Upload app.py

app.py

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+#import libraries
+import pandas as pd
+impurt numpy as np
+import streamlit as st
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+from tensorflow_hub.keras_layer import KerasLayer 
+
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+
+#import pickle
+import pickle
+
+#load model
+def run():
+    st.image('https://i.ytimg.com/vi/Y7nGCB3S5Ww/maxresdefault.jpg', use_container_width=True)
+    st.title("Skin Type Prediction Model")
+    st.write("Upload an image to know your skin type!")
+    file = st.file_uploader("Upload an image", type=["jpg", "png"])
+
+    model = load_model('model_aug.keras', custom_objects={'KerasLayer': KerasLayer})
+    target_size=(220, 220)
+
+    def import_and_predict(image_data, model):
+        image = load_img(image_data, target_size=(220,220))
+        img_array = img_to_array(image)
+        img_array = tf.expand_dims(img_array, 0)
+
+        #Normalize image
+        img_array = img_array/255
+
+        #make prediction
+        predictions = model.predict(img_array)
+
+        #Get class with the highest possibility
+        idx = np.where(predictions => 0.5, 1, 0).item()
+
+        type = ['oily', 'dry', 'normal']
+        result = f'Prediction: {type[idx]}'
+
+        return result
+    
+    if file is None:
+        st.text("Please upload in image file")
+    else:
+        result = import_and_predict(file, model)
+        st.image(file)
+        st.write(result)
+    
+    if __name__ == "__main__"
+        run