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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
@dataclass
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("Hello Custom CSS Chatbot 🤖")
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,
)
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