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╔══════════════════════════════════════════════════════════════════════════════╗
║ ║
║ 🎓 CISC 121 - OOP Sorting & Searching Visualizer ║
║ ║
║ Queen's University - Introduction to Computing Science I ║
║ ║
║ This application demonstrates Object-Oriented Programming concepts ║
║ through interactive visualization of sorting and searching algorithms. ║
║ ║
║ HOW TO RUN: python app_oop_gradio.py ║
║ ║
╚══════════════════════════════════════════════════════════════════════════════╝
📚 PHASE 5: Gradio UI
This is the final phase - creating a user-friendly web interface that:
1. Allows capturing/uploading gesture images
2. Displays the image list with gesture recognition
3. Lets users run sorting/searching algorithms
4. Visualizes each step of the algorithm
The UI demonstrates COMPOSITION - the GradioApp class composes:
- ImageList (data management)
- SortingAlgorithm / SearchAlgorithm (algorithm execution)
- Visualizer (step-by-step display)
"""
# ==============================================================================
# IMPORTS
# ==============================================================================
import gradio as gr
from PIL import Image
import os
from typing import List, Tuple, Optional
# Import our OOP package
from oop_sorting_teaching import (
# Models
GestureRanking,
GestureImage,
ImageList,
StepType,
Step,
# Sorting
BubbleSort,
MergeSort,
QuickSort,
PivotStrategy,
PartitionScheme,
# Searching
LinearSearch,
BinarySearch,
# Visualization
Visualizer,
VisualizationConfig,
RendererFactory,
)
# Try to import transformers for gesture recognition
try:
from transformers import pipeline
CLASSIFIER_AVAILABLE = True
except ImportError:
CLASSIFIER_AVAILABLE = False
print("⚠️ transformers not installed. Using manual gesture selection.")
# ==============================================================================
# CONFIGURATION
# ==============================================================================
MODEL_NAME = "dima806/hand_gestures_image_detection"
HF_TOKEN = os.environ.get("HF_TOKEN", None)
APP_TITLE = "## 🎓 CISC 121 - OOP Sorting & Searching Visualizer"
APP_DESCRIPTION = """
**Learn Object-Oriented Programming through Algorithm Visualization!**
This app demonstrates key OOP concepts:
- 📦 **Classes & Objects**: GestureImage, ImageList, Algorithms
- 🎭 **Inheritance**: All sorting algorithms inherit from SortingAlgorithm
- 🔄 **Polymorphism**: Swap between algorithms seamlessly
- 🏭 **Factory Pattern**: RendererFactory creates the right visualizer
**How to use:**
1. **Add images** using the buttons below (capture or manual)
2. **View your list** of gesture images
3. **Run an algorithm** to see step-by-step visualization
4. **Navigate steps** to understand how the algorithm works
"""
# ==============================================================================
# GRADIO APP CLASS
# ==============================================================================
class GradioApp:
"""
📚 CONCEPT: Composition
The GradioApp class COMPOSES (contains) other objects:
- ImageList for managing captured images
- Visualizer for displaying algorithm steps
- Classifier for gesture recognition (if available)
This is the Controller in MVC pattern - it coordinates
between user interface (View) and data/logic (Model).
"""
def __init__(self):
"""Initialize the application state."""
self.image_list = ImageList()
self.visualizer = Visualizer(VisualizationConfig(
show_statistics=True,
show_legend=True,
image_size=60
))
self._capture_count = 0
# Initialize classifier if available
self.classifier = None
if CLASSIFIER_AVAILABLE:
try:
self.classifier = pipeline(
"image-classification",
model=MODEL_NAME,
token=HF_TOKEN
)
print(f"✅ Loaded model: {MODEL_NAME}")
except Exception as e:
print(f"⚠️ Could not load model: {e}")
# -------------------------------------------------------------------------
# Image Management Methods
# -------------------------------------------------------------------------
def add_manual_gesture(self, gesture_name: str) -> Tuple[str, str]:
"""
Add a gesture image manually (without camera).
Returns:
Tuple of (image_list_html, status_message)
"""
if not gesture_name:
return self._render_image_list(), "⚠️ Please select a gesture"
self._capture_count += 1
self.image_list.add_new(gesture_name)
return (
self._render_image_list(),
f"✅ Added {GestureRanking.get_emoji(gesture_name)} {gesture_name} (#{self._capture_count})"
)
def add_from_image(self, image: Image.Image) -> Tuple[str, str]:
"""
Add a gesture from an uploaded/captured image.
