import os
import time
import openai
import gradio as gr
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chat_models import ChatOpenAI
from langchain.schema import AIMessage, HumanMessage, SystemMessage

# Sets up OpenAI embeddings model
embeddings = OpenAIEmbeddings()

# Loads database from persisted directory
db_directory = "chroma_db"
db = Chroma(persist_directory=db_directory, embedding_function=embeddings)

# Retrieves relevant documents based on a similarity search 
retriever = db.as_retriever(search_type='similarity', search_kwargs={"k":3})

with open('system_prompt.txt', 'r') as file:
    ORIG_SYSTEM_MESSAGE_PROMPT = file.read()

openai.api_key = os.getenv("OPENAI_API_KEY")

chat = ChatOpenAI(model_name="gpt-4",temperature=0)

# Here is the langchain
def predict(history, input):
    context = retriever.get_relevant_documents(input)
    print(context) #For debugging
    history_langchain_format = []
    history_langchain_format.append(SystemMessage(content=f"{ORIG_SYSTEM_MESSAGE_PROMPT}"))
    for human, ai in history:
        history_langchain_format.append(HumanMessage(content=human))
        history_langchain_format.append(AIMessage(content=ai))
    history_langchain_format.append(HumanMessage(content=input))
    history_langchain_format.append(SystemMessage(content=f"Here are some stories the user may like: {context}"))
    
    gpt_response = chat(history_langchain_format)
    
    # Extract pairs of HumanMessage and AIMessage
    pairs = []
    for i in range(len(history_langchain_format)):
        if isinstance(history_langchain_format[i], HumanMessage) and (i+1 < len(history_langchain_format)) and isinstance(history_langchain_format[i+1], AIMessage):
            pairs.append((history_langchain_format[i].content, history_langchain_format[i+1].content))
    
    # Add new AI response to the pairs for subsequent interactions
    pairs.append((input, gpt_response.content))
    
    return pairs

# Function to handle user message
def user(user_message, chatbot_history):
    return "", chatbot_history + [[user_message, ""]]

# Function to handle AI's response
def bot(chatbot_history):
    user_message = chatbot_history[-1][0] #This line is because we cleared the user_message previously in the user function above
    # Call the predict function to get the AI's response
    pairs = predict(chatbot_history, user_message)
    _, ai_response = pairs[-1]  # Get the latest response

    response_in_progress = ""
    for character in ai_response:
        response_in_progress += character
        chatbot_history[-1][1] = response_in_progress
        time.sleep(0.05)
        yield chatbot_history

# This is a function to do something with the voted information
def vote(data: gr.LikeData):
    if data.liked:
        print("You upvoted this response: " + data.value)
    else:
        print("You downvoted this response: " + data.value)
        with open("logs.txt", "a") as text_file:
          print(f"Disliked content: {data.value}", file=text_file)

# The Gradio App interface
with gr.Blocks() as demo:
    gr.Markdown("""<h1><center>Technocomplex Bot</center></h1>""")
    gr.Markdown("""<h3><center>This is a demo for Our Complex Relationships with Technology course, Duke, 2023</center></h3>""")
    chatbot = gr.Chatbot(label="Technocomplex Bot")
    textbox = gr.Textbox(label="Start chatting here and click 'Enter' to submit")
    clear = gr.Button("Clear")
    
    # Chain user and bot functions with `.then()`
    textbox.submit(user, [textbox, chatbot], [textbox, chatbot], queue=False).then(
        bot, chatbot, chatbot,
    )
    clear.click(lambda: None, None, chatbot, queue=False)
    chatbot.like(vote, None, None)

# Enable queuing
demo.queue()
demo.launch(debug=True, share=True)