Initial commit

app.py

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+import json
+import requests
+import numpy as np
+import streamlit as st
+import tensorflow as tf
+from huggingface_hub import hf_hub_url, cached_download
+from tf.keras.preprocessing.image import img_to_array
+from tf.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)
+
+    top4 = predictions.argsort()[0, -1:-5:-1]
+
+    breakdown = []
+    for race, acc in zip(np.array(race_names)[top4], predictions[0, top4]):
+        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 = load_model(cached_download(
+        hf_hub_url('JbIPS/DogRace', 'saved_model')
+    ))
+
+    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()

requirements.txt

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+streamlit==1.5.1
+huggingface==0.5.1
+tensorflow==2.8.0
+pillow==9.0.1
+