Uses AI classification if available, otherwise prompts for manual selection.
"""
if image is None:
return self._render_image_list(), "⚠️ No image provided"
if self.classifier:
try:
# Classify the image
results = self.classifier(image)
if results:
top_result = results[0]
gesture_name = top_result['label'].lower()
confidence = top_result['score']
self._capture_count += 1
img = GestureImage.create_from_prediction(
gesture_name=gesture_name,
capture_id=self._capture_count,
image=image,
confidence=confidence
)
self.image_list._save_state() # Save before modifying
self.image_list._images.append(img)
return (
self._render_image_list(),
f"✅ Detected: {img.emoji} {gesture_name} ({confidence:.1%} confidence)"
)
except Exception as e:
return self._render_image_list(), f"⚠️ Classification error: {e}"
return self._render_image_list(), "⚠️ No classifier available. Use manual gesture selection."
def remove_image(self, index: int) -> Tuple[str, str]:
"""Remove an image at the given index."""
if 0 <= index < len(self.image_list):
removed = self.image_list[index]
self.image_list.remove(index)
return self._render_image_list(), f"✅ Removed {removed}"
return self._render_image_list(), "⚠️ Invalid index"
def shuffle_images(self) -> Tuple[str, str]:
"""Shuffle the image list."""
self.image_list.shuffle()
return self._render_image_list(), "🔀 Shuffled!"
def clear_images(self) -> Tuple[str, str]:
"""Clear all images."""
count = len(self.image_list)
self.image_list.clear()
self._capture_count = 0
self.visualizer.reset()
return self._render_image_list(), f"🗑️ Cleared {count} images"
def undo_action(self) -> Tuple[str, str]:
"""Undo the last action."""
if self.image_list.undo():
return self._render_image_list(), "↩️ Undone!"
return self._render_image_list(), "⚠️ Nothing to undo"
def add_sample_data(self) -> Tuple[str, str]:
"""Add sample data for testing."""
gestures = ['fist', 'peace', 'like', 'peace', 'ok', 'fist']
for g in gestures:
self._capture_count += 1
self.image_list.add_new(g)
return self._render_image_list(), f"✅ Added {len(gestures)} sample gestures"
def add_instability_demo(self) -> Tuple[str, str]:
"""
Add data specifically designed to demonstrate Quick Sort instability.
📚 EDUCATIONAL PURPOSE:
This creates a scenario where Quick Sort will reorder equal elements,
demonstrating that it's an UNSTABLE sorting algorithm.
Setup: [✌️₁] [✌️₂] [✌️₃] [✊₄]
After Quick Sort: The peace signs may be reordered (e.g., ₂,₃,₁)
After Bubble/Merge Sort: Order preserved (₁,₂,₃)
"""
self.clear_images()
# Three peace signs followed by a lower-ranked fist
demo_gestures = ['peace', 'peace', 'peace', 'fist']
for g in demo_gestures:
self._capture_count += 1
self.image_list.add_new(g)
return (
self._render_image_list(),
"🎓 Instability Demo: [✌️₁][✌️₂][✌️₃][✊₄]\n"
"Try Quick Sort vs Bubble Sort - watch the subscript order!"
)
def add_worst_case_demo(self) -> Tuple[str, str]:
"""
Add already-sorted data to demonstrate worst-case for Quick Sort.
📚 EDUCATIONAL PURPOSE:
When data is already sorted and we use First Pivot strategy,
Quick Sort degrades to O(n²) - its worst case!
"""
self.clear_images()
# Sorted order: fist(1) < peace(2) < like(3) < ok(4) < call(5)
sorted_gestures = ['fist', 'peace', 'like', 'ok', 'call']
for g in sorted_gestures:
self._capture_count += 1
self.image_list.add_new(g)
return (
self._render_image_list(),
"🎓 Worst-Case Demo: Already sorted data!\n"
"Quick Sort with First Pivot → O(n²)\n"
"Try Median-of-3 or Random pivot to see the difference."
)
def add_binary_search_demo(self) -> Tuple[str, str]:
"""
Add sorted data for binary search demonstration.
📚 EDUCATIONAL PURPOSE:
Binary search requires sorted data. This preset shows
how O(log n) is much faster than O(n) linear search.
