import os
import streamlit as st
from streamlit.logger import get_logger
from langchain.schema.messages import HumanMessage
from utils.mongo_utils import get_db_client
from utils.app_utils import create_memory_add_initial_message, get_random_name,  DEFAULT_NAMES_DF
from utils.memory_utils import clear_memory, push_convo2db
from utils.chain_utils import get_chain, custom_chain_predict
from app_config import ISSUES, SOURCES, source2label, issue2label

logger = get_logger(__name__)
openai_api_key = os.environ['OPENAI_API_KEY'] 
temperature = 0.8
# username = "barb-chase" #"ivnban-ctl"

if "sent_messages" not in st.session_state:
    st.session_state['sent_messages'] = 0
if "issue" not in st.session_state:
    st.session_state['issue'] = ISSUES[0]
if 'previous_source' not in st.session_state:
    st.session_state['previous_source'] = SOURCES[0]
if 'db_client' not in st.session_state:
    st.session_state["db_client"] = get_db_client()
if 'texter_name' not in st.session_state:
    st.session_state["texter_name"] = get_random_name(names_df=DEFAULT_NAMES_DF)
    logger.info(f"texter name is {st.session_state['texter_name']}")

memories = {'memory':{"issue": st.session_state['issue'], "source": st.session_state['previous_source']}}

with st.sidebar:
    username = st.text_input("Username", value='Barbara', max_chars=30)
    if 'counselor_name' not in st.session_state:
        st.session_state["counselor_name"] = username #get_random_name(names_df=DEFAULT_NAMES_DF)
    # temperature = st.slider("Temperature", 0., 1., value=0.8, step=0.1)
    issue = st.selectbox("Select a Scenario", ISSUES, index=0, format_func=issue2label,
                            on_change=clear_memory, kwargs={"memories":memories, "username":username, "language":"English"}
                        )
    supported_languages = ['en', "es"] if issue == "Anxiety" else ['en']
    language = st.selectbox("Select a Language", supported_languages, index=0,
                            format_func=lambda x: "English" if x=="en" else "Spanish",
                            on_change=clear_memory, kwargs={"memories":memories, "username":username, "language":"English"}
                        )
                            
    source = st.selectbox("Select a source Model A", SOURCES, index=0,
                          format_func=source2label, 
                        )
    st.markdown(f"### Previous Prompt Count: :red[**{st.session_state['sent_messages']}**]")

changed_source = any([
    st.session_state['previous_source'] != source,
    st.session_state['issue'] != issue,
    st.session_state['counselor_name'] != username,
])
if changed_source:
    st.session_state["counselor_name"] = username #get_random_name(names_df=DEFAULT_NAMES_DF)
    st.session_state["texter_name"] = get_random_name(names_df=DEFAULT_NAMES_DF)
    st.session_state['previous_source'] = source
    st.session_state['issue'] = issue
    st.session_state['sent_messages'] = 0
create_memory_add_initial_message(memories,
                                  issue, 
                                  language, 
                                  changed_source=changed_source, 
                                  counselor_name=st.session_state["counselor_name"], 
                                  texter_name=st.session_state["texter_name"])
st.session_state['previous_source'] = source
memoryA = st.session_state[list(memories.keys())[0]]
# issue only without "." marker for model compatibility
llm_chain, stopper = get_chain(issue.split(".")[0], language, source, memoryA, temperature, texter_name=st.session_state["texter_name"])

st.title("💬 Simulator") 

for msg in memoryA.buffer_as_messages:
    role = "user" if type(msg) == HumanMessage else "assistant"
    st.chat_message(role).write(msg.content)

if prompt := st.chat_input():
    st.session_state['sent_messages'] += 1
    if 'convo_id' not in st.session_state:
        push_convo2db(memories, username, language)

    st.chat_message("user").write(prompt)
    responses = custom_chain_predict(llm_chain, prompt, stopper)
    # responses = llm_chain.predict(input=prompt, stop=stopper)
    # response = update_memory_completion(prompt, st.session_state["memory"], OA_engine, temperature)
    for response in responses:
        st.chat_message("assistant").write(response)