|
import os |
|
import gradio as gr |
|
import pinecone |
|
from gpt_index import GPTIndexMemory, GPTPineconeIndex |
|
from langchain.agents import Tool |
|
from langchain.chains.conversation.memory import ConversationBufferMemory |
|
from langchain import OpenAI |
|
from langchain.agents import initialize_agent |
|
|
|
OPENAI_API_KEY=os.environ["OPENAI_API_KEY"] |
|
PINECONE_API_KEY=os.environ["PINECONE_API_KEY"] |
|
|
|
pinecone.init(api_key=PINECONE_API_KEY, environment="us-east1-gcp") |
|
|
|
pindex=pinecone.Index("mahathir") |
|
indexed_pinecone=GPTPineconeIndex([], pinecone_index=pindex) |
|
|
|
tools = [ |
|
Tool( |
|
name = "GPT Index", |
|
func=lambda q: str(indexed_pinecone.query(q)), |
|
description="useful for when you want to answer questions about the author. The input to this tool should be a complete english sentence.", |
|
return_direct=True |
|
), |
|
] |
|
memory = GPTIndexMemory(index=indexed_pinecone, memory_key="chat_history", query_kwargs={"response_mode": "compact"}) |
|
llm=OpenAI(temperature=0) |
|
agent_chain = initialize_agent(tools, llm, agent="conversational-react-description", memory=memory, verbose=True) |
|
|
|
def predict(input, history=[]): |
|
response = agent_chain.run(input) |
|
history = history + [(input, response)] |
|
response = history |
|
|
|
|
|
return response, response |
|
|
|
with gr.Blocks() as demo: |
|
chatbot = gr.Chatbot() |
|
state = gr.State([]) |
|
|
|
with gr.Row(): |
|
gr.Markdown("<h3><center>Train on three books and one wiki. A Doctor in The House (Memoir), his wikipedia, Malaysian Maverick, and The Malay Dillema </center></h3>") |
|
|
|
with gr.Row(): |
|
txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False) |
|
|
|
gr.Examples( |
|
examples=[ |
|
"What are your opinion on malaysian resiliency during covid 19 from the perspective of Mahathir Mohamad", |
|
"how close was anwar ibrahim with mahathir before? any interesting stories?", |
|
"What is the focus for mahathir to move malaysia towards a more modern economy", |
|
], |
|
inputs=txt, |
|
) |
|
|
|
txt.submit(predict, [txt, state], [chatbot, state]) |
|
|
|
|
|
demo.launch() |
