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import gradio as gr
import torch
from flair.data import Sentence
from flair.models import SequenceTagger
import requests
import re
from bs4 import BeautifulSoup
# Load FashionNLP (Flair-based NER model)
tagger = SequenceTagger.load("flair/ner-english-large")
# Regex for extracting price
price_pattern = re.compile(r'(\bunder\b|\babove\b|\bbelow\b|\bbetween\b)?\s?(\d{1,5})\s?(AED|USD|EUR)?', re.IGNORECASE)
# Keywords for gender extraction
gender_keywords = ["men", "male", "women", "female", "unisex"]
def extract_fashion_entities(text):
"""
Extracts fashion-related entities (Brand, Category, Material, Price, Gender) from text.
"""
sentence = Sentence(text)
tagger.predict(sentence)
extracted_entities = {"Brand": "Unknown", "Category": "Unknown", "Material": "Unknown", "Price": "Unknown", "Gender": "Unknown"}
for entity in sentence.get_spans('ner'):
entity_text = entity.text
entity_label = entity.tag
if entity_label in ["ORG", "BRAND", "HOUSE"]:
extracted_entities["Brand"] = entity_text
elif entity_label in ["PRODUCT", "CATEGORY"]:
extracted_entities["Category"] = entity_text
elif entity_label in ["MATERIAL"]:
extracted_entities["Material"] = entity_text
elif entity_label in ["PRICE"]:
extracted_entities["Price"] = entity_text
elif entity_label in ["GENDER"]:
extracted_entities["Gender"] = entity_text
# Extract gender
for gender in gender_keywords:
if gender in text.lower():
extracted_entities["Gender"] = gender.capitalize()
break
# Extract price if not found by NER
price_match = price_pattern.search(text)
if price_match and extracted_entities["Price"] == "Unknown":
condition, amount, currency = price_match.groups()
extracted_entities["Price"] = f"{condition.capitalize() if condition else ''} {amount} {currency if currency else 'AED'}".strip()
return extracted_entities
def scrape_fashion_trends(url):
"""
Scrapes fashion trend articles from a given URL and extracts key entities.
"""
headers = {'User-Agent': 'Mozilla/5.0'}
response = requests.get(url, headers=headers)
if response.status_code != 200:
return {"Error": "Unable to fetch data"}
soup = BeautifulSoup(response.text, 'html.parser')
# Extract article text
paragraphs = soup.find_all("p")
text = " ".join([p.text for p in paragraphs])
# Run entity extraction
extracted_trends = extract_fashion_entities(text)
return extracted_trends
# Define Gradio UI
def parse_fashion_query(user_query):
"""
Parses a fashion search query and extracts structured attributes.
"""
return extract_fashion_entities(user_query)
with gr.Blocks() as demo:
gr.Markdown("# 🛍️ Luxury Fashion Query Parser using FashionNLP")
with gr.Tab("Search Query Parser"):
query_input = gr.Textbox(label="Enter your search query", placeholder="e.g., Gucci men’s perfume under 200AED")
output_box = gr.JSON(label="Parsed Output")
parse_button = gr.Button("Parse Query")
parse_button.click(parse_fashion_query, inputs=[query_input], outputs=[output_box])
with gr.Tab("Fashion Trends Analyzer"):
url_input = gr.Textbox(label="Enter Fashion News URL", placeholder="e.g., https://www.vogue.com/fashion")
trends_output = gr.JSON(label="Extracted Trends")
scrape_button = gr.Button("Analyze Trends")
scrape_button.click(scrape_fashion_trends, inputs=[url_input], outputs=[trends_output])
demo.launch() |