Update app.py

app.py

@@ -3,6 +3,8 @@ from streamlit_option_menu import option_menu
 import json
 from streamlit_lottie import st_lottie
 from PIL import Image
+from tensorflow.keras.models import load_model
+import numpy as np
 
 page_bg_img = '''
 <style>
@@ -14,7 +16,20 @@ body {
 '''
 
 st.markdown(page_bg_img, unsafe_allow_html=True)
-
+def img_prep(paths):
+    out = []
+    for i in paths:
+        img = Image.open(i)
+        img = img.resize((256,256))
+        img = ((np.asarray(img))/127.5)-1
+        out.append(img)
+
+def model_out(img,model_path):
+    img = np.expand_dims(img,0)
+    model = load_model(model_path)
+    pred = model.predict(img)
+    return pred[0]
+    
 def lottiemaker(path):
     with open(path) as f:
         return json.load(f)
@@ -24,6 +39,7 @@ def loadimg(path):
     img = img.resize((150,150))
     return img
 
+
 selected = option_menu(
     menu_title=None,
     options = ['About me','Tools and Experience','Projects','Contacts'],
@@ -144,7 +160,22 @@ elif selected == 'Projects':
     
     options = {'Cyclegan':1, 'Pix2Pix':2, 'DCGAN':3,'YOLO Segmentation':4,'Unet':5,'Machine Learning Text classifier':6}
     selected_option = st.selectbox('Select an option', options)
-    
+
+    if selected_option == 'Unet':
+        imgs = img_prep(['unet1','unet2'])
+        col1,col2 = st.columns(2)
+        with col1:
+            st.image(imgs[0],use_column_width=False)
+        with col2:
+            st.image(imgs[1],use_column_width=False)
+
+        img_options = {'img1':1, 'img2':2}
+        img_selected_option = st.selectbox('Select an option', options)
+        if img_selected_option == 'img1':
+            st.image(imgs[0],use_column_width=False)
+        if img_selected_option == 'img2':
+            st.image(imgs[1],use_column_width=False)
+        
     st.write('You selected:', selected_option)
 else:
     st.write(selected)