from sentence_transformers import SentenceTransformer, util
import os
from tqdm import tqdm
import pandas as pd
import json
import pickle
import torch
import gradio as gr


with open('new_transcript.json', 'r', encoding='utf-8') as openfile:

    # Reading from json file
    json_object1 = json.load(openfile)

json_object1[0]

model = SentenceTransformer('keepitreal/vietnamese-sbert', device='cpu')

#Load sentences & embeddings from disc
with open('embeddings.pkl', "rb") as fIn:
    stored_data = pickle.load(fIn)
    stored_sentences = stored_data['sentences']
    stored_embeddings = stored_data['embeddings']

emb = torch.from_numpy(stored_embeddings)


def semantic_search(query, top_k=20):
  query_embedding = model.encode(query, convert_to_tensor=True)

  # We use cosine-similarity and torch.topk to find the highest 5 scores
  cos_scores = util.cos_sim(query_embedding, emb)[0]
  top_results = torch.topk(cos_scores, k=top_k)

  str_results = ""
  for score, idx in zip(top_results[0], top_results[1]):
    str_results += str(json_object1[idx]) + " - (Score: {:.4f})".format(score) + "\n"

  return str_results


demo = gr.Interface(
    fn=semantic_search,
    inputs=gr.Textbox(lines=2, placeholder="Input text query..."),
    outputs="text",
)
demo.launch(share=True)