"""
self.clear_images()
# Create larger sorted dataset for more dramatic comparison
gestures = ['fist', 'fist', 'peace', 'peace', 'like', 'like',
'ok', 'ok', 'call', 'call', 'palm', 'palm']
for g in gestures:
self._capture_count += 1
self.image_list.add_new(g)
return (
self._render_image_list(),
"🎓 Search Demo: 12 sorted elements\n"
"Linear Search: up to 12 comparisons\n"
"Binary Search: at most 4 comparisons (log₂12 ≈ 3.6)"
)
# -------------------------------------------------------------------------
# Algorithm Execution Methods
# -------------------------------------------------------------------------
def run_sort(self, algorithm_name: str, pivot_strategy: str = "first",
partition_scheme: str = "2-way") -> Tuple[str, str, str]:
"""
Run a sorting algorithm on the image list.
Returns:
Tuple of (visualization_html, image_list_html, status_message)
"""
if len(self.image_list) < 2:
return (
self.visualizer.render_current(),
self._render_image_list(),
"⚠️ Need at least 2 images to sort"
)
# Create the algorithm instance
if algorithm_name == "Bubble Sort":
algo = BubbleSort()
elif algorithm_name == "Merge Sort":
algo = MergeSort()
elif algorithm_name == "Quick Sort":
# Map string to enum
pivot_map = {
"first": PivotStrategy.FIRST,
"last": PivotStrategy.LAST,
"median": PivotStrategy.MEDIAN_OF_THREE,
"random": PivotStrategy.RANDOM,
}
partition_map = {
"2-way": PartitionScheme.TWO_WAY,
"3-way": PartitionScheme.THREE_WAY,
}
algo = QuickSort(
pivot_strategy=pivot_map.get(pivot_strategy, PivotStrategy.FIRST),
partition_scheme=partition_map.get(partition_scheme, PartitionScheme.TWO_WAY)
)
else:
return (
self.visualizer.render_current(),
self._render_image_list(),
f"⚠️ Unknown algorithm: {algorithm_name}"
)
# Get data copy and run algorithm
data = list(self.image_list)
sorted_data, steps = algo.run_full(data)
# Load into visualizer
self.visualizer.load_steps(steps, sorted_data, algo.name)
# Update the image list to sorted order
self.image_list._save_state() # Save before modifying
self.image_list._images = list(sorted_data)
return (
self.visualizer.render_current(),
self._render_image_list(),
f"✅ {algo.name}: {len(steps)} steps"
)
def run_search(self, algorithm_name: str, target_index: int) -> Tuple[str, str]:
"""
Run a search algorithm.
Args:
algorithm_name: "Linear Search" or "Binary Search"
target_index: Index of the target element to search for
Returns:
Tuple of (visualization_html, status_message)
"""
if len(self.image_list) < 1:
return self.visualizer.render_current(), "⚠️ Need at least 1 image to search"
if not (0 <= target_index < len(self.image_list)):
return self.visualizer.render_current(), "⚠️ Invalid target index"
data = list(self.image_list)
target = data[target_index]
# For binary search, we need sorted data
if algorithm_name == "Binary Search":
if not self.image_list.is_sorted():
return (
self.visualizer.render_current(),
"⚠️ Binary Search requires sorted data! Run a sort first."
)
algo = BinarySearch(variant="iterative")
else:
algo = LinearSearch()
# Run the search
result_index, steps = algo.run_full(data, target)
# Load into visualizer
self.visualizer.load_steps(steps, data, algo.name)
if result_index is not None:
status = f"✅ {algo.name}: Found {target} at index {result_index}"
else:
status = f"❌ {algo.name}: {target} not found"
return self.visualizer.render_current(), status
# -------------------------------------------------------------------------
# Visualization Navigation Methods
# -------------------------------------------------------------------------
def viz_next(self) -> str:
"""Go to next visualization step."""
return self.visualizer.next_step()
def viz_prev(self) -> str:
"""Go to previous visualization step."""
return self.visualizer.prev_step()
def viz_start(self) -> str:
"""Go to first step."""
return self.visualizer.go_to_start()
def viz_end(self) -> str:
"""Go to last step."""
return self.visualizer.go_to_end()
def viz_goto(self, step: int) -> str:
"""Go to a specific step."""
return self.visualizer.go_to_step(int(step) - 1) # Convert to 0-based
# -------------------------------------------------------------------------
# Rendering Methods
# -------------------------------------------------------------------------
def _render_image_list(self) -> str:
"""Render the current image list as HTML."""
