#bot from https://beebom.com/how-train-ai-chatbot-custom-knowledge-base-chatgpt-api/
#!pip freeze > requirements2.txt

from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain.chat_models import ChatOpenAI
import gradio as gr
import openai
import sys
import os
import time

#After this version of langchain it created a problem and bot not running
#pip uninstall -y langchain
#pip install langchain==0.0.153

#os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
openai.api_key = os.getenv("OPENAI_API_KEY")

def construct_index(directory_path):
    max_input_size = 4096
    num_outputs = 512
    max_chunk_overlap = 20
    chunk_size_limit = 600

    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)

    llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.1, model_name="gpt-3.5-turbo", max_tokens=num_outputs))

    documents = SimpleDirectoryReader(directory_path).load_data()

    index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper)

    index.save_to_disk('index.json')

    return index

messages = [{"role": "system", "content": """You are a helpful customer service assistant. \
# You respond to questions in the language the user asked the question in. \
# You respond to questions about Dekalin products, services, website and company. \
# You use only the material available in your knowledge base \
# You respond in a friendly and helpful tone, giving clear and very concise answers \
# In your first response you politely include a question that helps you clarify the question the user is asking, \
# and you answer again based on this clarification\
# If you don't know the answer to a question you invite the user to send an email to support@dekalin.de"""}]


def question_answer(question):
    index = GPTSimpleVectorIndex.load_from_disk('index.json')
    while True: 
        response = index.query(question, response_mode="compact")        
        messages.append({"User asks": question, "System response": response.response.strip()})
        #return response.response.strip()
        for el in range(1):
            history = []
            for el in response.response.strip():
                history.append(el)
                word = ''.join(history)
                time.sleep(0.02)
                yield str(word)
        break


#os.environ["OPENAI_API_KEY"] = 'OPENAI_API_KEY'
openai.api_key = os.getenv("OPENAI_API_KEY")

construct_index("context_data/data/done")


demo = gr.Interface(fn=question_answer, inputs="text", outputs="text", cache_examples=False,
                        examples=[['What do I use to install a sky hatch?'],
                                  ['What Dekalin product is best to install an antenna?'],
                                  ['When do I use 8936? '],
                                  ['How do I fix loose screws in the wall? '],
                                  ['What do I use to attach a shelf or a hook to the wall?'],
                                  ['What do I use to grout the shower?'],
                                  ['How do I repair damage to the outside wall of my vehicle?'],
                                  ['How do I repair a tear in my bumper?'],
                                  ['How do I repair damage to the outside wall of my vehicle?']],
                        title="Dekalin helpful AI robot assistant",
                        css="footer {visibility: hidden}")#.queue()


demo.queue()
demo.launch(inline=False, auth=('dekalin', 'dekalin'))