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| import streamlit as st | |
| from streamlit_chat import message | |
| from streamlit.components.v1 import html | |
| from dataclasses import dataclass | |
| from typing import Literal | |
| import os | |
| from llamaapi import LlamaAPI | |
| from langchain_experimental.llms import ChatLlamaAPI | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| import pinecone | |
| from langchain.vectorstores import Pinecone | |
| from langchain.prompts import PromptTemplate | |
| from langchain.chains import RetrievalQA | |
| from langchain_groq import ChatGroq | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.memory import ChatMessageHistory, ConversationBufferMemory | |
| import time | |
| HUGGINGFACEHUB_API_TOKEN = st.secrets['HUGGINGFACEHUB_API_TOKEN'] | |
| class Message: | |
| """Class for keeping track of a chat message.""" | |
| origin: Literal["👤 Human", "👨🏻⚖️ Ai"] | |
| message: str | |
| def download_hugging_face_embeddings(): | |
| embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2') | |
| return embeddings | |
| def initialize_session_state(): | |
| if "history" not in st.session_state: | |
| st.session_state.history = [] | |
| if "conversation" not in st.session_state: | |
| llama = LlamaAPI(st.secrets["LlamaAPI"]) | |
| model = ChatLlamaAPI(client=llama) | |
| chat = ChatGroq(temperature=0.5, groq_api_key=st.secrets["Groq_api"], model_name="mixtral-8x7b-32768") | |
| embeddings = download_hugging_face_embeddings() | |
| # Initializing the Pinecone | |
| pinecone.init( | |
| api_key=st.secrets["PINECONE_API_KEY"], # find at app.pinecone.io | |
| environment=st.secrets["PINECONE_API_ENV"] # next to api key in console | |
| ) | |
| index_name = "legal-advisor" # put in the name of your pinecone index here | |
| docsearch = Pinecone.from_existing_index(index_name, embeddings) | |
| prompt_template = """ | |
| You are a trained bot to guide people about Indian Law. You will answer user's query with your knowledge and the context provided. | |
| If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. | |
| Also check if the given context answer the question asked if not don't answer using that context. | |
| Do not say thank you and tell you are an AI Assistant and be open about everything. | |
| Use the following pieces of context to answer the users question. | |
| Give very detailed answer. | |
| Context: {context} | |
| Question: {question} | |
| Only return the helpful answer below and nothing else. | |
| Helpful answer: | |
| """ | |
| PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"]) | |
| message_history = ChatMessageHistory() | |
| memory = ConversationBufferMemory( | |
| memory_key="chat_history", | |
| output_key="answer", | |
| chat_memory=message_history, | |
| return_messages=True, | |
| ) | |
| retrieval_chain = ConversationalRetrievalChain.from_llm(llm=chat, | |
| chain_type="stuff", | |
| retriever=docsearch.as_retriever( | |
| search_kwargs={'k': 2}), | |
| return_source_documents=True, | |
| combine_docs_chain_kwargs={"prompt": PROMPT}, | |
| memory= memory | |
| ) | |
| st.session_state.conversation = retrieval_chain | |
| def on_click_callback(): | |
| human_prompt = st.session_state.human_prompt | |
| st.session_state.human_prompt = "" | |
| response = st.session_state.conversation(human_prompt) | |
| llm_response = response['answer'] | |
| st.session_state.history.append(Message("👤 Human", human_prompt)) | |
| st.session_state.history.append(Message("👨🏻⚖️ Ai", llm_response)) | |
| initialize_session_state() | |
| st.title("LegalEase Advisor Chatbot 🇮🇳") | |
| st.markdown( | |
| """ | |
| 👋 **Namaste! Welcome to LegalEase Advisor!** | |
| I'm here to assist you with your legal queries within the framework of Indian law. Whether you're navigating through specific legal issues or seeking general advice, I'm here to help. | |
| 📚 **How I Can Assist:** | |
| - Answer questions on various aspects of Indian law. | |
| - Guide you through legal processes relevant to India. | |
| - Provide information on your rights and responsibilities as per Indian legal standards. | |
| ⚖️ **Disclaimer:** | |
| While I can provide general information, it's essential to consult with a qualified Indian attorney for advice tailored to your specific situation. | |
| 🤖 **Getting Started:** | |
| Feel free to ask any legal question related to Indian law, using keywords like "property rights," "labor laws," or "family law." I'm here to assist you! | |
| Let's get started! How can I assist you today? | |
| """ | |
| ) | |
| if "history" not in st.session_state: | |
| st.session_state.history = [] | |
| if "generated" not in st.session_state: | |
| st.session_state.generated = [] | |
| if "past" not in st.session_state: | |
| st.session_state.past = [] | |
| chat_placeholder = st.empty() | |
| with chat_placeholder.container(): | |
| for i, chat in enumerate(st.session_state.history): | |
| if chat.origin == "👤 Human": | |
| message(chat.message, is_user=True, key=f"{i}_user") | |
| else: | |
| message(chat.message, key=f"{i}") | |
| def on_input_change(): | |
| user_input = st.session_state.user_input | |
| st.session_state.past.append(user_input) | |
| st.session_state.generated.append({"type": "normal", "data": f"The message from Bot\nWith new line\n{user_input}"}) | |
| st.session_state.history.append(Message("👤 Human", user_input)) | |
| st.session_state.history.append(Message("👨🏻⚖️ Ai", f"The message from Bot\nWith new line\n{user_input}")) | |
| def on_btn_click(): | |
| del st.session_state.past[:] | |
| del st.session_state.generated[:] | |
| del st.session_state.history[:] | |
| with st.container(): | |
| st.text_input("User Input:", on_change=on_input_change, key="user_input") | |
| st.button("Clear message", on_click=on_btn_click) | |