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#Reference : https://medium.com/@tahreemrasul/building-a-chatbot-application-with-chainlit-and-langchain-3e86da0099a6
#Reference : https://platform.deepseek.com/api-docs/api/create-chat-completion
from langchain.chains import LLMChain
from prompts import maths_assistant_prompt_template
from langchain.memory.buffer import ConversationBufferMemory
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
import torch
import chainlit as cl
#from chainlit import Button # Import Button component
# Load the model and tokenizer
model_name = "deepseek-ai/deepseek-math-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
@cl.on_chat_start
async def start_llm():
conversation_memory = ConversationBufferMemory(memory_key="chat_history",
max_len=50,
return_messages=True,
)
llm_chain = LLMChain(llm=model, prompt=maths_assistant_prompt_template, memory=conversation_memory)
cl.user_session.set("llm_chain", llm_chain)
#Send initial message to the user
#await cl.Message("What is your topic of interest?").send()
# Send initial message with selectable buttons
actions = [
cl.Action(name="Probability", value="Probability", description="Select Quiz Topic!"),
cl.Action(name="Linear Algebra", value="Linear Algebra", description="Select Quiz Topic!"),
cl.Action(name="Accounts", value="Accounts", description="Select Quiz Topic!"),
cl.Action(name="Calculus", value="Calculus", description="Select Quiz Topic!")
]
await cl.Message(content="**Pick a Topic and Let the Quiz Adventure Begin!** 🎉📚", actions=actions).send()
@cl.on_message
async def query_llm(message: cl.Message):
llm_chain = cl.user_session.get("llm_chain")
#selected_topic = cl.user_session.get("selected_topic", "probability") # Default to probability if not set
print("Message being sent to the LLM is")
print(message.content)
#response = await llm_chain.ainvoke(message.content,
# callbacks=[
# cl.AsyncLangchainCallbackHandler()])
response = await llm_chain.ainvoke({
"chat_history": llm_chain.memory.load_memory_variables({})["chat_history"],
"question": message.content
}, callbacks=[
cl.AsyncLangchainCallbackHandler()
])
await cl.Message(response["text"]).send()
async def send_good_luck_message():
await cl.Message(content="Good luck! 🍀", align="bottom").send()
async def handle_topic_selection(action: cl.Action):
llm_chain = cl.user_session.get("llm_chain")
#cl.user_session.set("selected_topic", action.value)
#await cl.Message(content=f"Selected {action.value}").send()
response = await llm_chain.ainvoke({
"chat_history": llm_chain.memory.load_memory_variables({})["chat_history"],
"question": f"Quiz me on the topic {action.value}."
}, callbacks=[
cl.AsyncLangchainCallbackHandler()
])
await cl.Message(response["text"]).send()
@cl.action_callback("Linear Algebra")
async def on_action(action: cl.Action):
await handle_topic_selection(action)
@cl.action_callback("Probability")
async def on_action(action: cl.Action):
await handle_topic_selection(action)
@cl.action_callback("Accounts")
async def on_action(action: cl.Action):
await handle_topic_selection(action)
@cl.action_callback("Calculus")
async def on_action(action: cl.Action):
await handle_topic_selection(action)
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