Update app.py

app.py

@@ -154,13 +154,18 @@ if selected == 'Tools and Experience':
 
 elif selected == 'Projects':
     st.header('Projects')
-    st.write('I have made more than 50 projects in fields including Cnn, Gan, Machine Learning, Yolo object detection and segmentation, Pix2pix, Cyclegan and more')
+    st.write('I have made more than 50 projects in fields including CNN, GAN, Machine Learning, Yolo object detection and segmentation, Pix2pix, Cyclegan and more')
     st.write('Below are some models(one from each category)')
     
-    options = {'Cyclegan':1, 'DCGAN':2,'YOLO Segmentation':3,'Unet':4,'Machine Learning Text classifier':5}
+    options = {'Cyclegan':1, 'DCGAN':2,'YOLO Segmentation':3,'Machine Learning Text classifier':4}
     selected_option = st.selectbox('Select an option', options)
 
     if selected_option == 'Unet':
+        st.header('Description')
+        st.write('This is a UNET model that segments the waterbodies in the image')
+        st.header('Architecture')
+        st.write('The model is of unet architecture to preserve the spatial informations in the images after applying Conv2D')
+        
         imgs = img_prep(['unet1.jpg','unet2.jpg'])
         col1,col2 = st.columns(2)
         with col1:
@@ -177,6 +182,12 @@ elif selected == 'Projects':
 
                 
     if selected_option == 'Cyclegan':
+        st.header('Description')
+        st.write('This is a CycleGAN model that turns an input image into an Image of a Monet Painting')
+        st.header('Architecture')
+        st.write('The Generator model is the prebuild tensorflow pix2pix generator model')
+        st.write('The Discriminator model is the discriminator model from the same module')
+        
         imgs = img_prep(['cyclegan1.jpg','cyclegan2.jpg'])
         col1,col2 = st.columns(2)
         with col1:
@@ -196,6 +207,8 @@ elif selected == 'Projects':
         st.header('Description')
         st.write('This is a DCGAN model that turns a random noise vector into an Image of a dog(that sometimes turns out disformed) using Convolutional2D Transpose layers')
         st.header('Architecture')
+        st.write('The Generator model is just a set of Convolutional2D Transpose, BatchNormalization and Leaky relu')
+        st.write('The Discriminator model is a very simple model with Convolutional2D, Dropout and Leaky Relu')
         model = tf.keras.models.load_model('Portfolio Projects/doggen3.h5')
 
 # Create a button
@@ -209,5 +222,9 @@ elif selected == 'Projects':
             pred = pred * 0.5 + 0.5  # Normalize the pixel values
             pred = np.squeeze(pred)  # Remove singleton dimensions if any
             st.image(pred,use_column_width=True)
+
+    st.write('To see other projects, You can Visit my profile on HuggingFace')
+    st.link_button('\n \t \t \t HuggingFace Account \t \t \r \n','https://huggingface.co/Beasto')
 else:
-    st.write(selected)
+    st.header('Contact me')
+