doctork/doctor.py

from langchain.chat_models import ChatOpenAI
#from langchain.llms import OpenAI
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import messages_from_dict, messages_to_dict
from time import sleep
import sys
import os

def slowprint(s):
    for c in s+'\n':
        sys.stdout.write(c)
        sys.stdout.flush()
        sleep(0.01)

MODEL_NAME = "gpt-3.5-turbo-16k-0613"

# Read prompt template
template = open("prompt-template.txt").read().strip()

PROMPT = PromptTemplate(input_variables=["history", "input"], template=template)

memory = ConversationBufferWindowMemory(ai_prefix="DoctorK", human_prefix="JJ", k=64)
for line in open("book-chats.txt").readlines():
    v=[s.strip() for s in line.split(":")]
    if len(v)!=2:
        continue
    if v[0].lower()=="doc":
        memory.chat_memory.add_ai_message(v[1])
    else:
        memory.chat_memory.add_user_message(v[1])

conversation = ConversationChain(
    prompt=PROMPT,
    llm=ChatOpenAI(model_name=MODEL_NAME),
    verbose=False,
    memory=memory
)

sys.stderr.write(chr(27) + "[2J") # Clear screen ;)
sys.stderr.flush()
slowprint("""=====================================================================
Interactive Talk-Program loaded and started.
Users:
* [Doctor Kernel]
* [JJ] <- you
Hit <Enter> to exit
=====================================================================""")
slowprint("Doctor Kernel: {}".format(conversation.predict(input="Hello again, doctor. Please remind me what we were talking about last time.")))
while True:
    try:
        prompt = input("> ").strip()
    except EOFError:
        prompt = ""
    if not prompt:
            slowprint("""
=====================================================================
End of Talk
=====================================================================""")
            sys.exit(0)
    slowprint("\nJJ: {}".format(prompt))
    slowprint("Doctor Kernel: {}".format(conversation.predict(input=prompt)))