import os

import gradio as gr
import pkg_resources
from dotenv import load_dotenv

load_dotenv()


def package_installed(package_name):
    try:
        pkg_resources.get_distribution(package_name)
    except pkg_resources.DistributionNotFound:
        return False
    else:
        return True


def answer(query):
    answer = agent.answer(query=query)
    return answer


if not package_installed("cv_assistant"):
    os.system("pip install cv_assistant-0.1-py2.py3-none-any.whl")

from cv_assistant.agent import Agent  # noqa: E402

agent = Agent(
    faiss_index_path="./content_assets/docs.index",
    faise_store_path="./content_assets/faiss_store.pkl",
)

description = """
### Ask about my experience, skills, and education!

I built this using [Gradio](https://gradio.app) and [LangChain](https://langchain.readthedocs.io/en/latest/).
"""  # noqa: E501
title = "Career Chatbot"

hf_writer = gr.HuggingFaceDatasetSaver(os.getenv("HF_TOKEN"), "cv-assistant-logging")

iface = gr.Interface(
    fn=answer,
    inputs=gr.Textbox(
        value="What's his experience in recommender systems?", label="Question"
    ),
    outputs=gr.Textbox(label="Answer"),
    description=description,
    title=title,
    analytics_enabled=True,
    allow_flagging="auto",
    flagging_callback=hf_writer,
)
iface.launch()