search app generated with

gpt_chat_bm25_search.py

"""
Query:
Write a gradio app that shows results of searching in a list of texts:
- tokenizing texts with nltk
- using rank_bm25 library
- displaying results as dataframe under the search box

Response:
Here is an example of a gradio app that uses the rank_bm25 library to search through a list of texts, tokenizes the texts and queries using nltk, and shows the results as a dataframe under the search box:
"""

import gradio as gr
from rank_bm25 import BM25Okapi
import pandas as pd
import nltk

# list of texts to search through
texts = ["Lorem ipsum dolor sit amet",
         "consectetur adipiscing elit",
         "sed do eiusmod tempor incididunt",
         "ut labore et dolore magna aliqua"]

# create the BM25Okapi ranking model
bm25 = BM25Okapi(texts)

# function to search the texts using BM25Okapi and return the top matching results as a dataframe
def search_texts(query):
  # tokenize the query using nltk
  query_tokens = nltk.word_tokenize(query)

  # use the BM25Okapi model to score the relevance of each text to the query
  scores = bm25.get_scores(query_tokens)

  # create a dataframe of the texts and their relevance scores
  df = pd.DataFrame({"Text": texts, "Score": scores})

  # sort the dataframe by relevance score and return the top 3 results
  return df.sort_values(by="Score", ascending=False).head(3)

# create the gradio app with a text input, a dataframe output, and a "search_texts" function
app = gr.Interface(search_texts, [gr.inputs.Textbox(label="Search query")], gr.outputs.Dataframe(columns=["Text", "Score"]), live=True,