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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

README.md

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 ---
-title: Flower Classification Fcahpt Hnd2 Tolulope
-emoji: ⚡
-colorFrom: indigo
-colorTo: indigo
+title: Oxford 102 Flower Classification
+emoji: 😻
+colorFrom: red
+colorTo: pink
 sdk: streamlit
-sdk_version: 1.33.0
+sdk_version: 1.25.0
 app_file: app.py
 pinned: false
-license: mit
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# All imports
+import streamlit as st
+import tensorflow as tf
+from PIL import Image
+import io
+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)
+        img = Image.open(io.BytesIO(image_data))
+        img = img.resize((224,224))
+        return img
+    else:
+        return None
+
+def load_model():
+    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()
+    return data
+
+def predict(model, labels, img):
+    img_array = tf.keras.preprocessing.image.img_to_array(img)
+    img_array = tf.expand_dims(img_array, 0)  # Create a batch
+
+    prediction = model.predict(img_array)
+    predicted_class = np.argmax(prediction[0], axis=-1)
+    
+    flower = labels[predicted_class]
+    closeness = np.round(prediction[0][predicted_class] * 100, 2)
+    
+    return flower, closeness
+
+def main():
+    st.title('Flower Classification Using Deep Learning ')
+    st.markdown('### NAME:')
+    st.write('TOLULOPE')
+    st.markdown('### CLASS:')
+    st.write('HND2')
+    st.markdown('### LEVEL:')
+    st.write('400L')
+    st.markdown('---')
+    st.write("This is a demo of an image classification model trained on the Oxford Flower Dataset. The Oxford Flower Dataset, consisting of 102 flower categories. The images have large scale, pose and light variations. In addition, there are categories that have large variations within the category and several very similar categories. The dataset is visualized using isomap with shape and colour features. Link to the dataset is available  @ https://www.kaggle.com/datasets/yousefmohamed20/oxford-102-flower-dataset.. . To test the model, upload an image of a flower and click the 'Run on image' button.")
+    st.markdown('---')
+    model = load_model()
+    labels = load_labels()
+    image = load_image()
+    result = st.button('Run on image')
+    if result and image is not None:
+        st.markdown('**_Calculating results..._**')
+        flower, closeness = predict(model, labels, image)
+        st.markdown(f'<h4 style="color:blue;">Flower Type: <span style="color:black;">{flower}</span></h4>', unsafe_allow_html=True)
+        st.markdown(f'<h4 style="color:green;">Closeness: <span style="color:black;">{closeness}%</span></h4>', unsafe_allow_html=True)
+
+if __name__ == '__main__':
+    main()