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| # 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 | |
| # Setup logging | |
| logging.basicConfig(level=logging.INFO) | |
| openai.api_key = os.environ['OpenAI'] | |
| 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) | |