from langchain_community.document_loaders import PyPDFLoader,DirectoryLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
import os
from langchain.prompts import PromptTemplate
from langchain_together import Together
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import openai
from langchain.memory import ConversationBufferWindowMemory
from langchain.chains import ConversationalRetrievalChain
import streamlit as st
import time




from openai import OpenAI
api_key = os.getenv("OPENAI_API_KEY")
client = OpenAI(api_key=api_key)

from langchain.embeddings.openai import OpenAIEmbeddings
embeddings=OpenAIEmbeddings()

#embeddings = HuggingFaceEmbeddings(model_name="nomic-ai/nomic-embed-text-v1",model_kwargs={"trust_remote_code":True,"revision":"289f532e14dbbbd5a04753fa58739e9ba766f3c7"})
#vectordb = Chroma.from_documents(texts, embedding=embeddings, persist_directory="./data")
#db_retriever =vectordb.as_retriever(search_type="similarity",search_kwargs={'k':4})
    #https://s3.ap-south-1.amazonaws.com/makerobosfastcdn/cms-assets/Legal_AI_Chatbot.png

db = FAISS.load_local("vector-db", embeddings, allow_dangerous_deserialization=True)
db_retriever = db.as_retriever(search_type="similarity",search_kwargs={"k": 4})


st.set_page_config(page_title="Qanoon-Bot")
col1, col2, col3 = st.columns([1,4,1])
with col2:
    st.image("qanoonbot.png")

st.markdown(
    """
    <style>
div.stButton > button:first-child {
    background-color: #ffd0d0;
}
div.stButton > button:active {
    background-color: #ff6262;
}
   div[data-testid="stStatusWidget"] div button {
        display: none;
        }
    
    .reportview-container {
            margin-top: -2em;
        }
        #MainMenu {visibility: hidden;}
        .stDeployButton {display:none;}
        footer {visibility: hidden;}
        #stDecoration {display:none;}
    button[title="View fullscreen"]{
    visibility: hidden;}
        </style>
""",
    unsafe_allow_html=True,
)

def reset_conversation():
  st.session_state.messages = []
  st.session_state.memory.clear()

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) 

##embeddings = HuggingFaceEmbeddings(model_name="nomic-ai/nomic-embed-text-v1",model_kwargs={"trust_remote_code":True,"revision":"289f532e14dbbbd5a04753fa58739e9ba766f3c7"})
#db=FAISS.load_local("/content/ipc_vector_db", embeddings, allow_dangerous_deserialization=True)

prompt_template = """<s>[INST]This is a chat template and As a legal chat bot specializing in pakistan Property laws and rights queries and , your primary objective is to provide accurate and concise information based on the user's questions. offer relevant context from the knowledge base while avoiding unnecessary details. Your responses will be brief, to the point, and in compliance with the established format. If a question falls outside the given context, you will refrain from utilizing the chat history and instead rely on your own knowledge base to generate an appropriate response. You will prioritize the user's query and refrain from posing additional questions. The aim is to deliver professional, precise, and contextually relevant information pertaining to the Indian Penal Code.
CONTEXT: {context}
CHAT HISTORY: {chat_history}
QUESTION: {question}
ANSWER:
</s>[INST]
"""

prompt = PromptTemplate(template=prompt_template,
                        input_variables=['context', 'question', 'chat_history'])

# You can also use other LLMs options from https://python.langchain.com/docs/integrations/llms. Here I have used TogetherAI API


llm=ChatOpenAI(temperature=0.2,model_name='gpt-4-turbo')
qa = ConversationalRetrievalChain.from_llm(
    llm=llm,
    memory=st.session_state.memory,
    retriever=db_retriever,
    combine_docs_chain_kwargs={'prompt': prompt}
)

for message in st.session_state.messages:
    with st.chat_message(message.get("role")):
        st.write(message.get("content"))

input_prompt = st.chat_input("Say something")

if input_prompt:
    with st.chat_message("user"):
        st.write(input_prompt)

    st.session_state.messages.append({"role":"user","content":input_prompt})

    with st.chat_message("assistant"):
        with st.status("Thinking 💡...",expanded=True):
            result = qa.invoke(input=input_prompt)

            message_placeholder = st.empty()

            full_response = "**_Note: Information provided by Qanoon-Bot may be inaccurate. ** \n\n\n"
        for chunk in result["answer"]:
            full_response+=chunk
            time.sleep(0.02)
            
            message_placeholder.markdown(full_response+" ▌")
        st.button('Reset All Chat 🗑️', on_click=reset_conversation)

    st.session_state.messages.append({"role":"assistant","content":result["answer"]})