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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) |