import os
import time
import streamlit as st
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferWindowMemory
from langchain.chains import ConversationalRetrievalChain
from langchain_together import Together
from footer import footer  # Ensure this module is present in the working directory

# Set Streamlit configuration
st.set_page_config(page_title="AI Legal App", layout="centered")

# Display a logo or banner (replace with a local image or URL)
col1, col2, col3 = st.columns([1, 30, 1])
with col2:
    st.image("https://github.com/Nike-one/BharatLAW/blob/master/images/banner.png?raw=true", use_column_width=True)

def hide_hamburger_menu():
    st.markdown("""
        <style>
            #MainMenu {visibility: hidden;}
            footer {visibility: hidden;}
        </style>
        """, unsafe_allow_html=True)

hide_hamburger_menu()

# Initialize session state
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)

@st.cache_resource
def load_embeddings():
    """Load and cache the embeddings model."""
    return HuggingFaceEmbeddings(model_name="law-ai/InLegalBERT")

embeddings = load_embeddings()
db = FAISS.load_local("ipc_embed_db", embeddings, allow_dangerous_deserialization=True)
db_retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 3})

prompt_template = """
<s>[INST]
As a legal chatbot specializing in Indian law, your responses must be concise and accurate:
- Provide bullet points summarizing key legal aspects.
- Avoid assumptions or overly specific advice unless requested.
- Clarify any common misconceptions.
- Keep responses aligned with general legal principles.
CONTEXT: {context}
CHAT HISTORY: {chat_history}
QUESTION: {question}
ANSWER:
</s>[INST]
"""

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

api_key = os.getenv('TOGETHER_API_KEY')
llm = Together(model="mistralai/Mixtral-8x22B-Instruct-v0.1", temperature=0.5, max_tokens=1024, together_api_key=api_key)

qa = ConversationalRetrievalChain.from_llm(llm=llm, memory=st.session_state.memory, retriever=db_retriever, combine_docs_chain_kwargs={'prompt': prompt})

def extract_answer(full_response):
    """Extracts the assistant's answer from the response."""
    return full_response.strip()

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

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

input_prompt = st.chat_input("Ask your legal query...")
if input_prompt:
    with st.chat_message("user"):
        st.markdown(f"**You:** {input_prompt}")

    st.session_state.messages.append({"role": "user", "content": input_prompt})
    with st.chat_message("assistant"):
        with st.spinner("Analyzing..."):
            result = qa.invoke(input=input_prompt)
            message_placeholder = st.empty()
            answer = extract_answer(result["answer"])

            # Simulated typing effect
            response = ""
            for char in answer:
                response += char
                time.sleep(0.02)
                message_placeholder.markdown(response + " |", unsafe_allow_html=True)

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

        if st.button('🗑️ Reset Chat', on_click=reset_conversation):
            st.experimental_rerun()

footer()