Update app.py

app.py

@@ -1,11 +1,8 @@
 # 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():
@@ -13,8 +10,13 @@ def load_image():
     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_models():
+def load_model():
     model_name = 'Model/model.h5'
     model = tf.keras.models.load_model(model_name) 
     return model
@@ -22,54 +24,31 @@ def load_models():
 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)
+    return data
 
-    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)
+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
 
-    percentages = []
-    flowers = []
-    for i in high:
-        key, value = i
-        flowers.append(categories[key])
-        percentages.append(np.round(value*100, 2))
-    return flowers, percentages
+    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('Oxford 102 Flower CLassification Demo')
-    model = load_models()
-    categories = load_labels()
+    st.title('Oxford 102 Flower Classification Demo')
+    model = load_model()
+    labels = load_labels()
     image = load_image()
     result = st.button('Run on image')
-    if result:
+    if result and image is not None:
         st.write('Calculating results...')
-        flowers, percentages = predict(model, categories, image)
-        st.text(flowers)
-        st.text(percentages)
+        flower, closeness = predict(model, labels, image)
+        st.write(f'Flower Type: {flower}')
+        st.write(f'Closeness: {closeness}%')
 
 if __name__ == '__main__':
     main()
-