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app.py
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import argparse
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import logging
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from pathlib import Path
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from threading import Thread
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from typing import List
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import gradio as gr
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logger = logging.getLogger(__name__)
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def parseargs() -> argparse.Namespace:
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"""
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Parses command line arguments for the Financial Assistant Bot.
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Returns:
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argparse.Namespace: An object containing the parsed arguments.
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"""
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parser = argparse.ArgumentParser(description="Financial Assistant Bot")
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parser.add_argument(
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"--env-file-path",
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type=str,
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default=".env",
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help="Path to the environment file",
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)
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parser.add_argument(
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"--logging-config-path",
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type=str,
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default="logging.yaml",
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help="Path to the logging configuration file",
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)
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parser.add_argument(
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"--model-cache-dir",
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type=str,
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default="./model_cache",
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help="Path to the directory where the model cache will be stored",
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)
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parser.add_argument(
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"--embedding-model-device",
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type=str,
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default="cuda:0",
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help="Device to use for the embedding model (e.g. 'cpu', 'cuda:0', etc.)",
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)
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parser.add_argument(
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"--debug",
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action="store_true",
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default=False,
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help="Enable debug mode",
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)
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return parser.parse_args()
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args = parseargs()
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# === Load Bot ===
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def load_bot(
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env_file_path: str = ".env",
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logging_config_path: str = "logging.yaml",
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model_cache_dir: str = "/model_cache",
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embedding_model_device: str = "cuda:0",
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debug: bool = False,
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):
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"""
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Load the financial assistant bot in production or development mode based on the `debug` flag
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In DEV mode the embedding model runs on CPU and the fine-tuned LLM is mocked.
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Otherwise, the embedding model runs on GPU and the fine-tuned LLM is used.
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Args:
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env_file_path (str): Path to the environment file.
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logging_config_path (str): Path to the logging configuration file.
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model_cache_dir (str): Path to the directory where the model cache is stored.
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embedding_model_device (str): Device to use for the embedding model.
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debug (bool): Flag to indicate whether to run the bot in debug mode or not.
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Returns:
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FinancialBot: An instance of the FinancialBot class.
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"""
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from financial_bot import initialize
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# Be sure to initialize the environment variables before importing any other modules.
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initialize(logging_config_path=logging_config_path, env_file_path=env_file_path)
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from financial_bot import utils
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from financial_bot.langchain_bot import FinancialBot
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logger.info("#" * 100)
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utils.log_available_gpu_memory()
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utils.log_available_ram()
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logger.info("#" * 100)
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bot = FinancialBot(
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model_cache_dir=Path(model_cache_dir) if model_cache_dir else None,
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embedding_model_device=embedding_model_device,
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streaming=True,
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debug=debug,
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)
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return bot
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bot = load_bot(
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env_file_path=args.env_file_path,
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logging_config_path=args.logging_config_path,
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model_cache_dir=args.model_cache_dir,
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embedding_model_device=args.embedding_model_device,
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debug=args.debug,
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)
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# === Gradio Interface ===
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def predict(message: str, history: List[List[str]], about_me: str) -> str:
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"""
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Predicts a response to a given message using the financial_bot Gradio UI.
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Args:
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message (str): The message to generate a response for.
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history (List[List[str]]): A list of previous conversations.
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about_me (str): A string describing the user.
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Returns:
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str: The generated response.
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"""
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generate_kwargs = {
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"about_me": about_me,
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"question": message,
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"to_load_history": history,
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}
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if bot.is_streaming:
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t = Thread(target=bot.answer, kwargs=generate_kwargs)
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t.start()
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for partial_answer in bot.stream_answer():
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yield partial_answer
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else:
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yield bot.answer(**generate_kwargs)
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demo = gr.ChatInterface(
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predict,
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textbox=gr.Textbox(
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placeholder="Ask me a financial question",
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label="Financial question",
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container=False,
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scale=7,
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),
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additional_inputs=[
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gr.Textbox(
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"I am a student and I have some money that I want to invest.",
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label="About me",
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)
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],
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title="Your Personal Financial Assistant",
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description="Ask me any financial or crypto market questions, and I will do my best to answer them.",
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theme="soft",
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examples=[
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[
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"What's your opinion on investing in startup companies?",
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"I am a 30 year old graphic designer. I want to invest in something with potential for high returns.",
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],
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[
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"What's your opinion on investing in AI-related companies?",
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"I'm a 25 year old entrepreneur interested in emerging technologies. \
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I'm willing to take calculated risks for potential high returns.",
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],
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[
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"Do you think advancements in gene therapy are impacting biotech company valuations?",
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"I'm a 31 year old scientist. I'm curious about the potential of biotech investments.",
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],
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],
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cache_examples=False,
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retry_btn=None,
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undo_btn=None,
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clear_btn="Clear",
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)
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if __name__ == "__main__":
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demo.queue().launch()
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