gradio/demo/demo4/predict.py

"""
This examples demonstrates the setup for Question-Answer-Retrieval.

You can input a query or a question. The script then uses semantic search
to find relevant passages in Simple English Wikipedia (as it is smaller and fits better in RAM).

As model, we use: nq-distilbert-base-v1

It was trained on the Natural Questions dataset, a dataset with real questions from Google Search
together with annotated data from Wikipedia providing the answer. For the passages, we encode the
Wikipedia article tile together with the individual text passages.

Google Colab Example: https://colab.research.google.com/drive/11GunvCqJuebfeTlgbJWkIMT0xJH6PWF1?usp=sharing
"""
import json
import time
import gzip
import os

base_directory = os.path.dirname(os.path.realpath(__file__))

def predict(query: str):
    pass

if __name__ == "__main__":
    predict("What is the capital of Germany?")