Software engineer by trade,
AI enthusiast by obsession.

CODE SAMPLES

Rust

@compute @workgroup_size(16, 16, 1)
fn non_maximum_suppression(
@builtin(global_invocation_id) global_id: vec3<u32>
) {
let x = global_id.x;
let y = global_id.y;
let idx = y * params.width + x;
let magnitude = gradient_magnitude[idx];
 
// Quantize direction to find neighbors
var neighbor1_mag: f32;
var neighbor2_mag: f32;
 
if direction < PI / 8.0 {
neighbor1_mag = gradient_magnitude[idx - 1];
neighbor2_mag = gradient_magnitude[idx + 1];
}

Rust

pub fn calculate_ssim(
img1: &GrayImage,
img2: &GrayImage
) -> Result<SsimResult> {
let mut ssim_sum = 0.0;
let half_window = WINDOW_SIZE / 2;
 
for y in half_window..(height - half_window) {
for x in half_window..(width - half_window) {
let w1 = extract_window(img1, x, y)?;
let w2 = extract_window(img2, x, y)?;
 
let luminance = (2.0 * s1.mean * s2.mean + C1)
/ (s1.mean.powi(2) + s2.mean.powi(2) + C1);
}
}

Python

def correct_text(self, text: str) -> Tuple[str, List]:
words = text.split()
corrections = []
 
for word in words:
if any(word.lower() in word_set
for word_set in self.vocabulary.values()):
corrected_words.append(word)
continue
 
corrected_word, category, dist = \
self._find_closest_match(word)
if dist != -1:
corrections.append((word, corrected_word))
 
return ' '.join(corrected_words), corrections

TypeScript

export function debounce<T extends (...args: any[]) => any>(
func: T,
wait: number,
immediate?: boolean
): (...args: Parameters<T>) => void {
let timeout: NodeJS.Timeout | null = null;
 
return function executedFunction(...args: Parameters<T>) {
const later = () => {
timeout = null;
if (!immediate) func(...args);
};
 
const callNow = immediate && !timeout;
if (timeout) clearTimeout(timeout);
timeout = setTimeout(later, wait);
if (callNow) func(...args);
};
}

React

const handleMicClick = () => {
if (!isRecording) {
const SpeechRecognition =
window.SpeechRecognition ||
window.webkitSpeechRecognition;
const recognition = new SpeechRecognition();
 
recognition.lang = 'en-US';
recognition.continuous = false;
 
recognition.onresult = (event) => {
const transcript = event.results[0][0].transcript;
setIsRecording(false);
processUserInput(transcript);
};
 
recognition.start();
}
};

Rust

fn render_svg_to_image(
svg_content: &str,
dimensions: (u32, u32)
) -> Result<RgbaImage> {
let (width, height) = dimensions;
 
// Parse SVG with options
let options = usvg::Options::default();
let tree = usvg::Tree::from_str(svg_content, &options)?;
 
// Create pixmap for rendering
let mut pixmap = tiny_skia::Pixmap::new(width, height)?;
 
// Render and convert BGRA to RGBA
resvg::render(&tree, Transform::identity(), &mut pixmap);
Ok(convert_pixmap_to_image(pixmap))
}

Python

def connect(self):
for attempt in range(3):
try:
print(f"MongoDB attempt {attempt + 1}")
proxy_url = os.getenv('QUOTAGUARD_URL')
 
if proxy_url:
parsed = urlparse(proxy_url)
socks.setdefaultproxy(
socks.PROXY_TYPE_SOCKS5,
parsed.hostname,
parsed.port
)
 
self.client = MongoClient(self.uri, tls=True)
self.client.admin.command('ping')
break
except Exception as e:
time.sleep(5)

