app.py

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

    documents = SimpleDirectoryReader(directory_path).load_data()

    index = GPTSimpleVectorIndex.from_documents(documents)

    index.save_to_disk('index.json')

    return index

def ask_ai():
    index = GPTSimpleVectorIndex.load_from_disk('index.json')
    while True: 
        query = input("What do you want to ask? ")
        response = index.query(query, response_mode="compact")
        display(Markdown(f"Response: <b>{response.response}</b>"))



os.environ["OPENAI_API_KEY"] = "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"


construct_index("context_data.txt")

iface = gr.Interface(fn=ask_ai, inputs="text", outputs="text" ,title="Chatbot")

iface.launch()