app.py

from llama_index import GPTSimpleVectorIndex
from langchain import OpenAI
import gradio as gr
import sys
import os
import datetime
import huggingface_hub
from huggingface_hub import Repository
from datetime import datetime
import csv

os.environ["OPENAI_API_KEY"] = os.environ['SECRET_CODE']

# Need to write to persistent dataset because cannot store temp data on spaces
DATASET_REPO_URL = "https://huggingface.co/datasets/peterpull/MediatorBot"
DATA_FILENAME = "data.txt"
DATA_FILE = os.path.join("data", DATA_FILENAME)

# I am guessing we need a write access token.
HF_TOKEN = os.environ.get("HF_TOKEN")
print("HF TOKEN is none?", HF_TOKEN is None)
print("HF hub ver", huggingface_hub.__version__)

repo = Repository(
    local_dir="data", 
    clone_from=DATASET_REPO_URL, 
    use_auth_token=HF_TOKEN)


def generate_text() -> str:
    with open(DATA_FILE) as file:
        text = ""
        for line in file:
            row_parts = line.strip().split(";")
            if len(row_parts) != 3:
                continue
            user, chatbot, time = row_parts
            text += f"Time: {time}\nUser: {user}\nChatbot: {chatbot}\n\n"
        return text if text else "No messages yet"

def store_message(chatinput: str, chatresponse: str):
    if chatinput and chatresponse:
        with open(DATA_FILE, "a") as file:
            file.write(f"{datetime.now()},{chatinput},{chatresponse}\n")

    return generate_text()
            
#gets the index file which is the context data
def get_index(index_file_path):
    if os.path.exists(index_file_path):
        return GPTSimpleVectorIndex.load_from_disk(index_file_path)
    else:
        print(f"Error: '{index_file_path}' does not exist.")
        sys.exit()

# passes the prompt to the chatbot
def chatbot(input_text, mentioned_person='Mediator John Haynes'):
    index = get_index('./index/indexsmall.json')
    prompt = f"You are {mentioned_person}: {input_text}\n\n At the end of your answer  ask a provocative question."
    response = index.query(prompt, response_mode="compact")

    store_message(input_text,response)
    
    # return the response
    return response.response



iface = gr.Interface(fn=chatbot, 
                     inputs=gr.inputs.Textbox("Enter your question"), 
                     outputs="text",
                     title="AI Chatbot trained on J. Haynes mediation material, v0.1",
                     description="test")
iface.launch()