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Build error
File size: 2,519 Bytes
8f53255 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | from langchain_nvidia_ai_endpoints import ChatNVIDIA
import streamlit as st
from tools.summarizer import Summarizer
from dotenv import load_dotenv
import os
load_dotenv()
class QuestionAnswering(object):
def __init__(self, model_name, memory) -> None:
self.model_name = model_name
self.memory = memory
self.llm = ChatNVIDIA(
model=self.model_name,
api_key=os.getenv('NV_API_KEY'),
max_tokens=700,
temperature=0.01,
top_p=.7
)
self.summarizer = Summarizer()
def generate_answer(self, question:str):
if self.model_name in ['google/gemma-2-27b-it', 'microsoft/phi-3-medium-128k-instruct']:
prompt = [
{
'role' : "assistant",
'content' : "You are a helpful and native chatbot who can guide clients to write and talk naturally\
and reduce their mistakes in the different aspects of their English skills. And please\
guide users in short and concise answers."
},
]
else:
prompt = [
{
'role' : "system",
'content' : "You are a helpful and native chatbot who can guide clients to write and talk naturally\
and reduce their mistakes in the different aspects of their English skills. And please\
guide users in short and concise answers."
},
]
with st.expander('Question'):
st.write('User: ', question)
if len(self.memory) != 0:
# Save the last 2 conversations
short_term_memory = self.memory[-4:]
try:
summarized_short_term_memory = self.summarizer.summarize(short_term_memory)
except:
st.warning(body="Refresh the page or Try it again later.", icon="🤖")
else:
prompt.extend(summarized_short_term_memory)
user_dict = {'role' : 'user', 'content' : question}
self.memory.append(user_dict)
prompt.append(user_dict)
res = self.llm.invoke(prompt)
assistant_dict = {'role' : res.response_metadata['role'], 'content' : res.content}
self.memory.append(assistant_dict)
with st.expander('Answer'):
st.write("Assistant: ", assistant_dict['content'])
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