import gradio
import numpy as np
import pandas as pd
import re
import fasttext
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(repo_id="facebook/fasttext-id-vectors", filename="model.bin")
model = fasttext.load_model(model_path)
new_catalog = pd.read_csv('new_catalog.csv')
new_prodcatalog_vec = np.load('product_catalog_vec.npy')

def preprocess(text):
    # remove units
    regex_f = r'(\d+[.]{0,1}\d*\s*(kg|KG|Kg|ml|ML|Ml|mL|mil|ltr|LTR|liter|lt|Lt|LT|L|l|kilograms|%|gram|Gram|grm|GR|Gr|gr|g|G|wp|Wp|WP|METER|sp|Amper|Ahm|A|Btr|btr|ons|kapsul|cm|inc|cc|SL|sl))'
    text = re.sub(regex_f, "", text)

    # replace extra characters
    text = text.replace('@', ' ')
    text = text.replace('.', ' ')
    text = text.replace('-', ' ')
    text = text.replace('(', ' ')
    text = text.replace(')', ' ')
    text = text.strip()

    # lowercase
    text = text.lower()
    return text

def extract_features(text):
    return model.get_sentence_vector(text)

def cosine_similarity(v1, v2):
    return np.dot(v1,v2) / (np.linalg.norm(v1)*np.linalg.norm(v2))

def get_most_similar(target, reference, n=5):
    score = []
    for r in reference:
        if np.count_nonzero(r) == 0:  # untuk menghindari score nan
            score.append(0.)
        else:
            score.append(cosine_similarity(target, r))
    # ranking descending
    idx = np.array(score).argsort()[::-1][:n]
    top_score = np.take(score, idx)
    return idx, top_score

def predict_catalog(text):
    text = preprocess(text)
    X = extract_features(text)
    idx, top_score = get_most_similar(X, new_prodcatalog_vec)
    catalog = np.take(new_catalog['Product SKU'].values, idx)[0]
    return catalog

demo = gradio.Interface(
    fn=predict_catalog,
    inputs=gradio.Textbox(placeholder="Enter a product name here..."),
    outputs="label",
    examples=[["NPK Kujang 30.6.8 @ 25 Kg"]])

demo.launch(share=True)