TypeScript

export function memoize<T extends (...args: any[]) => any>(
func: T,
keyGenerator?: (...args: Parameters<T>) => string
): T & { cache: Map<string, ReturnType<T>> } {
const cache = new Map<string, ReturnType<T>>();
 
const memoizedFunc = (...args: Parameters<T>): ReturnType<T> => {
const key = keyGenerator
? keyGenerator(...args)
: JSON.stringify(args);
 
if (cache.has(key)) {
return cache.get(key)!;
}
 
const result = func(...args);
cache.set(key, result);
return result;
};
 
return Object.assign(memoizedFunc, { cache });
}

Svelte

import { onMount } from 'svelte';
import { gpuService } from '$lib/gpu-service';
 
let capabilities = $state(null);
let loading = $state(true);
let error = $state(null);
 
onMount(async () => {
try {
await gpuService.initialize();
capabilities = await gpuService.getCapabilities();
loading = false;
} catch (e) {
error = e.message;
loading = false;
}
});

Svelte

import { onMount, tick } from 'svelte';
 
interface Props {
target?: Element | string;
children?: import('svelte').Snippet;
}
 
let { target = 'body', children }: Props = $props();
let portal: Element;
let mounted = $state(false);
 
onMount(async () => {
await tick();
portal = typeof target === 'string'
? document.querySelector(target) ?? document.body
: target;
mounted = true;
});

Rust

fn extract_window(
img: &GrayImage,
cx: usize, cy: usize,
size: usize
) -> Result<Vec<f64>> {
let half = size / 2;
let mut window = Vec::with_capacity(size * size);
 
for dy in 0..size {
for dx in 0..size {
let x = (cx + dx).saturating_sub(half);
let y = (cy + dy).saturating_sub(half);
 
if x < img.width() as usize {
let pixel = img.get_pixel(x as u32, y as u32);
window.push(pixel[0] as f64);
}
}
}
Ok(window)
}

Rust

pub fn validate_dimensions(
width: u32,
height: u32,
max_size: u32
) -> Result<(), ValidationError> {
if width == 0 || height == 0 {
return Err(ValidationError::InvalidDimensions(
"Dimensions must be non-zero".into()
));
}
 
if width > max_size || height > max_size {
return Err(ValidationError::TooLarge {
width, height, max_size
});
}
 
Ok(())
}

Python

# Load SpaCy model and Sentence-BERT model
nlp = spacy.load(config.SPACY_MODEL)
sentence_model = SentenceTransformer(config.SENTENCE_MODEL)
 
# Prepare patterns and tags from the intents file
all_patterns = []
pattern_tags = []
for intent in intents['intents']:
tag = intent['tag']
for pattern in intent['patterns']:
cleaned_pattern = clean_sentence(pattern)
all_patterns.append(cleaned_pattern)
pattern_tags.append(tag)
 
# Encode all patterns once using Sentence-BERT
pattern_embeddings = sentence_model.encode(all_patterns)

Python

# Compute cosine similarity between input and patterns
cosine_similarities = np.dot(pattern_embeddings, input_embedding) / (
np.linalg.norm(pattern_embeddings, axis=1) *
np.linalg.norm(input_embedding)
)
 
# Compute Jaccard similarity on token sets
input_tokens = set(cleaned_input.split())
jaccard_similarities = np.array([
len(input_tokens.intersection(set(pattern.split()))) /
len(input_tokens.union(set(pattern.split())))
if len(input_tokens.union(set(pattern.split()))) > 0 else 0
for pattern in all_patterns
])
 
# Dynamically choose metric weights based on input length
input_length = len(cleaned_input.split())
weights = (config.SHORT_INPUT_WEIGHTS if input_length <= 3
else config.LONG_INPUT_WEIGHTS)

Python

def _find_closest_match(self, word, max_distance=2):
if len(word) < 3:
return word, '', -1
 
min_distance = max_distance + 1
best_match = word
best_category = ''
 
# Check each vocabulary entry for minimal distance
for category, valid_words in self.vocabulary.items():
for valid_word in valid_words:
if not self._should_attempt_correction(word, valid_word):
continue
 
current_distance = distance(word.lower(), valid_word.lower())
if current_distance < min_distance and current_distance <= max_distance:
min_distance = current_distance
best_match = valid_word
best_category = category
 
if min_distance <= max_distance:
return best_match, best_category, min_distance
return word, '', -1

