Update app.py

app.py

@@ -5,18 +5,19 @@ from PIL import Image
 import numpy as np
 import cv2
 from huggingface_hub import from_pretrained_keras
+
+
 try:
     model=from_pretrained_keras("SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net")
 except:
     model=tf.keras.models.load_model("dental_xray_seg.h5")
     pass
-st.header("Teeth segmentation  X-ray Image Using Machine learning")
-
-examples=["107.png"]
+    
+st.header(" Teeth segmentation in X-ray Image Using Machine learing")
+examples=["107.png","108.png"]
 def load_image(image_file):
 	img = Image.open(image_file)
 	return img
-
 def convert_one_channel(img):
     #some images have 3 channels , although they are grayscale image
     if len(img.shape)>2:
@@ -32,14 +33,28 @@ def convert_rgb(img):
         return img
     else:
         return img
-st.subheader("Upload Dental Panoramic X-ray Image Image")
+    
+    
+st.subheader("Upload Dental X-ray Image Image")
 image_file = st.file_uploader("Upload Images", type=["png","jpg","jpeg"])
-    ex=load_image(examples)
+
+
+col1, col2 = st.columns(2)
+with col1:
+    ex=load_image(examples[0])
     st.image(ex,width=200)
     if st.button('Example 1'):
-        image_file=examples[0]  
+        image_file=examples[0]
+
+with col2:
+    ex1=load_image(examples[1])
+    st.image(ex1,width=200)
+    if st.button('Example 2'):
+        image_file=examples[1]
 if image_file is not None:
+
       img=load_image(image_file)
+      
       st.text("Making A Prediction ....")
       st.image(img,width=850)
       
@@ -67,4 +82,4 @@ if image_file is not None:
           st.write(output.shape)
           st.image(output,width=850)
 
-      st.text("DONE ! ....")
+      st.text("DONE")