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import tensorflow as tf | |
from tensorflow.keras.models import load_model | |
import streamlit as st | |
from PIL import Image | |
import numpy as np | |
# Load Sequential Model | |
model = load_model('model.h5') | |
def run(): | |
upload = st.file_uploader("Please upload an image", type=["jpg", "png"]) | |
if upload is not None: | |
img = Image.open(upload) | |
resize_image = img.resize((32, 32)) | |
X = np.array(resize_image) | |
X = X/255 | |
X_inf = np.expand_dims(X, axis=0) | |
inf_pred = model.predict(X_inf) | |
inf_class =np.argmax(inf_pred,axis=1) | |
st.image(upload) | |
st.write('## Prediction : ', inf_class[0]) | |
st.write('## Metadata : ') | |
st.image('meta.png') | |
if __name__ == '__main__': | |
run() |