Python

def process_order_spacy(session_id, input_sentence):
segments = segment_input(input_sentence)
item_addons = defaultdict(
lambda: {"meats": [], "rice": [], "beans": [], "toppings": []}
)
 
for seg in segments:
doc = nlp(seg)
main_items_in_seg = extract_menu_items(seg)
 
if main_items_in_seg:
main_item = main_items_in_seg[0]
# Convert colloquial name to official name
official_name = name_to_colloquial.get(main_item)
if official_name:
main_item = official_name
 
# Match add-ons using PhraseMatcher
matches = addon_matcher(doc)
for match_id, start, end in matches:
span = doc[start:end]
category = nlp.vocab.strings[match_id]
addon = span.text.lower()

Python

def fetch_menu_data():
menu_item_collection = db.get_db()['MenuItem']
all_items = list(menu_item_collection.find({}))
menu = {}
name_to_colloquial = {}
 
for item in all_items:
name = item['name'].lower()
price = (item['size_details'][0].get('price', 0)
if 'size_details' in item and item['size_details'] else 0)
category = item.get('category', 'other')
colloquial = (item.get('colloquialism', '').lower()
if item.get('colloquialism') else None)
 
# Normalize category to lowercase or default
if isinstance(category, list):
category = [c.lower() for c in category]
elif isinstance(category, str):
category = category.lower()

Rust

@compute @workgroup_size(16, 16, 1)
fn non_maximum_suppression(
@builtin(global_invocation_id) global_id: vec3<u32>
) {
let x = global_id.x;
let y = global_id.y;
let idx = y * params.width + x;
let magnitude = gradient_magnitude[idx];
 
// Quantize direction to find neighbors
var neighbor1_mag: f32;
var neighbor2_mag: f32;
 
if direction < PI / 8.0 {
neighbor1_mag = gradient_magnitude[idx - 1];
neighbor2_mag = gradient_magnitude[idx + 1];
}

Rust

pub fn calculate_ssim(
img1: &GrayImage,
img2: &GrayImage
) -> Result<SsimResult> {
let mut ssim_sum = 0.0;
let half_window = WINDOW_SIZE / 2;
 
for y in half_window..(height - half_window) {
for x in half_window..(width - half_window) {
let w1 = extract_window(img1, x, y)?;
let w2 = extract_window(img2, x, y)?;
 
let luminance = (2.0 * s1.mean * s2.mean + C1)
/ (s1.mean.powi(2) + s2.mean.powi(2) + C1);
}
}

Python

def correct_text(self, text: str) -> Tuple[str, List]:
words = text.split()
corrections = []
 
for word in words:
if any(word.lower() in word_set
for word_set in self.vocabulary.values()):
corrected_words.append(word)
continue
 
corrected_word, category, dist = \
self._find_closest_match(word)
if dist != -1:
corrections.append((word, corrected_word))
 
return ' '.join(corrected_words), corrections

TypeScript

export function debounce<T extends (...args: any[]) => any>(
func: T,
wait: number,
immediate?: boolean
): (...args: Parameters<T>) => void {
let timeout: NodeJS.Timeout | null = null;
 
return function executedFunction(...args: Parameters<T>) {
const later = () => {
timeout = null;
if (!immediate) func(...args);
};
 
const callNow = immediate && !timeout;
if (timeout) clearTimeout(timeout);
timeout = setTimeout(later, wait);
if (callNow) func(...args);
};
}

React

const handleMicClick = () => {
if (!isRecording) {
const SpeechRecognition =
window.SpeechRecognition ||
window.webkitSpeechRecognition;
const recognition = new SpeechRecognition();
 
recognition.lang = 'en-US';
recognition.continuous = false;
 
recognition.onresult = (event) => {
const transcript = event.results[0][0].transcript;
setIsRecording(false);
processUserInput(transcript);
};
 
recognition.start();
}
};

Rust

fn render_svg_to_image(
svg_content: &str,
dimensions: (u32, u32)
) -> Result<RgbaImage> {
let (width, height) = dimensions;
 
