code: # Import standard libraries
  import os
  import re
  import json
  import getpass
  import logging
  
  # Import third-party libraries for web scraping, API interactions, and data processing
  import requests
  import pandas as pd
  from bs4 import BeautifulSoup
  
  # Import libraries for interacting with OpenAI and other language models
  import openai
  import llama_index
  from llama_index.llms import OpenAI
  from llama_index.embeddings import OpenAIEmbedding
  from llama_index.llms import (
      CustomLLM,
      CompletionResponse,
      CompletionResponseGen,
      LLMMetadata,
  )
  
  # Import for creating web interfaces
  import gradio as gr
  
  # Import specific utilities for news feed parsing and query processing
  from RAG_utils import NewsFeedParser, HybridRetriever, NewsQueryEngine
  
  with open('config.json') as config_file:
      config = json.load(config_file)
      
  # Setup logging
  logging.basicConfig(level=logging.INFO)
  openai.api_key = config['OPENAI_API_KEY']
  os.environ['OPENAI_API_KEY'] = openai.api_key
  
  
  llm = OpenAI(model="gpt-4", temperature=0.1, max_tokens=512)
  embed_model = OpenAIEmbedding()
  
  
  def chatbot(input_text):
      # Create an instance of NewsFeedParser and process query
      news_parser = NewsFeedParser()
      documents = news_parser.process_and_chunk_articles(input_text)
  
      # Initialize the query engine with the processed documents
      pdf_query_engine = NewsQueryEngine(documents, llm, embed_model)
      query_engine = pdf_query_engine.setup_query_engine()
  
      # Process the query using the query engine
      response = query_engine.query(input_text)
      return response
  
  # Gradio interface setup
  iface = gr.Interface(
      fn=chatbot,
      inputs=gr.components.Textbox(lines=3, label="Enter your text:"),
      outputs=gr.components.Textbox(lines=20, label="Answer:"),
      title="FinWise Explorer"
  )
  
  # Launch the Gradio interface
  iface.launch(share=True)
  
apis: llama_index.llms.OpenAI llama_index.embeddings.OpenAIEmbedding
extract_api: [((854, 893), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO'}), '(level=logging.INFO)\n', (873, 893), False, 'import logging\n'), ((990, 1044), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'model': '"""gpt-4"""', 'temperature': '(0.1)', 'max_tokens': '(512)'}), "(model='gpt-4', temperature=0.1, max_tokens=512)\n", (996, 1044), False, 'from llama_index.llms import OpenAI\n'), ((1059, 1076), 'llama_index.embeddings.OpenAIEmbedding', 'OpenAIEmbedding', ([], {}), '()\n', (1074, 1076), False, 'from llama_index.embeddings import OpenAIEmbedding\n'), ((810, 832), 'json.load', 'json.load', (['config_file'], {}), '(config_file)\n', (819, 832), False, 'import json\n'), ((1183, 1199), 'RAG_utils.NewsFeedParser', 'NewsFeedParser', ([], {}), '()\n', (1197, 1199), False, 'from RAG_utils import NewsFeedParser, HybridRetriever, NewsQueryEngine\n'), ((1354, 1398), 'RAG_utils.NewsQueryEngine', 'NewsQueryEngine', (['documents', 'llm', 'embed_model'], {}), '(documents, llm, embed_model)\n', (1369, 1398), False, 'from RAG_utils import NewsFeedParser, HybridRetriever, NewsQueryEngine\n'), ((1645, 1701), 'gradio.components.Textbox', 'gr.components.Textbox', ([], {'lines': '(3)', 'label': '"""Enter your text:"""'}), "(lines=3, label='Enter your text:')\n", (1666, 1701), True, 'import gradio as gr\n'), ((1715, 1763), 'gradio.components.Textbox', 'gr.components.Textbox', ([], {'lines': '(20)', 'label': '"""Answer:"""'}), "(lines=20, label='Answer:')\n", (1736, 1763), True, 'import gradio as gr\n')]