JbIPS
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642a5f0
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Parent(s):
6a64fc1
Initial commit
Browse files- app.py +68 -0
- requirements.txt +5 -0
app.py
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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 hf_hub_url, cached_download
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from tf.keras.preprocessing.image import img_to_array
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from tf.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) # Create batch axis
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predictions = model.predict(img_array)
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top4 = predictions.argsort()[0, -1:-5:-1]
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breakdown = []
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for race, acc in zip(np.array(race_names)[top4], predictions[0, top4]):
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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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# Load model
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model = load_model(cached_download(
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hf_hub_url('JbIPS/DogRace', 'saved_model')
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))
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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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requirements.txt
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@@ -0,0 +1,5 @@
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streamlit==1.5.1
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huggingface==0.5.1
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tensorflow==2.8.0
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pillow==9.0.1
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