// Parse SVG with options
let options = usvg::Options::default();
let tree = usvg::Tree::from_str(svg_content, &options)?;
 
// Create pixmap for rendering
let mut pixmap = tiny_skia::Pixmap::new(width, height)?;
 
// Render and convert BGRA to RGBA
resvg::render(&tree, Transform::identity(), &mut pixmap);
Ok(convert_pixmap_to_image(pixmap))
}

Python

def connect(self):
for attempt in range(3):
try:
print(f"MongoDB attempt {attempt + 1}")
proxy_url = os.getenv('QUOTAGUARD_URL')
 
if proxy_url:
parsed = urlparse(proxy_url)
socks.setdefaultproxy(
socks.PROXY_TYPE_SOCKS5,
parsed.hostname,
parsed.port
)
 
self.client = MongoClient(self.uri, tls=True)
self.client.admin.command('ping')
break
except Exception as e:
time.sleep(5)

TypeScript

export function memoize<T extends (...args: any[]) => any>(
func: T,
keyGenerator?: (...args: Parameters<T>) => string
): T & { cache: Map<string, ReturnType<T>> } {
const cache = new Map<string, ReturnType<T>>();
 
const memoizedFunc = (...args: Parameters<T>): ReturnType<T> => {
const key = keyGenerator
? keyGenerator(...args)
: JSON.stringify(args);
 
if (cache.has(key)) {
return cache.get(key)!;
}
 
const result = func(...args);
cache.set(key, result);
return result;
};
 
return Object.assign(memoizedFunc, { cache });
}

Svelte

import { onMount } from 'svelte';
import { gpuService } from '$lib/gpu-service';
 
let capabilities = $state(null);
let loading = $state(true);
let error = $state(null);
 
onMount(async () => {
try {
await gpuService.initialize();
capabilities = await gpuService.getCapabilities();
loading = false;
} catch (e) {
error = e.message;
loading = false;
}
});

Svelte

import { onMount, tick } from 'svelte';
 
interface Props {
target?: Element | string;
children?: import('svelte').Snippet;
}
 
let { target = 'body', children }: Props = $props();
let portal: Element;
let mounted = $state(false);
 
onMount(async () => {
await tick();
portal = typeof target === 'string'
? document.querySelector(target) ?? document.body
: target;
mounted = true;
});

Rust

fn extract_window(
img: &GrayImage,
cx: usize, cy: usize,
size: usize
) -> Result<Vec<f64>> {
let half = size / 2;
let mut window = Vec::with_capacity(size * size);
 
for dy in 0..size {
for dx in 0..size {
let x = (cx + dx).saturating_sub(half);
let y = (cy + dy).saturating_sub(half);
 
if x < img.width() as usize {
let pixel = img.get_pixel(x as u32, y as u32);
window.push(pixel[0] as f64);
}
}
}
Ok(window)
}

Rust

pub fn validate_dimensions(
width: u32,
height: u32,
max_size: u32
) -> Result<(), ValidationError> {
if width == 0 || height == 0 {
return Err(ValidationError::InvalidDimensions(
"Dimensions must be non-zero".into()
));
}
 
if width > max_size || height > max_size {
return Err(ValidationError::TooLarge {
width, height, max_size
});
}
 
Ok(())
}

Python

# Load SpaCy model and Sentence-BERT model
nlp = spacy.load(config.SPACY_MODEL)
sentence_model = SentenceTransformer(config.SENTENCE_MODEL)
 
# Prepare patterns and tags from the intents file
all_patterns = []
pattern_tags = []
for intent in intents['intents']:
tag = intent['tag']
for pattern in intent['patterns']:
cleaned_pattern = clean_sentence(pattern)
all_patterns.append(cleaned_pattern)
pattern_tags.append(tag)
 
# Encode all patterns once using Sentence-BERT
pattern_embeddings = sentence_model.encode(all_patterns)

Python

# Compute cosine similarity between input and patterns
cosine_similarities = np.dot(pattern_embeddings, input_embedding) / (
np.linalg.norm(pattern_embeddings, axis=1) *
np.linalg.norm(input_embedding)
)
 
