Upload app.py

app.py

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+import cv2
+import time
+from PIL import Image, ImageOps
+import numpy as np
+import streamlit as st
+import tensorflow as tf
+
+@st.cache(allow_output_mutation=True)
+def load_model():
+    model=tf.keras.models.load_model('MN21.h5')
+    return model
+with st.spinner('Model is being loaded..'):
+    model=load_model()
+
+st.write("""# SaferNet with AI""")
+
+file = st.file_uploader("Please upload an image to classify", type=["jpg", "png", "jpeg"])
+def import_and_predict(image_data, model):
+    size = (224,224)    
+    image = ImageOps.fit(image_data, size, Image.ANTIALIAS)
+    image = np.asarray(image)
+    img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    #img_resize = (cv2.resize(img, dsize=(75, 75),    interpolation=cv2.INTER_CUBIC))/255.
+    
+    img_reshape = img[np.newaxis,...]
+    
+    st.write(img_reshape.shape)
+    
+    start = time.time()
+    prediction = model.predict(img_reshape)
+    end = time.time()
+    time_take = end - start
+    return prediction, time_take 
+
+if file is None:
+    st.text("Please upload an image file")
+else:
+    image = Image.open(file)
+    st.image(image, use_column_width=True)
+    predictions, time_take = import_and_predict(image, model)
+    st.write("Time taken to predict is ", time_take, "second")
+    st.write(predictions)
+    print(predictions)
+    
+    print(np.argmax(predictions))
+    st.write(np.argmax(predictions))
+    
+    if predictions[0][0] < 0.5:
+        st.write("This is SFW image :sunglasses:")
+    else:
+        st.write("This is NSFW image")