Create app.py

app.py

@@ -0,0 +1,47 @@
+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.applications.imagenet_utils import decode_predictions
+import cv2
+from PIL import Image, ImageOps
+import numpy as np
+ 
+@st.cache(allow_output_mutation=True)
+def load_model():
+  model=tf.keras.models.load_model('keras.h5')
+  return model
+with st.spinner('Model is being loaded..'):
+  model=load_model()
+ 
+st.write("""
+         # Image Classification
+         """
+         )
+ 
+file = st.file_uploader("Upload the image to be classified U0001F447", type=["jpg", "png"])
+st.set_option('deprecation.showfileUploaderEncoding', False)
+ 
+def upload_predict(upload_image, model):
+    
+        size = (180,180)    
+        image = ImageOps.fit(upload_image, size, Image.ANTIALIAS)
+        image = np.asarray(image)
+        img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        img_resize = cv2.resize(img, dsize=(224, 224),interpolation=cv2.INTER_CUBIC)
+        
+        img_reshape = img_resize[np.newaxis,...]
+    
+        prediction = model.predict(img_reshape)
+        pred_class=decode_predictions(prediction,top=1)
+        
+        return pred_class
+if file is None:
+    st.text("Please upload an image file")
+else:
+    image = Image.open(file)
+    st.image(image, use_column_width=True)
+    predictions = upload_predict(image, model)
+    image_class = str(predictions[0][0][1])
+    score=np.round(predictions[0][0][2]) 
+    st.write("The image is classified as",image_class)
+    st.write("The similarity score is approximately",score)
+    print("The image is classified as ",image_class, "with a similarity score of",score)