app.py

#!git clone https://github.com/sudipmondal1310/Internship.git
#!pip install llama-index==0.5.6
#!pip install langchain==0.0.148
from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext
from langchain import OpenAI
import sys
import os
from IPython.display import Markdown, display
import time
import random
import gradio as gr

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 prompt helper
    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)

    # define LLM
    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="text-davinci-003", max_tokens=num_outputs))
 
    documents = SimpleDirectoryReader(directory_path).load_data()
    
    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
    index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)

    index.save_to_disk('index.json')

    return index

def ask_ai_new(query):
    index = GPTSimpleVectorIndex.load_from_disk('index.json')
    while True: 
        #query = input("What do you want to ask? ")
        response = index.query(query)
        response = Markdown(f"<b>{response.response}</b>")
        print(response)
        return response.data


        

os.environ["OPENAI_API_KEY"]="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"

construct_index("Data")

#pip install gradio


with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    query = gr.inputs.Textbox(label="Enter your message here")
    clear = gr.Button("Clear")

    def user(user_message, history):
        return "", history + [[user_message, None]]

    def bot(history):
        bot_message = ask_ai_new(history[-1][0])
        print(bot_message)
        history[-1][1] = ""
        for character in bot_message:
            history[-1][1] += character
            time.sleep(0.05)
            yield history

    query.submit(user, [query, chatbot], [query, chatbot], queue=False).then(
        bot, chatbot, chatbot
    )
    clear.click(lambda: None, None, chatbot, queue=False)

demo.queue()
demo.launch()