingface_hub import from_pretrained_keras
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
from PIL import Image
from io import BytesIO
def make_prediction(url, model, race_names):
response = requests.get(url)
img = Image.open(BytesIO(response.content)).resize((180, 180))
img_array = img_to_array(img)
img_array = tf.expand_dims(img_array, 0) # Create batch axis
predictions = model.predict(img_array)
top5 = predictions.argsort()[0, -1:-6:-1]
breakdown = []
for race, acc in zip(np.array(race_names)[top5], predictions[0, top5]):
breakdown.append(f'{race} at {acc:.2%}')
return breakdown
def main():
race_file = open('race_names.json', 'r')
race_names = json.load(race_file)
# Load model
model = from_pretrained_keras('JbIPS/DogRace')
st.set_page_config("Who let's the dogs out")
st.title('Quelle est ta race de chien totem ?')
st.text('''
Pour découvrir ta race de chien, colle l'adresse d'une photo.
''')
url = st.text_input('URL de la photo')
predict_btn = st.button('Prédire')
if predict_btn:
pred = make_prediction(url, model, race_names)
main_race = pred[0].split(' at')[0].lower()
main_race = '/'.join(reversed(main_race.replace('-', '').split(' ')))
if main_race.startswith('husky'):
main_race = main_race.split('/')[0]
col1, col2 = st.columns(2)
with col1:
st.image(url)
with col2:
response = requests.get(url=f'https://dog.ceo/api/breed/{main_race}/images/random').json()
if response['status'] == 'success':
st.image(response['message'])
else:
st.text(main_race)
st.text(response)
st.write('Les races qui te correspondent le plus sont :')
for race in pred:
st.write(race)
if __name__ == '__main__':
main()
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