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7f6215b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 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__"
run |