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| from dotenv import load_dotenv | |
| import streamlit as st | |
| from langchain.chains import RetrievalQA | |
| #from langchain.llms import HuggingFaceHub | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.vectorstores import Qdrant | |
| import qdrant_client | |
| import os | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.chains import ConversationalRetrievalChain | |
| from htmlTemplates import css, user_template, bot_template | |
| from langchain_community.embeddings.fastembed import FastEmbedEmbeddings | |
| import streamlit as st | |
| def get_vector_store(): | |
| client = qdrant_client.QdrantClient( | |
| os.getenv("QDRANT_HOST"), | |
| api_key=os.getenv("QDRANT_API_KEY") | |
| ) | |
| embeddings = embeddings = FastEmbedEmbeddings(model_name="BAAI/bge-base-en-v1.5") | |
| vector_store = Qdrant( | |
| client=client, | |
| collection_name="PenalCode", | |
| embeddings=embeddings, | |
| ) | |
| return vector_store | |
| def get_conversation_chain(vectorstore): | |
| llm = ChatOpenAI() | |
| #llm = HuggingFaceHub( repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512}) | |
| memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True) | |
| conversation_chain = ConversationalRetrievalChain.from_llm( | |
| llm =llm, | |
| retriever=vectorstore.as_retriever(), | |
| memory=memory | |
| ) | |
| return conversation_chain | |
| def handle_userinput(user_question): | |
| response = st.session_state.conversation({'question':user_question}) | |
| st.session_state.chat_history = response['chat_history'] | |
| for i, message in enumerate(st.session_state.chat_history): | |
| if i%2 == 0: | |
| st.write(user_template.replace("{{MSG}}",message.content), unsafe_allow_html=True) | |
| else: | |
| st.write(bot_template.replace("{{MSG}}",message.content), unsafe_allow_html=True) | |
| def main(): | |
| load_dotenv() | |
| st.set_page_config(page_title="Legal Assistant", page_icon=":robot_face:") | |
| st.write(css, unsafe_allow_html=True) | |
| st.markdown("<h1 style='text-align: center; color: red;font-family:Georgia'>AI Lawyer Bot 🤖</h1>", unsafe_allow_html=True) | |
| st.subheader("\"_Is that legal❓_\"") | |
| st.write("This bot is made to answer all your legal queries in the context of the Indian Penal Code.") | |
| with st.expander("**Disclamer**"): | |
| st.write("1. **This is not legal advice**.") | |
| st.write("2. While the model has the context of the IPC it has not been fine-tuned and hence may not be able to answer all your queries. ") | |
| st.divider() | |
| st.caption("Try something like \"What is the punishment for criminal intimidation?\" or \"How is theft defined in the IPC?\"") | |
| if "conversation" not in st.session_state: | |
| st.session_state.conversation = None | |
| if "chat_history" not in st.session_state: | |
| st.session_state.chat_history = None | |
| # create vector store | |
| vector_store = get_vector_store() | |
| st.session_state.conversation = get_conversation_chain(vector_store) | |
| user_question = st.text_input("Ask your questions here:") | |
| if user_question: | |
| handle_userinput(user_question) | |
| if __name__ == '__main__': | |
| main() | |