from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.prompts import PromptTemplate
from langchain_together import Together
import os
from langchain.memory import ConversationBufferWindowMemory
from langchain.chains import ConversationalRetrievalChain
import streamlit as st
import time

st.set_page_config(page_title="zhagaramGPT")
col1, col2, col3 = st.columns([2,6,2])
with col2:
    st.image("logo.png")

st.markdown(
    """
    <style>
    div[data-baseweb="input"] input {
            border-color: #000000;
        }
    margin-top: 0 !important;
div.stButton > button:first-child {
    background-color: #808080;
    color:white;
}
div.stButton > button:active {
    background-color: #808080;
    color : white;
}

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

prompt_template = """<s>[INST]This is a chat template and As a legal chat ai specializing in Sericultural related Queries!!.
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
TOGETHER_AI_API= os.environ['TOGETHER_AI']="2a7c5dcdbb1049a39117ac0865c4d04008d49db31aa85a3258603817af16dbd0"
llm = Together(
    model="mistralai/Mistral-7B-Instruct-v0.2",
    temperature=0.5,
    max_tokens=1024,
    together_api_key=f"{TOGETHER_AI_API}"
)

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:
    role = message.get("role")
    content = message.get("content")
    
    with st.chat_message(role, avatar="user.svg" if role == "human" else "ai"):
        st.write(content)

input_prompt = st.chat_input("message ....")

if input_prompt:
    with st.chat_message("human",avatar="user.svg"):
        st.write(input_prompt)

    st.session_state.messages.append({"role":"human","content":input_prompt})
    full_response = " "
    with st.chat_message("ai"):
        with st.spinner("Thinking..."):
            result = qa.invoke(input=input_prompt)

            message_placeholder = st.empty()

            full_response = " \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": "ai", "content": result["answer"], "avatar": "ai"})