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# from langchain_community.chat_models import ChatAnthropic
from langchain_anthropic import ChatAnthropic
# from langchain.chat_models import ChatAnthropic
from langchain import PromptTemplate, LLMChain
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
AIMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.schema import AIMessage, HumanMessage, SystemMessage
import streamlit as st
from dotenv import load_dotenv
import PyPDF2
load_dotenv()
class LegalExpert:
def __init__(self):
self.system_prompt = self.get_system_prompt()
self.user_prompt = HumanMessagePromptTemplate.from_template("{legal_question}")
full_prompt_template = ChatPromptTemplate.from_messages(
[self.system_prompt, self.user_prompt]
)
self.chat = ChatAnthropic(model_name="claude-3-opus-20240229", max_tokens=4000)
self.chain = LLMChain(llm=self.chat, prompt=full_prompt_template)
def get_system_prompt(self):
# system_prompt = """
# You are a Canadian Legal Expert.
# Under no circumstances do you give legal advice.
# You are adept at explaining the law in laymans terms, and you are able to provide context to legal questions.
# While you can add context outside of the provided context, please do not add any information that is not directly relevant to the question, or the provided context.
# You speak {language}.
# ### CONTEXT
# {context}
# ### END OF CONTEXT
# """
system_prompt = """
You are a Singapore Legal Expert.
Under no circumstances do you give legal advice.
You are adept at summarizing the audio transcripts and conversations, and you are able to provide context to legal and social questions.
While you can add context outside of the provided context, please do not add any information that is not directly relevant to the question, or the provided context.
You speak {language}.
### CONTEXT
{context}
### END OF CONTEXT
"""
return SystemMessagePromptTemplate.from_template(system_prompt)
def run_chain(self, language, context, question):
return self.chain.run(
language=language, context=context, legal_question=question
)
def retrieve_pdf_text(pdf_file):
pdf_reader = PyPDF2.PdfReader(pdf_file)
text = ""
for page in pdf_reader.pages:
text += page.extract_text()
return text
# create a streamlit app
st.title("KILSA Transcript Specialist")
if "LegalExpert" not in st.session_state:
st.session_state.LegalExpert = LegalExpert()
# create a upload file widget for a pdf
pdf_file = st.file_uploader("Upload a PDF file", type=["pdf"])
# if a pdf file is uploaded
if pdf_file:
# retrieve the text from the pdf
if "context" not in st.session_state:
st.session_state.context = retrieve_pdf_text(pdf_file)
# create a button that clears the context
if st.button("Clear context"):
st.session_state.__delitem__("context")
st.session_state.__delitem__("legal_response")
# if there's context, proceed
if "context" in st.session_state:
# create a dropdown widget for the language
language = st.selectbox("Language", ["English"])
# create a text input widget for a question
question = st.text_input("Ask a question")
# create a button to run the model
if st.button("Run"):
# run the model
legal_response = st.session_state.LegalExpert.run_chain(
language=language, context=st.session_state.context, question=question
)
if "legal_response" not in st.session_state:
st.session_state.legal_response = legal_response
else:
st.session_state.legal_response = legal_response
# display the response
if "legal_response" in st.session_state:
st.write(st.session_state.legal_response)
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