import gradio as gr
import random
import time
import boto3
from botocore import UNSIGNED
from botocore.client import Config
import zipfile

from langchain.llms import HuggingFaceHub
model_id = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={"temperature":0.1, "max_new_tokens":1024})

from langchain.embeddings import HuggingFaceHubEmbeddings
embeddings = HuggingFaceHubEmbeddings()

from langchain.vectorstores import FAISS

from langchain.chains import RetrievalQA

s3 = boto3.client('s3', config=Config(signature_version=UNSIGNED))
s3.download_file('rad-rag-demos', 'vectorstores/faiss_db_ray.zip', './chroma_db/faiss_db_ray.zip')
with zipfile.ZipFile('./chroma_db/faiss_db_ray.zip', 'r') as zip_ref:
    zip_ref.extractall('./chroma_db/')

FAISS_INDEX_PATH='./chroma_db/faiss_db_ray'
#embeddings = HuggingFaceHubEmbeddings("multi-qa-mpnet-base-dot-v1")
embeddings = HuggingFaceHubEmbeddings()
db = FAISS.load_local(FAISS_INDEX_PATH, embeddings)
retriever = db.as_retriever(search_type = "mmr")

global qa 
qa = RetrievalQA.from_chain_type(llm=model_id, chain_type="stuff", retriever=retriever)


def add_text(history, text):
    history = history + [(text, None)]
    return history, ""

def bot(history):
    response = infer(history[-1][0])
    history[-1][1] = response['result']
    return history

def infer(question):  
    query = question
    result = qa({"query": query})
    return result

css="""
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
"""

title = """
<div style="text-align: center;max-width: 700px;">
    <h1>Chat with the RAY Docs</h1>
    <p style="text-align: center;">The AI bot is here to help you with the RAY Documentation, <br />
    start asking questions about the open-source software </p>
</div>
"""


with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.HTML(title)      
        chatbot = gr.Chatbot([], elem_id="chatbot")
        with gr.Row():
            question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
    question.submit(add_text, [chatbot, question], [chatbot, question]).then(
        bot, chatbot, chatbot
    )

demo.launch()