Update introduction.py

introduction.py

@@ -73,7 +73,7 @@ def Show_introduction():
     if model_choice == "Convolutional Neural Network (CNN)":
         st.subheader("🌀 Convolutional Neural Network (CNN)")
         cnn_image = Image.open("Convolutional-Neural-Network.jpg")  # Replace with your actual image file
-        st.image(cnn_image, caption="Typical CNN architecture", use_container_width=True)
+        st.image(cnn_image, caption="Typical CNN architecture")
         st.markdown("""
         CNNs are specialized deep learning models for image processing.  
         They consist of layers that automatically learn to detect features like edges, textures, and patterns in images.
@@ -89,7 +89,7 @@ def Show_introduction():
     elif model_choice == "Vision Transformer (ViT)":
         st.subheader("🧠 Vision Transformer (ViT)")
         vit_image = Image.open("vit.jpg")  # Replace with your actual image file
-        st.image(vit_image, caption="Vision Transformer concept", use_container_width=True)
+        st.image(vit_image, caption="Vision Transformer concept")
         st.markdown("""
         ViTs bring the power of transformer models to the vision domain by splitting images into patches and processing them using self-attention — a technique originally used in NLP.
 
@@ -104,7 +104,7 @@ def Show_introduction():
     elif model_choice == "VGG":
         st.subheader("🏗️ VGG Network")
         vgg_image = Image.open("new41.jpg")  # Replace with your actual image file
-        st.image(vgg_image, caption="VGG architecture overview", use_container_width=True)
+        st.image(vgg_image, caption="VGG architecture overview")
         st.markdown("""
         The VGG model, introduced by the Visual Geometry Group at Oxford, is known for its deep yet simple architecture using small (3x3) convolution filters.