import gradio as gr
import numpy as np
import re
from rapidfuzz import fuzz

class Predictor:
	def __init__(self):
		self.id_list = []
		self.text_list = []

	def _split_text(self, text: str) -> list[str]:
		words = re.split('[ \t　]', text)
		normalized_words = [word.strip().lower() for word in words if word.strip()]
		return list(set(normalized_words))

	def _normalize_text(self, text: str) -> str:
		return ' '.join(self._split_text(text))

	def update_text_list(self, text: str):
		for line in text.replace('\r', '').split('\n'):
			fields = line.split(',')
			if len(fields) < 2:
				return
			id = fields[0].strip()
			text = ' '.join(fields[1:])
			try:
				index = self.id_list.index(id)
				self.text_list[index] = self._normalize_text(f'{self.text_list[index]} {text}')
			except ValueError:
				self.id_list.append(id)
				self.text_list.append(self._normalize_text(text))

	def _calc_score(self, text: str, keyword: str) -> float:
		keywords = self._split_text(keyword)
		wordlist = self._split_text(text)
		return sum(map(lambda k: max(map(lambda w: fuzz.ratio(w, k), wordlist)), keywords))

	def predict(self, keyword: str) -> str:
		if len(self.text_list) <= 0:
			print('no data')
			return ''
		s = np.empty(0)
		for text in self.text_list:
			s = np.append(s, self._calc_score(text, keyword))
		index = np.argmax(s)
		result_id = self.id_list[index]
		result_desc = self.text_list[index]
		print(f'{result_id} {result_desc}')
		return result_id

def process_text(input_text: str, input_keyword: str) -> str:
	if input_text is None or input_text.strip() == '':
		print('no input_text')
		return None
	if input_keyword is None or input_keyword.strip() == '':
		print('no input_keyword')
		return None
	p = Predictor()
	p.update_text_list(input_text)
	return p.predict(input_keyword)

app = gr.Interface(
	title='Fuzzy Search',
	fn=process_text,
	inputs=[
		gr.Textbox(label='text (comma separated text for id and description)', lines=10),
		gr.Textbox(label='search keywords')
	],
	outputs=[
		gr.Textbox(label='predicted id'),
	],
	allow_flagging='never',
	concurrency_limit=20,
)
app.launch()