import gradio as gr
import pandas as pd
import nltk
import random
from bs4 import BeautifulSoup
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
import spacy
from numpy.linalg import norm
from sentence_transformers import SentenceTransformer, util

# nltk.download('punkt')
# nltk.download('wordnet')
# nltk.download('omw-1.4')

threshold = 0.65
sentence_length = 6
questions = [
    "Is it  new or used", "Are there any wear & tear", "Does it come with dust bag, receipt & original box",
    "Are there any scratches, marks", "Are there any fading, stains, discolorization",
    "Is this item customized, repainted or has hardware been replaced", "Is it special edition", "Is there any odour",
    "Are there multiple items or extra add-ons in this listing?",
    "Is there a date code or serial number present on the item?"
]

model = SentenceTransformer("all-MiniLM-L6-v2")


def generate_phrases(desc: str, length: int):
    desc_list = desc.split()
    phrase_list = []
    if len(desc_list) >= length:
        for i in range(len(desc_list) - (length - 1)):
            sub_list = []
            for j in range(i, i + length):
                sub_list.append(desc_list[j])
            phrase_list.append(' '.join(sub_list))
    else:
        phrase_list.append(' '.join(desc_list))

    return phrase_list


def find_answers_new(description: str):
    sentences = generate_phrases(description, sentence_length)
    sentences_embedding = model.encode(sentences)

    answers = []

    for question in questions:
        query_embedding = model.encode(question)
        similarities = util.cos_sim(query_embedding, sentences_embedding)

        similarity_i = 0

        new_row = None

        for similarity in similarities[0]:
            model_answer = sentences[similarity_i]

            similarity_i += 1

            if round(similarity.item(), 2) > threshold:
                if new_row is not None and similarity.item() < new_row['Similarity']:
                    continue

                new_row = {'ModelAnswer': model_answer, 'Similarity': similarity.item()}

        if new_row is not None:
            answers.append(new_row['ModelAnswer'])
        else:
            answers.append('No answer')
    return answers


authorized_users = [("test_user_account", "test_user_pwd"), ]

demo = gr.Interface(fn=find_answers_new,
                    inputs=[gr.Textbox(lines=20, label="Item Description", placeholder="Desc Here...")],
                    outputs=[gr.Textbox(lines=1, label="Is it  new or used"),
                             gr.Textbox(lines=1, label="Are there any wear & tear"),
                             gr.Textbox(lines=1, label="Does it come with dust bag, receipt & original box"),
                             gr.Textbox(lines=1, label="Are there any scratches, marks"),
                             gr.Textbox(lines=1, label="Are there any fading, stains, discolorization"),
                             gr.Textbox(lines=1,
                                        label="Is this item customized, repainted or has hardware been replaced"),
                             gr.Textbox(lines=1, label="Is it special edition"),
                             gr.Textbox(lines=1, label="Is there any odour"),
                             gr.Textbox(lines=1, label="Are there multiple items or extra add-ons in this listing?"),
                             gr.Textbox(lines=1, label="Is there a date code or serial number present on the item?")
                             ])
demo.launch(debug=False, share=True, auth=authorized_users)