import streamlit as st
from streamlit_feedback import streamlit_feedback

import os
import pandas as pd
import base64
from io import BytesIO
import nest_asyncio

import chromadb
from llama_index.legacy import (
            VectorStoreIndex, 
            SimpleDirectoryReader,
            ServiceContext,
            Document
)

from llama_index.legacy.vector_stores.chroma import ChromaVectorStore
from llama_index.legacy.storage.storage_context import StorageContext
from llama_index.legacy.embeddings import HuggingFaceEmbedding
from llama_index.legacy.llms import OpenAI
from llama_index.legacy.memory import ChatMemoryBuffer

from vision_api import get_transcribed_text

nest_asyncio.apply()

# App title
st.set_page_config(page_title="💬 Open AI Chatbot")
openai_api = os.getenv("OPENAI_API_KEY")

# "./raw_documents/HI_Knowledge_Base.pdf"
input_files = ["./raw_documents/HI Chapter Summary Version 1.3.pdf",
               "./raw_documents/qna.txt"]
embedding_model = "BAAI/bge-small-en-v1.5"
persisted_vector_db = "./models/chroma_db"
fine_tuned_path = "local:models/fine-tuned-embeddings"
system_content = ("You are a helpful study assistant. "
                  "You do not respond as 'User' or pretend to be 'User'. "
                  "You only respond once as 'Assistant'."
)

data_df = pd.DataFrame(
    {
        "Completion": [30, 40, 100, 10],
    }
)
data_df.index = ["Chapter 1", "Chapter 2", "Chapter 3", "Chapter 4"]

# Replicate Credentials
with st.sidebar:
    st.title("💬 Open AI Chatbot")
    st.write("This chatbot is created using the GPT model from Open AI.")
    if openai_api:
        pass
    elif "OPENAI_API_KEY" in st.secrets:
        st.success("API key already provided!", icon="✅")
        openai_api = st.secrets["OPENAI_API_KEY"]
    else:
        openai_api = st.text_input("Enter OpenAI API token:", type="password")
        if not (openai_api.startswith("sk-") and len(openai_api)==51):
            st.warning("Please enter your credentials!", icon="⚠️")
        else:
            st.success("Proceed to entering your prompt message!", icon="👉")

    ### for streamlit purpose
    os.environ["OPENAI_API_KEY"] = openai_api

    st.subheader("Models and parameters")
    selected_model = st.sidebar.selectbox("Choose an OpenAI model", 
                                          ["gpt-3.5-turbo-1106", "gpt-4-1106-preview"], 
                                           key="selected_model")
    temperature = st.sidebar.slider("temperature", min_value=0.0, max_value=2.0, 
                                    value=0.0, step=0.01)
    st.data_editor(
        data_df,
        column_config={
            "Completion": st.column_config.ProgressColumn(
                            "Completion %",
                            help="Percentage of content covered",
                            format="%.1f%%",
                            min_value=0,
                            max_value=100,
            ),
        },
        hide_index=False,
    )

    st.markdown("📖 Reach out to SakiMilo to learn how to create this app!")

if "init" not in st.session_state.keys():
    st.session_state.init = {"warm_started": "No"}
    st.session_state.feedback = False

# Store LLM generated responses
if "messages" not in st.session_state.keys():
    st.session_state.messages = [{"role": "assistant", 
                                  "content": "How may I assist you today?",
                                  "type": "text"}]

if "feedback_key" not in st.session_state:
    st.session_state.feedback_key = 0

if "release_file" not in st.session_state:
    st.session_state.release_file = "false"

def clear_chat_history():
    st.session_state.messages = [{"role": "assistant", 
                                  "content": "How may I assist you today?",
                                  "type": "text"}]
    chat_engine = get_query_engine(input_files=input_files, 
                                   llm_model=selected_model, 
                                   temperature=temperature,
                                   embedding_model=embedding_model,
                                   fine_tuned_path=fine_tuned_path,
                                   system_content=system_content,
                                   persisted_path=persisted_vector_db)
    chat_engine.reset()

st.sidebar.button("Clear Chat History", on_click=clear_chat_history)
if st.sidebar.button("I want to submit a feedback!"):
    st.session_state.feedback = True
    st.session_state.feedback_key += 1  # overwrite feedback component

@st.cache_resource
def get_document_object(input_files):
    documents = SimpleDirectoryReader(input_files=input_files).load_data()
    document = Document(text="\n\n".join([doc.text for doc in documents]))
    return document

@st.cache_resource
def get_llm_object(selected_model, temperature):
    llm = OpenAI(model=selected_model, temperature=temperature)
    return llm