if len(self.image_list) == 0:
return """
<div style="
text-align: center;
padding: 40px;
color: #666;
background: #f8f9fa;
border-radius: 12px;
border: 2px dashed #ddd;
">
<div style="font-size: 48px; margin-bottom: 15px;">📷</div>
<h3 style="margin: 0 0 10px 0;">No Images Yet</h3>
<p style="margin: 0;">Add gestures using the buttons above!</p>
</div>
"""
# Build image cards
cards = []
for i, img in enumerate(self.image_list):
card = f"""
<div style="
display: inline-flex;
flex-direction: column;
align-items: center;
margin: 6px;
padding: 12px;
border-radius: 10px;
background: white;
border: 2px solid #ddd;
min-width: 70px;
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
">
<div style="font-size: 32px; margin-bottom: 4px;">{img.emoji}</div>
<div style="font-size: 11px; color: #666;">₍{img.capture_id}₎</div>
<div style="font-size: 10px; color: #999;">rank {img.rank}</div>
<div style="font-size: 9px; color: #aaa; margin-top: 4px;">[{i}]</div>
</div>
"""
cards.append(card)
# Analysis
analysis = self.image_list.get_analysis()
is_sorted = "✅ Sorted" if self.image_list.is_sorted() else "❌ Not Sorted"
return f"""
<div style="
background: linear-gradient(135deg, #002D62 0%, #9B2335 100%);
color: white;
padding: 15px;
border-radius: 12px 12px 0 0;
">
<div style="display: flex; justify-content: space-between; align-items: center;">
<strong>Image List ({len(self.image_list)} items)</strong>
<span>{is_sorted}</span>
</div>
</div>
<div style="
background: #f8f9fa;
padding: 15px;
border-radius: 0 0 12px 12px;
border: 1px solid #ddd;
border-top: none;
">
<div style="
display: flex;
flex-wrap: wrap;
justify-content: center;
gap: 4px;
">
{''.join(cards)}
</div>
<div style="
margin-top: 15px;
padding-top: 10px;
border-top: 1px solid #ddd;
font-size: 12px;
color: #666;
text-align: center;
">
{analysis}
</div>
</div>
"""
# -------------------------------------------------------------------------
# Create Gradio UI
# -------------------------------------------------------------------------
def create_ui(self) -> gr.Blocks:
"""
Create the Gradio interface.
📚 CONCEPT: Builder Pattern (light version)
We build up the UI component by component, each with its
own responsibility. The final result is a complete interface.
"""
with gr.Blocks() as demo:
# Header
gr.Markdown(APP_TITLE)
gr.Markdown(APP_DESCRIPTION)
with gr.Tabs():
# ============================================================
# TAB 1: Image Management
# ============================================================
with gr.TabItem("📷 Capture & Manage"):
with gr.Row():
# Left column: Add images
with gr.Column(scale=1):
gr.Markdown("### Add Gestures")
# Manual gesture selection
gesture_dropdown = gr.Dropdown(
choices=GestureRanking.get_all_gestures(),
label="Select Gesture",
info="Choose a gesture to add"
)
add_btn = gr.Button("➕ Add Gesture", variant="primary")
gr.Markdown("---")
# Image upload
image_input = gr.Image(
label="Upload Image",
type="pil",
sources=["upload", "webcam"]
)
classify_btn = gr.Button("🔍 Classify & Add")
gr.Markdown("---")
# Quick actions
with gr.Row():
sample_btn = gr.Button("📝 Add Samples")
shuffle_btn = gr.Button("🔀 Shuffle")
with gr.Row():
undo_btn = gr.Button("↩️ Undo")
clear_btn = gr.Button("🗑️ Clear", variant="stop")
gr.Markdown("---")
# Educational demos
gr.Markdown("### 🎓 Educational Demos")
instability_btn = gr.Button(
"⚠️ Instability Demo",
variant="secondary"
)
worst_case_btn = gr.Button(
"📉 Worst-Case Demo",
variant="secondary"
)
search_demo_btn = gr.Button(
"🔍 Search Demo",
variant="secondary"
)
# Right column: Image list display
with gr.Column(scale=2):
gr.Markdown("### Current Image List")
image_list_display = gr.HTML(
value=self._render_image_list()
)
status_msg = gr.Textbox(
label="Status",
interactive=False
)
# Wire up events for Tab 1
add_btn.click(
fn=self.add_manual_gesture,
inputs=[gesture_dropdown],
outputs=[image_list_display, status_msg]
)
classify_btn.click(
fn=self.add_from_image,
inputs=[image_input],
