| import gradio as gr | |
| import os | |
| from langchain.retrievers import EnsembleRetriever | |
| from utils import * | |
| import requests | |
| from pyvi import ViTokenizer, ViPosTagger | |
| import time | |
| from transformers import AutoTokenizer, AutoModelForQuestionAnswering | |
| import torch | |
| retriever = load_the_embedding_retrieve(is_ready=False, k=5) | |
| bm25_retriever = load_the_bm25_retrieve(k=5) | |
| ensemble_retriever = EnsembleRetriever( | |
| retrievers=[bm25_retriever, retriever], weights=[0.5, 0.5] | |
| ) | |
| def greet2(quote): | |
| qa_chain = get_qachain(retriever=ensemble_retriever) | |
| prompt = os.environ['PROMPT'] | |
| qa_chain.combine_documents_chain.llm_chain.prompt.messages[0].prompt.template = prompt | |
| llm_response = qa_chain(quote) | |
| return llm_response['result'] | |
| if __name__ == "__main__": | |
| quote = "Địa chỉ nhà trường?" | |
| iface = gr.Interface(fn=greet2, inputs="text", outputs="text") | |
| iface.launch() | |