app.py

import gradio as gr
import openai
from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain.chat_models import ChatOpenAI
import sys
import os
from IPython.display import Markdown, display


def construct_index(directory_path):
    # set maximum input size
    max_input_size = 4096
    # set number of output tokens
    num_outputs = 2000
    # set maximum chunk overlap
    max_chunk_overlap = 20
    # set chunk size limit
    chunk_size_limit = 600

    # define LLM
    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>"))
'''
# Define the ask_ai() function
def ask_ai(question,api):
    if api == "":
      api = "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"
    os.environ["OPENAI_API_KEY"] = api
    index = GPTSimpleVectorIndex.load_from_disk('index.json')
    response = index.query(question, response_mode="compact")
    return response.response


construct_index("data")

# Create Gradio interface to prompt for API key
api_key = gr.inputs.Textbox(label="Enter your OpenAI API key:")

# Define the interface
iface = gr.Interface(fn=ask_ai, inputs=["text", api_key], outputs="text" ,title="Jim's Chatbot")

# Start the interface
iface.launch()