import urllib.request

from langchain.chains import RetrievalQA
from langchain_community.document_loaders import UnstructuredHTMLLoader
from langchain_openai import OpenAIEmbeddings
from langchain_openai import ChatOpenAI
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.vectorstores import Chroma

import gradio as gr

# get the html data and save it to a file
url = "https://sea.ai/faq"
html = urllib.request.urlopen(url).read()
with open("FAQ_SEA.AI.html", "wb") as f:
    f.write(html)

# load documents
loader = UnstructuredHTMLLoader("FAQ_SEA.AI.html")
documents = loader.load()
# split the documents into chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
# select which embeddings we want to use
embeddings = OpenAIEmbeddings()

# create the vectorestore to use as the index
db = Chroma.from_documents(texts, embeddings)
# expose this index in a retriever interface
retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 2})
# create a chain to answer questions
qa = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(),
    chain_type="stuff",
    retriever=retriever,
    return_source_documents=True,
    verbose=True,
)


def answer_question(message, history, system):
    # unwind the history of last 2 messages
    history = " ".join(f"{user} {bot}" for user, bot in history[-2:])
    # concatenate the history, message and system
    query = " ".join([history, message, system])
    retrieval_qa = qa.invoke(query)
    result = retrieval_qa["result"]
    result = result.replace('"', "").strip()  # clean up the result
    # query = retrieval_qa["query"]
    # source_documents = retrieval_qa["source_documents"]
    return result


title = "✨ SEA Dog"
description = """
<p align="center">
I have memorized the entire SEA.AI FAQ page. Ask me anything about it! 🧠
<br>
You can modify my response by using the <code>SYSTEM</code> input under
<code>Additional Inputs</code>.
</p>
"""

css = """
h1 {
    text-align: center;
    display: block;
}
"""

theme = gr.themes.Default(primary_hue=gr.themes.colors.indigo)

demo = gr.ChatInterface(
    answer_question,
    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."],
    ],
    css=css,
    theme=theme,
)

if __name__ == "__main__":
    demo.launch()