outputs=[image_list_display, status_msg]
)
sample_btn.click(
fn=self.add_sample_data,
outputs=[image_list_display, status_msg]
)
shuffle_btn.click(
fn=self.shuffle_images,
outputs=[image_list_display, status_msg]
)
undo_btn.click(
fn=self.undo_action,
outputs=[image_list_display, status_msg]
)
clear_btn.click(
fn=self.clear_images,
outputs=[image_list_display, status_msg]
)
instability_btn.click(
fn=self.add_instability_demo,
outputs=[image_list_display, status_msg]
)
worst_case_btn.click(
fn=self.add_worst_case_demo,
outputs=[image_list_display, status_msg]
)
search_demo_btn.click(
fn=self.add_binary_search_demo,
outputs=[image_list_display, status_msg]
)
# ============================================================
# TAB 2: Sorting Algorithms
# ============================================================
with gr.TabItem("📊 Sorting"):
with gr.Row():
# Left: Algorithm selection
with gr.Column(scale=1):
gr.Markdown("### Select Algorithm")
sort_algo = gr.Radio(
choices=["Bubble Sort", "Merge Sort", "Quick Sort"],
value="Bubble Sort",
label="Algorithm",
info="Each has different time complexity and stability"
)
# Educational info accordion
with gr.Accordion("📚 Algorithm Info", open=False):
gr.Markdown("""
**Bubble Sort** - O(n²) average, O(n) best
- ✅ Stable (preserves order of equal elements)
- Simple but slow for large lists
- Best when: Nearly sorted data
**Merge Sort** - O(n log n) always
- ✅ Stable
- Consistent performance
- Uses extra memory for merging
**Quick Sort** - O(n log n) average, O(n²) worst
- ❌ Unstable (may reorder equal elements)
- Fast in practice, in-place
- Best when: Random data, good pivot
""")
# Quick Sort options (only shown when Quick Sort selected)
with gr.Group() as quicksort_options:
gr.Markdown("**Quick Sort Options**")
pivot_strategy = gr.Radio(
choices=["first", "last", "median", "random"],
value="first",
label="Pivot Strategy",
info="Median/Random avoid worst-case O(n²)"
)
partition_scheme = gr.Radio(
choices=["2-way", "3-way"],
value="2-way",
label="Partition Scheme",
info="3-way handles duplicates better"
)
run_sort_btn = gr.Button("▶️ Run Sort", variant="primary", size="lg")
gr.Markdown("---")
gr.Markdown("### Current List")
sort_list_display = gr.HTML(value=self._render_image_list())
# Right: Visualization
with gr.Column(scale=2):
gr.Markdown("### Visualization")
sort_viz_display = gr.HTML(
value=self.visualizer.render_current()
)
# Navigation controls
with gr.Row():
viz_start_btn = gr.Button("⏮️ Start")
viz_prev_btn = gr.Button("◀️ Prev")
step_slider = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=1,
label="Step"
)
viz_next_btn = gr.Button("Next ▶️")
viz_end_btn = gr.Button("End ⏭️")
sort_status = gr.Textbox(label="Status", interactive=False)
# Wire up sorting events
run_sort_btn.click(
fn=self.run_sort,
inputs=[sort_algo, pivot_strategy, partition_scheme],
outputs=[sort_viz_display, sort_list_display, sort_status]
)
viz_next_btn.click(fn=self.viz_next, outputs=[sort_viz_display])
viz_prev_btn.click(fn=self.viz_prev, outputs=[sort_viz_display])
viz_start_btn.click(fn=self.viz_start, outputs=[sort_viz_display])
viz_end_btn.click(fn=self.viz_end, outputs=[sort_viz_display])
step_slider.change(fn=self.viz_goto, inputs=[step_slider], outputs=[sort_viz_display])
# ============================================================
# TAB 3: Searching Algorithms
# ============================================================
with gr.TabItem("🔍 Searching"):
with gr.Row():
# Left: Search controls
with gr.Column(scale=1):
gr.Markdown("### Search Settings")
search_algo = gr.Radio(
choices=["Linear Search", "Binary Search"],
value="Linear Search",
label="Algorithm",
info="Binary Search is O(log n) but requires sorted data"
)
# Educational info accordion
with gr.Accordion("📚 Algorithm Info", open=False):
gr.Markdown("""
**Linear Search** - O(n)
- Works on ANY list (sorted or unsorted)
- Checks each element one by one
- Simple but slow for large lists
**Binary Search** - O(log n)
- ⚠️ REQUIRES SORTED DATA!
