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import time
import os
import streamlit as st
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferWindowMemory
from langchain.chains import ConversationalRetrievalChain
from langchain_together import Together
from footer import footer
# Set the Streamlit page configuration and theme
st.set_page_config(page_title="BharatLAW", layout="centered")
# Display the logo image
col1, col2, col3 = st.columns([1, 30, 1])
with col2:
st.image("images/banner.png", use_column_width=True)
def hide_hamburger_menu():
st.markdown("""
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
""", unsafe_allow_html=True)
hide_hamburger_menu()
# Initialize session state for messages and memory
if "messages" not in st.session_state:
st.session_state.messages = []
if "memory" not in st.session_state:
st.session_state.memory = ConversationBufferWindowMemory(k=2, memory_key="chat_history", return_messages=True)
@st.cache_resource
def load_embeddings():
"""Load and cache the embeddings model."""
return HuggingFaceEmbeddings(model_name="law-ai/InLegalBERT")
embeddings = load_embeddings()
db = FAISS.load_local("ipc_embed_db", embeddings, allow_dangerous_deserialization=True)
db_retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 3})
prompt_template = """
<s>[INST]
As a legal chatbot specializing in the Indian Penal Code, you are tasked with providing highly accurate and contextually appropriate responses. Ensure your answers meet these criteria:
- Respond in a bullet-point format to clearly delineate distinct aspects of the legal query.
- Each point should accurately reflect the breadth of the legal provision in question, avoiding over-specificity unless directly relevant to the user's query.
- Clarify the general applicability of the legal rules or sections mentioned, highlighting any common misconceptions or frequently misunderstood aspects.
- Limit responses to essential information that directly addresses the user's question, providing concise yet comprehensive explanations.
- Avoid assuming specific contexts or details not provided in the query, focusing on delivering universally applicable legal interpretations unless otherwise specified.
- Conclude with a brief summary that captures the essence of the legal discussion and corrects any common misinterpretations related to the topic.
CONTEXT: {context}
CHAT HISTORY: {chat_history}
QUESTION: {question}
ANSWER:
- [Detail the first key aspect of the law, ensuring it reflects general application]
- [Provide a concise explanation of how the law is typically interpreted or applied]
- [Correct a common misconception or clarify a frequently misunderstood aspect]
- [Detail any exceptions to the general rule, if applicable]
- [Include any additional relevant information that directly relates to the user's query]
</s>[INST]
"""
prompt = PromptTemplate(template=prompt_template,
input_variables=['context', 'question', 'chat_history'])
api_key = os.getenv('TOGETHER_API_KEY')
llm = Together(model="mistralai/Mixtral-8x22B-Instruct-v0.1", temperature=0.5, max_tokens=1024, together_api_key=api_key)
qa = ConversationalRetrievalChain.from_llm(llm=llm, memory=st.session_state.memory, retriever=db_retriever, combine_docs_chain_kwargs={'prompt': prompt})
def extract_answer(full_response):
"""Extracts the answer from the LLM's full response by removing the instructional text."""
answer_start = full_response.find("Response:")
if answer_start != -1:
answer_start += len("Response:")
answer_end = len(full_response)
return full_response[answer_start:answer_end].strip()
return full_response
def reset_conversation():
st.session_state.messages = []
st.session_state.memory.clear()
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.write(message["content"])
input_prompt = st.chat_input("Say something...")
if input_prompt:
with st.chat_message("user"):
st.markdown(f"**You:** {input_prompt}")
st.session_state.messages.append({"role": "user", "content": input_prompt})
with st.chat_message("assistant"):
with st.spinner("Thinking 💡..."):
result = qa.invoke(input=input_prompt)
message_placeholder = st.empty()
answer = extract_answer(result["answer"])
# Initialize the response message
full_response = "⚠️ **_Note: Information provided may be inaccurate._** \n\n\n"
for chunk in answer:
# Simulate typing by appending chunks of the response over time
full_response += chunk
time.sleep(0.02) # Adjust the sleep time to control the "typing" speed
message_placeholder.markdown(full_response + " |", unsafe_allow_html=True)
st.session_state.messages.append({"role": "assistant", "content": answer})
if st.button('🗑️ Reset All Chat', on_click=reset_conversation):
st.experimental_rerun()
# Define the CSS to style the footer
footer()
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