import gradio as gr
import json 
import os
from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain import OpenAI
import sys

from IPython.display import Markdown, display


def construct_index(directory_path):
    max_input_size = 4096
    num_outputs = 2000
    max_chunk_overlap = 20
    chunk_size_limit = 600
    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)


def ask_ai(question, api_key):
    os.environ["OPENAI_API_KEY"] = api_key
    index = GPTSimpleVectorIndex.load_from_disk('index.json')
    response = index.query(question, response_mode="compact")
    return response.response


def chatbot_interface(api_key):
    iface = gr.Interface(fn=ask_ai, inputs=["text", gr.inputs.Textbox(label="OpenAI API Key")], outputs="text", title="Chatbot")
    iface.launch()


construct_index("data")
chatbot_interface("") # start interface with empty api key as default value