# Compute Jaccard similarity on token sets
input_tokens = set(cleaned_input.split())
jaccard_similarities = np.array([
len(input_tokens.intersection(set(pattern.split()))) /
len(input_tokens.union(set(pattern.split())))
if len(input_tokens.union(set(pattern.split()))) > 0 else 0
for pattern in all_patterns
])
 
# Dynamically choose metric weights based on input length
input_length = len(cleaned_input.split())
weights = (config.SHORT_INPUT_WEIGHTS if input_length <= 3
else config.LONG_INPUT_WEIGHTS)

Python

def _find_closest_match(self, word, max_distance=2):
if len(word) < 3:
return word, '', -1
 
min_distance = max_distance + 1
best_match = word
best_category = ''
 
# Check each vocabulary entry for minimal distance
for category, valid_words in self.vocabulary.items():
for valid_word in valid_words:
if not self._should_attempt_correction(word, valid_word):
continue
 
current_distance = distance(word.lower(), valid_word.lower())
if current_distance < min_distance and current_distance <= max_distance:
min_distance = current_distance
best_match = valid_word
best_category = category
 
if min_distance <= max_distance:
return best_match, best_category, min_distance
return word, '', -1

Python

def process_order_spacy(session_id, input_sentence):
segments = segment_input(input_sentence)
item_addons = defaultdict(
lambda: {"meats": [], "rice": [], "beans": [], "toppings": []}
)
 
for seg in segments:
doc = nlp(seg)
main_items_in_seg = extract_menu_items(seg)
 
if main_items_in_seg:
main_item = main_items_in_seg[0]
# Convert colloquial name to official name
official_name = name_to_colloquial.get(main_item)
if official_name:
main_item = official_name
 
# Match add-ons using PhraseMatcher
matches = addon_matcher(doc)
for match_id, start, end in matches:
span = doc[start:end]
category = nlp.vocab.strings[match_id]
addon = span.text.lower()

Python

def fetch_menu_data():
menu_item_collection = db.get_db()['MenuItem']
all_items = list(menu_item_collection.find({}))
menu = {}
name_to_colloquial = {}
 
for item in all_items:
name = item['name'].lower()
price = (item['size_details'][0].get('price', 0)
if 'size_details' in item and item['size_details'] else 0)
category = item.get('category', 'other')
colloquial = (item.get('colloquialism', '').lower()
if item.get('colloquialism') else None)
 
# Normalize category to lowercase or default
if isinstance(category, list):
category = [c.lower() for c in category]
elif isinstance(category, str):
category = category.lower()

Rust

@compute @workgroup_size(16, 16, 1)
fn non_maximum_suppression(
@builtin(global_invocation_id) global_id: vec3<u32>
) {
let x = global_id.x;
let y = global_id.y;
let idx = y * params.width + x;
let magnitude = gradient_magnitude[idx];
 
// Quantize direction to find neighbors
var neighbor1_mag: f32;
var neighbor2_mag: f32;
 
if direction < PI / 8.0 {
neighbor1_mag = gradient_magnitude[idx - 1];
neighbor2_mag = gradient_magnitude[idx + 1];
}

Rust

pub fn calculate_ssim(
img1: &GrayImage,
img2: &GrayImage
) -> Result<SsimResult> {
let mut ssim_sum = 0.0;
let half_window = WINDOW_SIZE / 2;
 
for y in half_window..(height - half_window) {
for x in half_window..(width - half_window) {
let w1 = extract_window(img1, x, y)?;
let w2 = extract_window(img2, x, y)?;
 
let luminance = (2.0 * s1.mean * s2.mean + C1)
/ (s1.mean.powi(2) + s2.mean.powi(2) + C1);
}
}

