Update app.py

app.py

@@ -1,51 +1,51 @@
-#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__"
+#import libraries
+import pandas as pd
+import 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