Update app.py

app.py

@@ -210,33 +210,3 @@ st.markdown("""
 
 **Thank you for exploring this demonstration!**  
 """)
-
-# -----------------------------------------------------------------
-# How the Code Works - Explanation
-# -----------------------------------------------------------------
-st.header("How This Code Works")
-st.markdown("""
-1. **Utility Functions**  
-   - **`generate_colorful_image`**: Creates a synthetic image with a customizable proportion of blue pixels. This simulates different real-world scenarios, such as varying amounts of a brand color on packaging.
-   - **`simple_threshold_blue`**: Uses basic HSV thresholding to identify blue pixels. Provides a quick, broad detection method.
-   - **`advanced_threshold_blue`**: Combines multiple ranges for different shades of blue and applies morphological operations for noise reduction, giving more robust results.
-   - **`plot_color_histogram`**: Plots separate color channel (B, G, R) histograms to visualize the distribution of pixel intensities.
-
-2. **Streamlit Layout**  
-   - **`Sidebar`**: Controls to adjust the fraction of blueish pixels and the image size. A button to generate a new random image is also included.
-   - **`Main Page`**: 
-     - Displays the generated image and its color histogram side by side.
-     - Shows the simple vs. advanced detection masks with the calculated percentage of blue. 
-     - Progress bars indicate the proportion of blue visually.
-   
-3. **Industry 4.0 Context**  
-   - By integrating this approach with IoT devices, large-scale manufacturing lines can automate color checks. The masks and percentage calculations serve as a foundation for real-time QA alerts, data logging, and future analytics.
-
-4. **Execution Flow**  
-   1. When the user clicks "`Generate New Random Image`," it calls `generate_colorful_image()` to create a fresh synthetic image.
-   2. The image is converted to RGB for display. Meanwhile, `plot_color_histogram()` renders a histogram for deeper insight into color distribution.
-   3. The **`simple_threshold_blue`** and **`advanced_threshold_blue`** functions each produce their own masks and calculations, displayed in the main interface.
-   4. By comparing the results of both methods, users can see the differences in detection accuracy and how morphological operations can refine the result.
-
-This cohesive setup ensures a **user-friendly**, **real-time** demonstration of how color segmentation can be applied to industrial use cases—especially relevant for ITC PSPD's commitments to **quality** and **innovation**.
-""")