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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 = "index.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") | |
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): | |
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() | |
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}. Only reply from the contextual data, or say you don't know. At the end of your answer ask an insightful question." | |
response = index.query(prompt, response_mode="default") | |
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() |