Update pages/1_Introduction.py

pages/1_Introduction.py

@@ -1,19 +1,12 @@
 import streamlit as st
 from PIL import Image
-import base64
 import io
 
-# Base64 encoded image string (ensure the base64 string is complete)
+# Path to the image generated by DALL-E
+image_path = "/mnt/data/A_scene_showing_a_father_sitting_with_his_child_in.png"
 
-from PIL import Image
-
-image_path = r"C:\Users\USER\Downloads\DALL·E 2024-12-27 18.19.15 - A warm and cozy scene of a father sitting with his 3-year-old child in a living room, showing pictures of dogs and cats. The father is pointing at a p.webp"
-
-try:
-    image = Image.open(image_path)
-    image.show()  # To display the image
-except Exception as e:
-    print(f"Error opening image: {e}")
+# Open the image
+image = Image.open(image_path)
 
 # Title and introduction
 st.title('Understanding Machine Learning: Teaching Machines Like Children')
@@ -25,8 +18,7 @@ In this app, we'll use the analogy of a father teaching his child to explain the
 """)
 
 # Display the image
-
-st.image(r"C:\Users\USER\Downloads\DALL·E 2024-12-27 18.19.15 - A warm and cozy scene of a father sitting with his 3-year-old child in a living room, showing pictures of dogs and cats. The father is pointing at a p.webp", caption='A father teaching his child by showing dog and cat pictures')
+st.image(image, caption="A father teaching his child by showing dog and cat pictures")
 
 # Supervised Learning Explanation
 st.header('Supervised Learning')
@@ -56,3 +48,4 @@ Unsupervised learning helps the machine find structure in data without needing l
 st.write("""
 Machine Learning mirrors how humans, especially children, learn by observing and being guided. This analogy helps simplify the complex concepts behind teaching machines to recognize patterns and make decisions. Just like the child learns over time, machines improve as they are exposed to more data.
 """)
+