Python

def correct_text(self, text: str) -> Tuple[str, List]:
words = text.split()
corrections = []
 
for word in words:
if any(word.lower() in word_set
for word_set in self.vocabulary.values()):
corrected_words.append(word)
continue
 
corrected_word, category, dist = \
self._find_closest_match(word)
if dist != -1:
corrections.append((word, corrected_word))
 
return ' '.join(corrected_words), corrections

TypeScript

export function debounce<T extends (...args: any[]) => any>(
func: T,
wait: number,
immediate?: boolean
): (...args: Parameters<T>) => void {
let timeout: NodeJS.Timeout | null = null;
 
return function executedFunction(...args: Parameters<T>) {
const later = () => {
timeout = null;
if (!immediate) func(...args);
};
 
const callNow = immediate && !timeout;
if (timeout) clearTimeout(timeout);
timeout = setTimeout(later, wait);
if (callNow) func(...args);
};
}

React

const handleMicClick = () => {
if (!isRecording) {
const SpeechRecognition =
window.SpeechRecognition ||
window.webkitSpeechRecognition;
const recognition = new SpeechRecognition();
 
recognition.lang = 'en-US';
recognition.continuous = false;
 
recognition.onresult = (event) => {
const transcript = event.results[0][0].transcript;
setIsRecording(false);
processUserInput(transcript);
};
 
recognition.start();
}
};

Rust

fn render_svg_to_image(
svg_content: &str,
dimensions: (u32, u32)
) -> Result<RgbaImage> {
let (width, height) = dimensions;
 
// Parse SVG with options
let options = usvg::Options::default();
let tree = usvg::Tree::from_str(svg_content, &options)?;
 
// Create pixmap for rendering
let mut pixmap = tiny_skia::Pixmap::new(width, height)?;
 
// Render and convert BGRA to RGBA
resvg::render(&tree, Transform::identity(), &mut pixmap);
Ok(convert_pixmap_to_image(pixmap))
}

Python

def connect(self):
for attempt in range(3):
try:
print(f"MongoDB attempt {attempt + 1}")
proxy_url = os.getenv('QUOTAGUARD_URL')
 
if proxy_url:
parsed = urlparse(proxy_url)
socks.setdefaultproxy(
socks.PROXY_TYPE_SOCKS5,
parsed.hostname,
parsed.port
)
 
self.client = MongoClient(self.uri, tls=True)
self.client.admin.command('ping')
break
except Exception as e:
time.sleep(5)

TypeScript

export function memoize<T extends (...args: any[]) => any>(
func: T,
keyGenerator?: (...args: Parameters<T>) => string
): T & { cache: Map<string, ReturnType<T>> } {
const cache = new Map<string, ReturnType<T>>();
 
const memoizedFunc = (...args: Parameters<T>): ReturnType<T> => {
const key = keyGenerator
? keyGenerator(...args)
: JSON.stringify(args);
 
if (cache.has(key)) {
return cache.get(key)!;
}
 
const result = func(...args);
cache.set(key, result);
return result;
};
 
return Object.assign(memoizedFunc, { cache });
}

Svelte

import { onMount } from 'svelte';
import { gpuService } from '$lib/gpu-service';
 
let capabilities = $state(null);
let loading = $state(true);
let error = $state(null);
 
onMount(async () => {
try {
await gpuService.initialize();
capabilities = await gpuService.getCapabilities();
loading = false;
} catch (e) {
error = e.message;
loading = false;
}
});

Svelte

import { onMount, tick } from 'svelte';
 
interface Props {
target?: Element | string;
children?: import('svelte').Snippet;
}
 
let { target = 'body', children }: Props = $props();
let portal: Element;
let mounted = $state(false);
 
onMount(async () => {
await tick();
portal = typeof target === 'string'
? document.querySelector(target) ?? document.body
: target;
mounted = true;
});

Rust

fn extract_window(
img: &GrayImage,
cx: usize, cy: usize,
size: usize
) -> Result<Vec<f64>> {
let half = size / 2;
let mut window = Vec::with_capacity(size * size);
 
for dy in 0..size {
for dx in 0..size {
let x = (cx + dx).saturating_sub(half);
let y = (cy + dy).saturating_sub(half);
 
if x < img.width() as usize {
let pixel = img.get_pixel(x as u32, y as u32);
window.push(pixel[0] as f64);
}
}
}
Ok(window)
}

