import streamlit as st
from langchain.chains import ConversationChain
from langchain.chains.conversation.memory import ConversationEntityMemory
from langchain.chains.conversation.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE
from model import get_llm

st.set_page_config(page_title='Bihar Now & Then', layout='wide')

if "generated" not in st.session_state:
    st.session_state["generated"] = []
if "past" not in st.session_state:
    st.session_state["past"] = []
if "input" not in st.session_state:
    st.session_state["input"] = ""
if "stored_session" not in st.session_state:
    st.session_state["stored_session"] = []

def get_text():

    input_text = st.text_input("You: ", st.session_state["input"], key="input",
                            placeholder="Ask me anything related to Bihar ...", 
                            label_visibility='hidden')
    return input_text

# Define function to start a new chat
def new_chat():
    """
    Clears session state and starts a new chat.
    """
    save = []
    for i in range(len(st.session_state['generated'])-1, -1, -1):
        save.append("User:" + st.session_state["past"][i])
        save.append("Bot:" + st.session_state["generated"][i])        
    st.session_state["stored_session"].append(save)
    st.session_state["generated"] = []
    st.session_state["past"] = []
    st.session_state["input"] = ""
    st.session_state.entity_memory.entity_store = {}
    st.session_state.entity_memory.buffer.clear()

# Set up sidebar with various options
with st.sidebar.expander("🛠️ ", expanded=False):
    # Option to preview memory store
    if st.checkbox("Preview memory store"):
        with st.expander("Memory-Store", expanded=False):
            st.session_state.entity_memory.store
    # Option to preview memory buffer
    if st.checkbox("Preview memory buffer"):
        with st.expander("Bufffer-Store", expanded=False):
            st.session_state.entity_memory.buffer
    MODEL = st.selectbox(label='Model', options=['gpt-3.5-turbo','text-davinci-003','text-davinci-002','code-davinci-002'])
    K = st.number_input(' (#)Summary of prompts to consider',min_value=3,max_value=1000)

# Set up the Streamlit app layout

st.subheader(" Powered by 🦜 LangChain + 🤗 HuggingFace + Streamlit")

model_name = "bert-large-uncased"
pinecone_index = "bert-large-uncased"
llm = "databricks/dolly-v2-3b"
llm_chain, docsearch = get_llm(model_name,pinecone_index,llm)
# Create a ConversationEntityMemory object if not already created
if 'entity_memory' not in st.session_state:
        st.session_state.entity_memory = ConversationEntityMemory(llm=llm, k=K )
    
    # Create the ConversationChain object with the specified configuration
Conversation = ConversationChain(
        llm=llm, 
        prompt=ENTITY_MEMORY_CONVERSATION_TEMPLATE,
        memory=st.session_state.entity_memory
    )  


st.sidebar.button("New Chat", on_click = new_chat, type='primary')

user_input = get_text()

if user_input:
    output = Conversation.run(input=user_input)  
    st.session_state.past.append(user_input)  
    st.session_state.generated.append(output)  

# Allow to download as well
download_str = []

with st.expander("Conversation", expanded=True):
    for i in range(len(st.session_state['generated'])-1, -1, -1):
        st.info(st.session_state["past"][i])
        st.success(st.session_state["generated"][i])
        download_str.append(st.session_state["past"][i])
        download_str.append(st.session_state["generated"][i])
    
    # Can throw error - requires fix
    download_str = '\n'.join(download_str)
    if download_str:
        st.download_button('Download',download_str)

# Display stored conversation sessions in the sidebar
for i, sublist in enumerate(st.session_state.stored_session):
        with st.sidebar.expander(label= f"Conversation-Session:{i}"):
            st.write(sublist)

# Allow the user to clear all stored conversation sessions
if st.session_state.stored_session:   
    if st.sidebar.checkbox("Clear-all"):
        del st.session_state.stored_session