app.py

from gpt_index import GPTSimpleVectorIndex
from langchain import OpenAI
import gradio as gr
from gradio import Interface, Textbox
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"
INDEX_FILENAME = "index2.json"
DATA_FILEP = os.path.join("data", DATA_FILENAME)
INDEX_FILE = os.path.join("data", INDEX_FILENAME)

# 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__)

#Clones the distant repo to the local repo
repo = Repository(
    local_dir='data', 
    clone_from=DATASET_REPO_URL, 
    use_auth_token=HF_TOKEN)

print(f"Repo local_dir: {repo.local_dir}")
print(f"Repo files: {os.listdir(repo.local_dir)}")

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")
            print(f"Wrote to datafile: {datetime.now()},{chatinput},{chatresponse}\n")
        
        #trial - take out if fails to write to local directory
        with open('backup.txt', "a") as file:
            file.write(f"{datetime.now()},{chatinput},{chatresponse}\n")
            print(f"Wrote to datafile: {datetime.now()},{chatinput},{chatresponse}\n")

    return generate_text()

def get_index(index_file_path):
    if os.path.exists(index_file_path):
        load_json_file(index_file_path)
        index_size = os.path.getsize(index_file_path)
        print(f"Size of {index_file_path}: {index_size} bytes") #let me know how big json file is.
        return GPTSimpleVectorIndex.load_from_disk(index_file_path)
    else:
        print(f"Error: '{index_file_path}' does not exist.")
        sys.exit()

def load_json_file(filepath):
    with open(filepath, 'r') as f:
        file_contents = f.read()
        print ("JSON FILE HEADER:")
        print(file_contents[:500]) # print only the first 500 characters

index = get_index(INDEX_FILE)
        
# passes the prompt to the chatbot

def chatbot(input_text, mentioned_person='Mediator John Haynes', confidence_threshold=0.5):
    prompt = f"You are {mentioned_person}. Answer this: {input_text}. Reply from the contextual data or say you don't know. To finish, ask an insightful question."
    response = index.query(prompt, response_mode="default", verbose=True)

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


with open('about.txt', 'r') as file:
    about = file.read()

iface = Interface(
    fn=chatbot,
    inputs=Textbox("Enter your question"),
    outputs="text",
    title="AI Chatbot trained on J. Haynes mediation material, v0.5",
    description=about)
                                         
iface.launch()