Update app.py

app.py

@@ -1261,7 +1261,7 @@ if st.session_state.page == "Home":
          if st.session_state.show_desc:
         # components.html(html_string)  # JavaScript works
             # st.markdown(html_string, unsafe_allow_html=True)
-            image_path = "new.jpg"
+            image_path = "giff.gif"
 
             st.container()
             st.markdown(
@@ -1270,8 +1270,8 @@ if st.session_state.page == "Home":
                         <div class="titles">
                             <h1>Brain Tummor Classfication</br> Using Transfer  learning</h1>
                             <div> This web application utilizes transfer learning to classify kidney ultrasound images</br>
-                            into four categories: HEALTH and TUMOR  Class.
-                            Built with Streamlit and powered by </br>a TensorFlow transfer learning
+                            into two categories: HEALTH and TUMOR  Class.
+                            Built with Streamlit and powered by </br>a Pytorch transfer learning
                             model based on <strong>VGG16</strong>
                             the app provides </br>a simple and efficient way for users 
                             to upload brain scans and receive instant predictions.</br> The model analyzes the image
@@ -1359,7 +1359,7 @@ if st.session_state.page == "Home":
                 placeholder.empty()
                 st.markdown(content, unsafe_allow_html=True)
             else:
-                default_image_path = "new.jpg"
+                default_image_path = "giff.gif"
                 with open(image_path, "rb") as image_file:
                     encoded_image = base64.b64encode(image_file.read()).decode()