files added to push to huggingfaces

Model/model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:73ed908fb076b4562d9fa52f9b23a363e891154fd7ef11c2bbb82d10a57a3956
+size 37247336

Oxford-102_Flower_dataset_labels.txt

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+ pink primrose
+ hard-leaved pocket orchid
+ canterbury bells
+ sweet pea
+ english marigold
+ tiger lily
+ moon orchid
+ bird of paradise
+ monkshood
+ globe thistle
+ snapdragon
+ colt's foot
+ king protea
+ spear thistle
+ yellow iris
+ globe-flower
+ purple coneflower
+ peruvian lily
+ balloon flower
+ giant white arum lily
+ fire lily
+ pincushion flower
+ fritillary
+ red ginger
+ grape hyacinth
+ corn poppy
+ prince of wales feathers
+ stemless gentian
+ artichoke
+ sweet william
+ carnation
+ garden phlox
+ love in the mist
+ mexican aster
+ alpine sea holly
+ ruby-lipped cattleya
+ cape flower
+ great masterwort
+ siam tulip
+ lenten rose
+ barbeton daisy
+ daffodil
+ sword lily
+ poinsettia
+ bolero deep blue
+ wallflower
+ marigold
+ buttercup
+ oxeye daisy
+ common dandelion
+ petunia
+ wild pansy
+ primula
+ sunflower
+ pelargonium
+ bishop of llandaff
+ gaura
+ geranium
+ orange dahlia
+ pink-yellow dahlia
+ cautleya spicata
+ japanese anemone
+ black-eyed susan
+ silverbush
+ californian poppy
+ osteospermum
+ spring crocus
+ bearded iris
+ windflower
+ tree poppy
+ gazania
+ azalea
+ water lily
+ rose
+ thorn apple
+ morning glory
+ passion flower
+ lotus
+ toad lily
+ anthurium
+ frangipani
+ clematis
+ hibiscus
+ columbine
+ desert-rose
+ tree mallow
+ magnolia
+ cyclamen
+ watercress
+ canna lily
+ hippeastrum
+ bee balm
+ ball moss
+ foxglove
+ bougainvillea
+ camellia
+ mallow
+ mexican petunia
+ bromelia
+ blanket flower
+ trumpet creeper
+ blackberry lily

app.py

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+# All imports
+import streamlit as st
+import tensorflow as tf
+from tensorflow import keras
+from PIL import Image
+from tensorflow.keras.preprocessing import image
+import io
+from collections import Counter
+import numpy as np
+
+def load_image():
+    uploaded_file = st.file_uploader(label='Pick an image to test')
+    if uploaded_file is not None:
+        image_data = uploaded_file.getvalue()
+        st.image(image_data)
+
+def load_models():
+    model_name = 'Model/model.h5'
+    model = tf.keras.models.load_model(model_name) 
+    return model
+
+def load_labels():
+    with open('Oxford-102_Flower_dataset_labels.txt', 'r') as file:
+        data = file.read().splitlines()
+    flower_dict = dict(enumerate(data, 1))
+    return flower_dict
+    
+def load_image():
+     uploaded_file = st.file_uploader(label='Pick an image to test')
+     if uploaded_file is not None:
+         image_data = uploaded_file.getvalue()
+         st.image(image_data)
+         img = Image.open(io.BytesIO(image_data))
+         img = img.resize((224,224))
+         return img
+     else:
+         return None
+    
+def predict(model, categories, img):
+    img_array = tf.keras.preprocessing.image.img_to_array(img)
+    prediction = [img_array]
+    prediction_test = [1]
+    test_ds = tf.data.Dataset.from_tensor_slices((prediction, prediction_test))
+    test_ds = test_ds.cache().batch(32).prefetch(buffer_size = tf.data.experimental.AUTOTUNE)
+
+    prediction = model.predict(test_ds)
+    prediction_dict = dict(enumerate(prediction.flatten(), 1))
+    k = Counter(prediction_dict)
+ 
+    # Finding 3 highest values
+    high = k.most_common(3)
+
+    percentages = []
+    flowers = []
+    for i in high:
+        key, value = i
+        flowers.append(categories[key])
+        percentages.append(np.round(value*100, 2))
+    return flowers, percentages
+
+def main():
+    st.title('Oxford 102 Flower CLassification Demo')
+    model = load_models()
+    categories = load_labels()
+    image = load_image()
+    result = st.button('Run on image')
+    if result:
+        st.write('Calculating results...')
+        flowers, percentages = predict(model, categories, image)
+        st.text(flowers)
+        st.text(percentages)
+
+if __name__ == '__main__':
+    main()
+