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

construct_index("data")

api_key_input = gr.inputs.Textbox(label="Enter your OpenAI API Key")

question_input = gr.inputs.Textbox(label="Ask a question")

output_text = gr.outputs.Textbox(label="Answer")

iface = gr.Interface(fn=ask_ai, inputs=[question_input, api_key_input], outputs=output_text, title="OpenAI Chatbot")

iface.launch()