from langchain.llms import OpenAI
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.docstore.document import Document
import requests
import pathlib
import subprocess
import tempfile
import os
import gradio as gr
import pickle

# using a vector space for our search
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.faiss import FAISS
from langchain.text_splitter import CharacterTextSplitter
                
#loading FAISS search index from disk
with open("search_index.pickle", "rb") as f:
    search_index = pickle.load(f)

#Get GPT3 response using Langchain
def print_answer(question, openai):   #openai_embeddings
    #search_index = get_search_index()
    chain = load_qa_with_sources_chain(openai) #(OpenAI(temperature=0)) 
    response = (
        chain(
            {
                "input_documents": search_index.similarity_search(question, k=4),
                "question": question,
            },
            return_only_outputs=True,
        )["output_text"]
    )
    if len(response.split('\n')[-1].split())>2:
        response = response.split('\n')[0] + ', '.join([' <a href="' + response.split('\n')[-1].split()[i] + '" target="_blank"><u>Click Link' + str(i) + '</u></a>' for i in range(1,len(response.split('\n')[-1].split()))])
    else: 
        response = response.split('\n')[0] + ' <a href="' + response.split('\n')[-1].split()[-1] + '" target="_blank"><u>Click Link</u></a>'
    return response


def chat(message, history, openai_api_key):
    #openai_embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
    openai = OpenAI(temperature=0, openai_api_key=openai_api_key )
    #os.environ["OPENAI_API_KEY"] = openai_api_key
    history = history or []
    message = message.lower()
    response = print_answer(message, openai)   #openai_embeddings
    history.append((message, response))
    return history, history


with gr.Blocks() as demo:
  gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
        <div
        style="
            display: inline-flex;
            align-items: center;
            gap: 0.8rem;
            font-size: 1.75rem;
        "
        >
        <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
            GLIP-BLIP-Vision-Langauge-Obj-Det-VQA QandA - LangChain Bot
        </h1>
        </div>
        <p style="margin-bottom: 10px; font-size: 94%">
        Hi, I'm a Q and A GLIP-BLIP-Vision-Langauge-Obj-Det-VQA expert bot, start by typing in your OpenAI API key, questions/issues you are facing in your GLIP-BLIP-Vision-Langauge-Obj-Det-VQA implementations and then press enter.<br>
        <a href="https://huggingface.co/spaces/ysharma/InstructPix2Pix_Chatbot?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate Space with GPU Upgrade for fast Inference & no queue<br> 
        Built using <a href="https://langchain.readthedocs.io/en/latest/" target="_blank">LangChain</a> and <a href="https://github.com/gradio-app/gradio" target="_blank">Gradio</a> for the GLIP-BLIP-Vision-Langauge-Obj-Det-VQA Repo
        </p>
    </div>""")  
  with gr.Row():
    question = gr.Textbox(label = 'Type in your questions about GLIP-BLIP-Vision-Langauge-Obj-Det-VQA here and press Enter!', placeholder = 'What questions do you want to ask about the GLIP-BLIP-Vision-Langauge-Obj-Det-VQA library?')
    openai_api_key = gr.Textbox(type='password', label="Enter your OpenAI API key here")
  state = gr.State()
  chatbot = gr.Chatbot()
  question.submit(chat, [question, state, openai_api_key], [chatbot, state])

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