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Model/mango_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d044730f579c767f367bfd8bbcc96c61f6b8fa2fe28a5284b9d7069b86487a99
+size 107572392

README.md

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 ---
 title: Mango Ripeness Classifier
-emoji: 📚
-colorFrom: purple
-colorTo: yellow
+emoji: 🥭
+colorFrom: red
+colorTo: pink
 sdk: streamlit
-sdk_version: 1.32.0
+sdk_version: 1.25.0
 app_file: app.py
 pinned: false
 ---

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/mango_model.h5'
+    model = tf.keras.models.load_model(model_name) 
+    return model
+
+def load_labels():
+    with open('dataset_labels.txt', 'r') as file:
+        data = file.read().splitlines()
+    mango_dict = dict(enumerate(data, 1))
+    return mango_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('Mango Ripeness Classifier 🥭')
+    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()
+

dataset_labels.txt

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+unripe
+ripe