import openai
import streamlit as st
from langchain_core.messages import AIMessage, ChatMessage, HumanMessage
from langchain_core.tracers.context import collect_runs
from langsmith import Client
from streamlit_feedback import streamlit_feedback

from rag.runnable import get_runnable
from utils.error_message_template import ERROR_MESSAGE

# Streamlit page configuration
st.set_page_config(
    page_title="ELLA AI Assistant",
    page_icon="💬",
    layout="centered",
    initial_sidebar_state="collapsed",
)

# Streamlit CSS configuration
with open("styles/styles.css") as css:
    st.markdown(f"<style>{css.read()}</style>", unsafe_allow_html=True)


# Get runnable and memory
@st.cache_resource(show_spinner=False)
def get_runnable_and_memory():
    try:
        return get_runnable(model="gpt-4-turbo", temperature=0)
    except Exception:
        st.warning(ERROR_MESSAGE, icon="🙁")
        st.stop()


chain, memory = get_runnable_and_memory()


# Set up session state variables
# Clean memory (important! to clean the memory at the end of each session)
if "history" not in st.session_state:
    st.session_state["history"] = []
    memory.clear()

if "messages" not in st.session_state:
    st.session_state["messages"] = []

if "selected_location" not in st.session_state:
    st.session_state["selected_location"] = None

if "disable_chat_input" not in st.session_state:
    st.session_state["disable_chat_input"] = True


# Welcome message and Selectbox for location preferences
def welcome_message():
    st.markdown(
        "Hello there! 👋 Need help finding the right service or practitioner? Let our AI assistant give you a hand.\n\n"
        "To get started, please select your preferred location and share details about your symptoms or needs. "
    )


def on_change_location():
    st.session_state["disable_chat_input"] = (
        False if st.session_state["selected_location"] else True
    )


with st.container():
    welcome_message()
    location = st.radio(
        "**Our Locations**:",
        (
            "Cordova Bay - Victoria",
            "James Bay - Victoria",
            "Commercial Drive - Vancouver",
        ),
        index=None,
        label_visibility="visible",
        key="selected_location",
        on_change=on_change_location,
    )
    st.markdown("<br>", unsafe_allow_html=True)

# Get user input only if a location is selected
user_input = st.chat_input(
    "Ask ELLA...", disabled=st.session_state["disable_chat_input"]
)

if user_input:
    st.session_state["messages"].append(ChatMessage(role="user", content=user_input))
    prompt = f"{user_input}\nLocation preference: {st.session_state.selected_location}."

else:
    prompt = None

# Display previous messages
user_avatar = "images/user.png"
ai_avatar = "images/tall-tree-logo.png"
for msg in st.session_state["messages"]:
    avatar = user_avatar if msg.role == "user" else ai_avatar
    with st.chat_message(msg.role, avatar=avatar):
        st.markdown(msg.content)

# Chat interface
if prompt:
    # Add all previous messages to memory
    for human, ai in st.session_state["history"]:
        memory.chat_memory.add_user_message(HumanMessage(content=human))
        memory.chat_memory.add_ai_message(AIMessage(content=ai))

    # render the assistant's response
    with st.chat_message("assistant", avatar=ai_avatar):
        message_placeholder = st.empty()

        try:
            partial_message = ""
            # Collect runs for feedback using Langsmith.
            with st.spinner(" "), collect_runs() as cb:
                for chunk in chain.stream({"message": prompt}):
                    partial_message += chunk
                    message_placeholder.markdown(partial_message + "|")
                st.session_state.run_id = cb.traced_runs[0].id
            message_placeholder.markdown(partial_message)
        except openai.BadRequestError:
            st.warning(ERROR_MESSAGE, icon="🙁")
            st.stop()
        except Exception:
            st.warning(ERROR_MESSAGE, icon="🙁")
            st.stop()

        # Add the full response to the history
        st.session_state["history"].append((prompt, partial_message))

        # Add AI message to memory after the response is generated
        memory.chat_memory.add_ai_message(AIMessage(content=partial_message))

        # Add the full response to the message history
        st.session_state["messages"].append(
            ChatMessage(role="assistant", content=partial_message)
        )


# Feedback system using streamlit-feedback and Langsmith

# Langsmith client for the feedback system
ls_client = Client()

# Feedback option
feedback_option = "thumbs"

if st.session_state.get("run_id"):
    run_id = st.session_state.run_id
    feedback = streamlit_feedback(
        feedback_type=feedback_option,
        optional_text_label="[Optional] Please provide an explanation",
        key=f"feedback_{run_id}",
    )
    score_mappings = {
        "thumbs": {"👍": 1, "👎": 0},
        "faces": {"😀": 1, "🙂": 0.75, "😐": 0.5, "🙁": 0.25, "😞": 0},
    }

    # Get the score mapping based on the selected feedback option
    scores = score_mappings[feedback_option]

    if feedback:
        # Get the score from the selected feedback option's score mapping
        score = scores.get(feedback["score"])

        if score is not None:
            # Formulate feedback type string incorporating the feedback option
            # and score value
            feedback_type_str = f"{feedback_option} {feedback['score']}"

            # Record the feedback with the formulated feedback type string
            feedback_record = ls_client.create_feedback(
                run_id,
                feedback_type_str,
                score=score,
                comment=feedback.get("text"),
            )
            st.session_state.feedback = {
                "feedback_id": str(feedback_record.id),
                "score": score,
            }
        else:
            st.warning("Invalid feedback score.")