- Halves the search space each step
- Much faster: 1000 elements → only 10 comparisons!
**Example (searching 1000 elements):**
- Linear: up to 1000 checks
- Binary: at most 10 checks (log₂1000 ≈ 10)
""")
target_index = gr.Number(
label="Target Index",
value=0,
precision=0,
info="Which element to search for (by index)"
)
run_search_btn = gr.Button("🔍 Run Search", variant="primary", size="lg")
gr.Markdown("---")
gr.Markdown("### Current List")
search_list_display = gr.HTML(value=self._render_image_list())
# Right: Visualization
with gr.Column(scale=2):
gr.Markdown("### Visualization")
search_viz_display = gr.HTML(
value=self.visualizer.render_current()
)
# Navigation controls
with gr.Row():
search_start_btn = gr.Button("⏮️ Start")
search_prev_btn = gr.Button("◀️ Prev")
search_next_btn = gr.Button("Next ▶️")
search_end_btn = gr.Button("End ⏭️")
search_status = gr.Textbox(label="Status", interactive=False)
# Wire up search events
run_search_btn.click(
fn=self.run_search,
inputs=[search_algo, target_index],
outputs=[search_viz_display, search_status]
)
search_next_btn.click(fn=self.viz_next, outputs=[search_viz_display])
search_prev_btn.click(fn=self.viz_prev, outputs=[search_viz_display])
search_start_btn.click(fn=self.viz_start, outputs=[search_viz_display])
search_end_btn.click(fn=self.viz_end, outputs=[search_viz_display])
# ============================================================
# TAB 4: Learn OOP
# ============================================================
with gr.TabItem("📚 Learn OOP"):
gr.Markdown("""
# Object-Oriented Programming Concepts
This application demonstrates several key OOP concepts:
## 📦 Classes & Objects
**Classes** are blueprints for creating objects. In this app:
- `GestureImage` - represents a single captured gesture
- `ImageList` - manages a collection of gestures
- `BubbleSort`, `MergeSort`, `QuickSort` - sorting algorithms
- `Visualizer` - handles step-by-step display
## 🎭 Inheritance
**Inheritance** lets classes share code. All sorting algorithms inherit from `SortingAlgorithm`:
```python
class SortingAlgorithm(ABC): # Abstract Base Class
@abstractmethod
def sort(self, data): ...
class BubbleSort(SortingAlgorithm): # Inherits from SortingAlgorithm
def sort(self, data):
# Bubble sort implementation
```
## 🔄 Polymorphism
**Polymorphism** means "same interface, different behavior":
```python
# All these work the same way!
algo = BubbleSort()
algo = MergeSort()
algo = QuickSort()
# Same method call, different algorithms
result, steps = algo.run_full(data)
```
## 🏭 Factory Pattern
**Factory Pattern** creates objects without exposing creation logic:
```python
# Factory creates the right renderer automatically
renderer = RendererFactory.create("Bubble Sort")
```
## 📊 Algorithm Comparison
| Algorithm | Time (Best) | Time (Worst) | Stable? | In-Place? |
|-----------|-------------|--------------|---------|-----------|
| Bubble Sort | O(n) | O(n²) | ✅ Yes | ✅ Yes |
| Merge Sort | O(n log n) | O(n log n) | ✅ Yes | ❌ No |
| Quick Sort | O(n log n) | O(n²) | ❌ No | ✅ Yes |
| Linear Search | O(1) | O(n) | - | - |
| Binary Search | O(1) | O(log n) | - | - |
## 🔍 Stability
A **stable** sort preserves the relative order of equal elements.
Example with two peace signs ✌️₁ and ✌️₂:
- **Stable**: Always produces [✌️₁, ✌️₂] (original order kept)
- **Unstable**: Might produce [✌️₂, ✌️₁] (order can change)
Try Quick Sort with duplicate gestures to see instability!
""")
# Footer
gr.Markdown("""
---
*Built for CISC 121 - Queen's University*
""")
return demo
# ==============================================================================
# MAIN ENTRY POINT
# ==============================================================================
def main():
"""Create and launch the Gradio app."""
app = GradioApp()
demo = app.create_ui()
demo.launch(share=False, ssr_mode=False, theme=gr.themes.Glass())
if __name__ == "__main__":
main()
# For HuggingFace Spaces compatibility (expects a 'demo' variable)
app = GradioApp()
demo = app.create_ui()
|