Update pages/3_Life Cycle Of ML Project.py

pages/3_Life Cycle Of ML Project.py

@@ -186,48 +186,59 @@ elif st.session_state.page == "unstructured_data":
         st.session_state.page = "introduction_to_image"
 
 # ----------------- Introduction to Image -----------------
+
 elif st.session_state.page == "Introduction_to_image":
-    st.header("🖼️ What is Image")
+    st.header("🖼️ What is an Image?")
     st.markdown("""
-    ###: What is an image?\nAn image is a two-dimensional visual representation of objects, people, scenes, or concepts. It can be captured using devices like cameras, scanners, or created digitally. Images are composed of individual units called pixels, which contain information about brightness and color.
-    Types of Images:
+    ### What is an Image?
+    An image is a two-dimensional visual representation of objects, people, scenes, or concepts. It can be captured using devices like cameras or scanners, or created digitally. Images are composed of individual units called pixels, which contain information about brightness and color.
+
+    #### Types of Images:
     - **Raster Images (Bitmap)**: Composed of a grid of pixels. Common formats include:
-    - JPEG
-    - PNG
-    - GIF
-    - **Vector Images**: Defined by mathematical equations and geometric shapes like lines and curves. Common format:
-    - SVG (Scalable Vector Graphics)
+      - JPEG
+      - PNG
+      - GIF
+    - **Vector Images**: Defined by mathematical equations and geometric shapes (like lines and curves). Common format:
+      - SVG (Scalable Vector Graphics)
     - **3D Images**: Represent objects or scenes in three dimensions, often used for rendering and modeling.
-                  Image Representation:
+
+    #### Image Representation:
     - **Grayscale Image**: Each pixel has a single intensity value, typically ranging from 0 (black) to 255 (white), representing different shades of gray.
     - **Color Image**: Usually represented in the RGB color space, where each pixel consists of three values indicating the intensity of Red, Green, and Blue.
-        Applications of Images:
+
+    #### Applications of Images:
     - **Photography & Visual Media**: Capturing moments and storytelling.
     - **Medical Imaging**: Diagnosing conditions using X-rays, MRIs, etc.
     - **Machine Learning & AI**: Tasks like image classification, object detection, and facial recognition.
     - **Remote Sensing**: Analyzing geographic and environmental data using satellite imagery.
-    - **Graphic Design & Art**: Creating creative visual content for marketing and design.
-        """)
+    - **Graphic Design & Art**: Creating visual content for marketing and design.
+    """)
 
     st.code("""
     from PIL import Image
     import numpy as np
     import matplotlib.pyplot as plt
+
     # Open an image file
     image = Image.open('sample_image.jpg')
     image.show()
+
     # Convert image to grayscale
     gray_image = image.convert('L')
     gray_image.show()
+
     # Resize the image
     resized_image = image.resize((200, 200))
     resized_image.show()
+
     # Rotate the image by 90 degrees
     rotated_image = image.rotate(90)
     rotated_image.show()
+
     # Convert the image to a NumPy array and display its shape
     image_array = np.array(image)
     print(image_array.shape)
+
     # Display the image array as a plot
     plt.imshow(image)
     plt.title("Original Image")
@@ -238,7 +249,8 @@ elif st.session_state.page == "Introduction_to_image":
     st.header("Color Spaces in Machine Learning")
     st.markdown("""
     A color space is a mathematical model for representing colors. In machine learning, different color spaces can be used for preprocessing and analyzing image data, depending on the task.
-    Common Color Spaces:
+
+    #### Common Color Spaces:
     - **RGB (Red, Green, Blue)**: The most common color space for digital images. Each pixel is represented by a combination of three values corresponding to the red, green, and blue channels.
       - **Use Cases**: Image classification, general-purpose image analysis.
     - **HSV (Hue, Saturation, Value)**: Separates color information (hue) from intensity (value), making it useful for tasks where distinguishing between color variations and intensity is important.
@@ -249,13 +261,16 @@ elif st.session_state.page == "Introduction_to_image":
       - **Use Cases**: Color correction, image processing tasks requiring color consistency.
     """)
 
-    # Button to Navigate to Operations Using OpenCV
-    if st.button("Operations Using OpenCV"):
+
+
+ # Button to Navigate to Operations Using OpenCV
+     if st.button("Operations Using OpenCV"):
         st.session_state.page = "operations_using_opencv"
 
-     # Navigation Button
-    if st.button("Back to Data Collection"):
-        st.session_state.page = "data_collection"  
+# Button to Navigate Back to Data Collection
+     if st.button("Back to Data Collection"):
+        st.session_state.page = "data_collection"
+  
         
  #       ---------- OPERATIONS USING OPENCV --------------------------------