Rust

pub fn validate_dimensions(
width: u32,
height: u32,
max_size: u32
) -> Result<(), ValidationError> {
if width == 0 || height == 0 {
return Err(ValidationError::InvalidDimensions(
"Dimensions must be non-zero".into()
));
}
 
if width > max_size || height > max_size {
return Err(ValidationError::TooLarge {
width, height, max_size
});
}
 
Ok(())
}

Python

# Load SpaCy model and Sentence-BERT model
nlp = spacy.load(config.SPACY_MODEL)
sentence_model = SentenceTransformer(config.SENTENCE_MODEL)
 
# Prepare patterns and tags from the intents file
all_patterns = []
pattern_tags = []
for intent in intents['intents']:
tag = intent['tag']
for pattern in intent['patterns']:
cleaned_pattern = clean_sentence(pattern)
all_patterns.append(cleaned_pattern)
pattern_tags.append(tag)
 
# Encode all patterns once using Sentence-BERT
pattern_embeddings = sentence_model.encode(all_patterns)

Python

# Compute cosine similarity between input and patterns
cosine_similarities = np.dot(pattern_embeddings, input_embedding) / (
np.linalg.norm(pattern_embeddings, axis=1) *
np.linalg.norm(input_embedding)
)
 
# Compute Jaccard similarity on token sets
input_tokens = set(cleaned_input.split())
jaccard_similarities = np.array([
len(input_tokens.intersection(set(pattern.split()))) /
len(input_tokens.union(set(pattern.split())))
if len(input_tokens.union(set(pattern.split()))) > 0 else 0
for pattern in all_patterns
])
 
# Dynamically choose metric weights based on input length
input_length = len(cleaned_input.split())
weights = (config.SHORT_INPUT_WEIGHTS if input_length <= 3
else config.LONG_INPUT_WEIGHTS)

Python

def _find_closest_match(self, word, max_distance=2):
if len(word) < 3:
return word, '', -1
 
min_distance = max_distance + 1
best_match = word
best_category = ''
 
# Check each vocabulary entry for minimal distance
for category, valid_words in self.vocabulary.items():
for valid_word in valid_words:
if not self._should_attempt_correction(word, valid_word):
continue
 
current_distance = distance(word.lower(), valid_word.lower())
if current_distance < min_distance and current_distance <= max_distance:
min_distance = current_distance
best_match = valid_word
best_category = category
 
if min_distance <= max_distance:
return best_match, best_category, min_distance
return word, '', -1

Python

def process_order_spacy(session_id, input_sentence):
segments = segment_input(input_sentence)
item_addons = defaultdict(
lambda: {"meats": [], "rice": [], "beans": [], "toppings": []}
)
 
for seg in segments:
doc = nlp(seg)
main_items_in_seg = extract_menu_items(seg)
 
if main_items_in_seg:
main_item = main_items_in_seg[0]
# Convert colloquial name to official name
official_name = name_to_colloquial.get(main_item)
if official_name:
main_item = official_name
 
# Match add-ons using PhraseMatcher
matches = addon_matcher(doc)
for match_id, start, end in matches:
span = doc[start:end]
category = nlp.vocab.strings[match_id]
addon = span.text.lower()

Python

def fetch_menu_data():
menu_item_collection = db.get_db()['MenuItem']
all_items = list(menu_item_collection.find({}))
menu = {}
name_to_colloquial = {}
 
for item in all_items:
name = item['name'].lower()
price = (item['size_details'][0].get('price', 0)
if 'size_details' in item and item['size_details'] else 0)
category = item.get('category', 'other')
colloquial = (item.get('colloquialism', '').lower()
if item.get('colloquialism') else None)
 
# Normalize category to lowercase or default
if isinstance(category, list):
category = [c.lower() for c in category]
elif isinstance(category, str):
category = category.lower()