@st.cache_resource
def get_embedding_model(model_name, fine_tuned_path=None):
    if fine_tuned_path is None:
        print(f"loading from `{model_name}` from huggingface")
        embed_model = HuggingFaceEmbedding(model_name=model_name)
    else:
        print(f"loading from local `{fine_tuned_path}`")
        embed_model = fine_tuned_path
    return embed_model

@st.cache_resource
def get_query_engine(input_files, llm_model, temperature,
                     embedding_model, fine_tuned_path,
                     system_content, persisted_path):
    
    llm = get_llm_object(llm_model, temperature)
    embedded_model = get_embedding_model(
                        model_name=embedding_model, 
                        fine_tuned_path=fine_tuned_path
    )
    service_context = ServiceContext.from_defaults(
                        llm=llm, 
                        embed_model=embedded_model
    )
    
    if os.path.exists(persisted_path):
        print("loading from vector database - chroma")
        db = chromadb.PersistentClient(path=persisted_path)
        chroma_collection = db.get_or_create_collection("quickstart")
        vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
        storage_context = StorageContext.from_defaults(
                                vector_store=vector_store
        )
        index = VectorStoreIndex.from_vector_store(
            vector_store=vector_store, 
            service_context=service_context, 
            storage_context=storage_context
        )
    else:
        print("create in-memory vector store")
        document = get_document_object(input_files)
        index = VectorStoreIndex.from_documents(
                    [document], 
                    service_context=service_context
        )
    
    memory = ChatMemoryBuffer.from_defaults(token_limit=15000)
    chat_engine = index.as_chat_engine(
        chat_mode="context",
        memory=memory,
        system_prompt=system_content
    )

    return chat_engine

def generate_llm_response(prompt_input):
    chat_engine = get_query_engine(input_files=input_files, 
                                   llm_model=selected_model, 
                                   temperature=temperature,
                                   embedding_model=embedding_model,
                                   fine_tuned_path=fine_tuned_path,
                                   system_content=system_content,
                                   persisted_path=persisted_vector_db)
    
    # st.session_state.messages
    response = chat_engine.stream_chat(prompt_input)
    return response

def handle_feedback(user_response):
    st.toast("✔️ Feedback received!")
    st.session_state.feedback = False

def handle_image_upload():
    st.session_state.release_file = "true"

# Warm start
if st.session_state.init["warm_started"] == "No":
    clear_chat_history()
    st.session_state.init["warm_started"] = "Yes"

# Image upload option
with st.sidebar:
    image_file = st.file_uploader("Upload your image here...", 
                                  type=["png", "jpeg", "jpg"],
                                  on_change=handle_image_upload)

    if st.session_state.release_file == "true" and image_file:
        with st.spinner("Uploading..."):
            b64string = base64.b64encode(image_file.read()).decode('utf-8')
            message = {
                    "role": "user", 
                    "content": b64string,
                    "type": "image"}
            st.session_state.messages.append(message)

            transcribed_msg = get_transcribed_text(b64string)
            message = {
                    "role": "admin", 
                    "content": transcribed_msg,
                    "type": "text"}
            st.session_state.messages.append(message)
            st.session_state.release_file = "false"

# Display or clear chat messages
for message in st.session_state.messages:
    if message["role"] == "admin":
        continue
    with st.chat_message(message["role"]):
        if message["type"] == "text":
            st.write(message["content"])
        elif message["type"] == "image":
            img_io = BytesIO(base64.b64decode(message["content"].encode("utf-8")))
            st.image(img_io)

# User-provided prompt
if prompt := st.chat_input(disabled=not openai_api):
    client = OpenAI()
    st.session_state.messages.append({"role": "user", 
                                      "content": prompt, 
                                      "type": "text"})
    with st.chat_message("user"):
        st.write(prompt)

# Retrieve text prompt from image submission
if prompt is None and \
   st.session_state.messages[-1]["role"] == "admin":
    prompt = st.session_state.messages[-1]["content"]

# Generate a new response if last message is not from assistant
if st.session_state.messages[-1]["role"] != "assistant":
    with st.chat_message("assistant"):
        with st.spinner("Thinking..."):
            response = generate_llm_response(prompt)
            placeholder = st.empty()
            full_response = ""
            for token in response.response_gen:
                full_response += token
                placeholder.markdown(full_response)
            placeholder.markdown(full_response)

    message = {"role": "assistant", 
               "content": full_response,
               "type": "text"}
    st.session_state.messages.append(message)

# Trigger feedback
if st.session_state.feedback:
    result = streamlit_feedback(
                feedback_type="thumbs",
                optional_text_label="[Optional] Please provide an explanation",
                on_submit=handle_feedback,
                key=f"feedback_{st.session_state.feedback_key}"
    )