| 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) |
|
|
| 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) |
| |
| 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() |
|
|
