Create app.py

app.py

@@ -0,0 +1,20 @@
+import tensorflow as tf
+model=tf.keras.models.load_model('model.h5')
+import streamlit as st
+st.header("Adidas VS Nike Classification")
+categories=['Adidas','NIKE']
+from PIL import Image
+uploaded_image=st.file_uploader("Upload image",type=["jpg","jpeg","webp"])
+import numpy as np
+import cv2
+if(uploaded_image!=None):
+    display_image=Image.open(uploaded_image)
+    st.image(display_image,width=200)
+    if st.button("Predict"):
+        img = np.array(display_image)
+        img=cv2.resize(img,(150,150))
+        img=img/255.0
+        img=img.reshape(1,150,150,3)
+        pred=model.predict(img)
+        print(pred[0][0])
+        st.text(categories[round(pred[0][0])])