Fiverr Resources Article Source Code for How to Train ChatGPT

from llama_index import SimpleDirectoryReader, StorageContext, load_index_from_storage, GPTVectorStoreIndex, LLMPredictor, PromptHelper
from langchain.chat_models import ChatOpenAI
import gradio as gr
import os
import tkinter as tk
import openai
from tkinter import filedialog

os.environ["OPENAI_API_KEY"] = '<YOUR-API-KEY>'
# sometimes, we need to explicitly set this as well
openai.api_key = '<YOUR-API-KEY>'

def construct_index(directory_path):
    max_input_size = 4096
    num_outputs = 512
    max_chunk_overlap = 0.2
    chunk_size_limit = 600

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

    llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.7, model_name="gpt-4-32k", max_tokens=num_outputs))

    documents = SimpleDirectoryReader(directory_path).load_data()

    index = GPTVectorStoreIndex(documents,
                                llm_predictor=llm_predictor,
                                prompt_helper=prompt_helper,
                                show_progress=True,
                                store_nodes_override=True)

    index.storage_context.persist(persist_dir=storage_folder)

    return index

def chatbot(input_text):
    response = qry.query(input_text)
    return response.response

iface = gr.Interface(fn=chatbot,
                     inputs=gr.components.Textbox(lines=7, label="Hello, how my I help you today?"),
                     outputs="text",
                     title="Fiverr Demo Chatbot")

root = tk.Tk()
root.withdraw()  # Hide the main window
folder_selected = filedialog.askdirectory()

if (folder_selected == ""):
    exit()

storage_folder = os.path.join(folder_selected, "storage")

index = construct_index(folder_selected)
index = load_index_from_storage(StorageContext.from_defaults(persist_dir=storage_folder))
qry = index.as_query_engine()
iface.launch()