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import urllib.request
import gradio as gr
from huggingface_hub import get_token
from chatbot import get_retrieval_qa
from flagging import HuggingFaceDatasetSaver
# get the html data and save it to a file
def download_html(_url: str, _filename: str):
html = urllib.request.urlopen(_url).read()
with open(_filename, "wb") as f:
f.write(html)
url = "https://sea.ai/faq"
filename = "FAQ_SEA.AI.html"
download_html(url, filename)
# load the retrieval QA model
qa = get_retrieval_qa(filename)
# dataset callback
dataset_name = "SEA-AI/seadog-chat-history"
hf_writer = HuggingFaceDatasetSaver(get_token(), dataset_name)
def answer_question(message, history, system):
print(f"{message=}, {history=}, {system=}")
# concatenate the history, message and system
query = " ".join([message, system])
retrieval_qa = qa.invoke(query)
result = retrieval_qa["result"] # "query" and "source_documents" are also available
result = result.replace('"', "").strip() # clean up the result
# save the query and result to the dataset
hf_writer.flag(flag_data=[query, [dict(role="assistant", content=result)]])
return result
title = "✨ SEA Dog"
description = """
<p align="center">
DISCLAIMERS
<br>
I can't remember conversations yet, be patient with me.
<br>
Your queries will be saved to
<a href='https://huggingface.co/datasets/SEA-AI/seadog-chat-history'>this dataset</a>
for analytics purposes.
</p>
"""
css = """
h1 {
text-align: center;
display: block;
}
"""
theme = gr.themes.Default(primary_hue=gr.themes.colors.indigo)
chatbot = gr.Chatbot(
value=[
gr.ChatMessage(
role="assistant",
content="I have memorized the entire SEA.AI FAQ page. Ask me anything about it! 🧠",
),
],
label="SEA Dog",
type="messages",
show_label=False,
show_copy_button=True,
)
def on_like(evt: gr.LikeData):
print(f"{evt.index=}, {evt.value=}, {evt.liked=}")
with gr.ChatInterface(
answer_question,
type=chatbot.type,
chatbot=chatbot,
title=title,
description=description,
additional_inputs=[gr.Textbox("", label="SYSTEM")],
# examples=[
# ["Can SEA.AI see at night?", "You are a helpful assistant."],
# ["Can SEA.AI see at night?", "Reply with sailor slang."],
# ],
cache_examples=False,
submit_btn=None,
css=css,
theme=theme,
) as demo:
# on page load, download the html and save it to a file
demo.load(lambda: download_html(url, filename))
# This needs to be called prior to the first call to callback.flag()
hf_writer.setup([demo.textbox, demo.chatbot], "flagged")
print("flagging setup done!")
# like callback
chatbot.like(on_like, None, None)
if __name__ == "__main__":
demo.launch()
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