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| from dataclasses import dataclass | |
| from typing import Literal | |
| import streamlit as st | |
| 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 | |
| import streamlit.components.v1 as components | |
| HUGGINGFACEHUB_API_TOKEN = st.secrets['HUGGINGFACEHUB_API_TOKEN'] | |
| os.environ["HUGGINGFACEHUB_API_TOKEN"] = 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) | |
| 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. | |
| 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. | |
| Context: {context} | |
| Question: {question} | |
| Only return the helpful answer below and nothing else. | |
| Helpful answer: | |
| """ | |
| PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"]) | |
| chain_type_kwargs = {"prompt": PROMPT} | |
| retrieval_chain = RetrievalQA.from_chain_type(llm=model, | |
| chain_type="stuff", | |
| retriever=docsearch.as_retriever( | |
| search_kwargs={'k': 2}), | |
| return_source_documents=True, | |
| chain_type_kwargs=chain_type_kwargs) | |
| st.session_state.conversation = retrieval_chain | |
| def on_click_callback(): | |
| human_prompt = st.session_state.human_prompt | |
| response = st.session_state.conversation( | |
| human_prompt | |
| ) | |
| llm_response = response['result'] | |
| print(llm_response) | |
| st.session_state.history.append( | |
| Message("human", human_prompt) | |
| ) | |
| st.session_state.history.append( | |
| Message("ai", llm_response) | |
| ) | |
| initialize_session_state() | |
| st.title("👋 Welcome to LegalEase Advisor! \n I'm here to help you navigate through legal questions and concerns. Whether you're seeking information about a specific legal topic or need general advice, I'm at your service.") | |
| chat_placeholder = st.container() | |
| prompt_placeholder = st.form("chat-form") | |
| with chat_placeholder: | |
| for chat in st.session_state.history: | |
| st.markdown(f"From {chat.origin} : {chat.message}") | |
| with prompt_placeholder: | |
| st.markdown("**Chat**") | |
| cols = st.columns((6, 1)) | |
| cols[0].text_input( | |
| "Chat", | |
| value="Hello bot", | |
| label_visibility="collapsed", | |
| key="human_prompt", | |
| ) | |
| cols[1].form_submit_button( | |
| "Submit", | |
| type="primary", | |
| on_click=on_click_callback, | |
| ) | |