Spaces:

abhisheky127
/

Fold_TransactionClassification

Sleeping

App Files Files Community

Fold_TransactionClassification / app.py

abhisheky127

Update app.py

0d4640f 8 months ago

raw history blame contribute delete

No virus

3.62 kB

	import gradio as gr
	import re
	from transformers import pipeline
	# from googlesearch import search
	import requests
	from bs4 import BeautifulSoup

	def get_google_description(keyword):
	query = keyword
	results = search(query, num_results=1, lang='en')

	for result in results:
	description = get_description_from_url(result)
	if description:
	return description

	return keyword

	def get_description_from_url(url):
	response = requests.get(url, timeout=10)
	soup = BeautifulSoup(response.text, 'html.parser')
	description_tag = soup.find('meta', {'name': 'description'})

	if description_tag:
	return description_tag.get('content')

	return None


	title = "Fold: Contextual Tag Recommendation System"
	description = "powered by bart-large-mnli, made by @abhisheky127"

	classifier = pipeline("zero-shot-classification",
	model="facebook/bart-large-mnli")





	#define a function to process your input and output
	def zero_shot(doc, candidates):
	given_labels = candidates.split(",")
	given_labels = list(map(str.strip, given_labels))
	doc = preprocess(doc)
	print(doc)
	dictionary = classifier(doc, given_labels)
	labels = dictionary['labels']
	scores = dictionary['scores']
	return dict(zip(labels, scores))

	def preprocess(text):
	# Remove digits
	cleaned_text = re.sub(r'\d', '', text)

	# Remove special characters except spaces and letters
	cleaned_text = re.sub(r'[^a-zA-Z\s]', ' ', cleaned_text)

	# Remove extra spaces
	cleaned_text = re.sub(r'\s+', ' ', cleaned_text).strip()

	# Convert to uppercase
	cleaned_text = cleaned_text.upper()

	# Remove unwanted words
	words_to_remove = ["MPS", "POS", "BIL", "ONL", "BANGALORE", "PVT", "LTD", "INDIA", "LT", "XXXXXXXXXXXX"]
	cleaned_text = " ".join([word for word in cleaned_text.split() if word not in words_to_remove])

	# Convert to lowercase
	cleaned_text = cleaned_text.lower()

	# cleaned_text = get_google_description(cleaned_text)

	return cleaned_text


	#create input and output objects
	#input object1
	input1 = gr.Textbox(label="Text")

	#input object 2
	input2 = gr.Textbox(label="Labels")

	#output object
	output = gr.Label(label="Output")

	#example object
	transactions_and_tags = [
	["MPS/TRUFFLES/202303261700/034587/Bangalore", "Medical, Food, Shopping, Subscription, Travel"],
	["MPS/TACO BELL/202304012247/108300/BANGALORE", "Medical, Food, Shopping, Subscription, Travel"],
	["POS XXXXXXXXXXXX0001 APOLLO PHARMACY", "Medical, Food, Shopping, Subscription, Travel"],
	["BIL/ONL/000471093694/1MG Techno/X7ZRUSVLURFQZO", "Medical, Food, Shopping, Subscription, Travel"],
	["POS XXXXXXXXXXXX1111 DECATHLON SPORTS", "Medical, Food, Shopping, Subscription, Travel"],
	["POS XXXXXXXXXXXX1111 WWW AMAZON IN", "Medical, Food, Shopping, Subscription, Travel"],
	["ME DC SI XXXXXXXXXXXX1111 SPOTIFY SI", "Medical, Food, Shopping, Subscription, Travel"],
	["POS/NETFLIX/1140920002/100623/17:25", "Medical, Food, Shopping, Subscription, Travel"],
	["POS XXXXXXXXXXXX1110 MAKEMYTRIP INDIA", "Medical, Food, Shopping, Subscription, Travel"],
	["BIL/ONL/000691178015/IRCTC Serv/XZZBX91LTCY1AZ", "Medical, Food, Shopping, Subscription, Travel"]
	]

	#create interface
	gui = gr.Interface(title=title,
	description=description,
	fn=zero_shot,
	inputs=[input1, input2],
	outputs=[output],
	examples=transactions_and_tags
	)

	#display the interface
	gui.launch(debug=True)