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# gradio imports
import gradio as gr
import os
import time
# Imports
import os
import openai
from langchain.chains import ConversationalRetrievalChain
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chat_models import ChatOpenAI
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.document_loaders import TextLoader
from langchain.memory import ConversationBufferMemory
from langchain.chat_models import ChatOpenAI
css="""
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
"""
title = """
<div style="text-align: center;max-width: 700px;">
<h1>Chat about Dialogues • Games • AI • AI Regulation</h1>
<p style="text-align: left;">Chat is built from:<br />
This is a Dialogue (https://www.jonnyjohnson.com/this-is-a-dialogue)<br />
Game-Making articles (https://dialogues-ai.github.io/papers/docs/ai_regulation/gamemaking)<br />
As well as 25 blog posts contributed to BMC <br />
</div>
"""
prompt_hints = """
<div style="text-align: center;max-width: 700px;">
<p style="text-align: left;">Some things you can ask:<br />
Should I be worried about AIs?<br />
How do we improve the games between AIs and humans?<br />
What is a dialogue? <br />
Do you agree that everything is language? <br />
</div>
"""
# from index import PERSIST_DIRECTORY, CalendarIndex
PERSIST_DIRECTORY = "chromadb"
# Create embeddings
# # create memory object
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
def loading_pdf():
return "Loading..."
def loading_database(open_ai_key):
if open_ai_key is not None:
if os.path.exists(PERSIST_DIRECTORY):
embeddings = OpenAIEmbeddings(openai_api_key=open_ai_key)
docs_retriever = Chroma(persist_directory=PERSIST_DIRECTORY, embedding_function=embeddings)
global qa_chain
qa_chain = ConversationalRetrievalChain.from_llm(ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.0, openai_api_key=open_ai_key),
retriever=docs_retriever.as_retriever(),
memory=memory,
return_source_documents=False
)
return "Ready"
else:
return "You forgot OpenAI API key"
def add_text(history, text):
history = history + [(text, None)]
return history, ""
def bot(history):
response = infer(history[-1][0], history)
history[-1][1] = ""
for character in response:
history[-1][1] += character
time.sleep(0.05)
yield history
def infer(question, history):
res = []
for human, ai in history[:-1]:
pair = (human, ai)
res.append(pair)
chat_history = res
query = question
result = qa_chain({"question": query, "chat_history": chat_history})
return result["answer"]
def update_message(question_component, chat_prompts):
question_component.value = chat_prompts.get_name()
return None
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.HTML(title)
with gr.Column():
with gr.Row():
openai_key = gr.Textbox(label="OpenAI API key", type="password")
submit_api_key = gr.Button("Submit")
with gr.Row():
langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)
chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350)
question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
submit_btn = gr.Button("Send Message")
gr.HTML(prompt_hints)
submit_api_key.click(loading_database, inputs=[openai_key], outputs=[langchain_status], queue=False)
# demo.load(loading_database, None, langchain_status)
question.submit(add_text, [chatbot, question], [chatbot, question]).then(
bot, chatbot, chatbot
)
submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then(
bot, chatbot, chatbot)
demo.queue(concurrency_count=2, max_size=20).launch() |