app.py

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)

construct_index("data")

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


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


iface.launch()