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