# TODO:
#
# 1. Gradio session / multi-user thread
# 2. Function calling - https://platform.openai.com/docs/assistants/tools/function-calling
#    - get_stock_price - yfinance

# Reference:
#
# https://vimeo.com/990334325/56b552bc7a
# https://platform.openai.com/playground/assistants
# https://cookbook.openai.com/examples/assistants_api_overview_python
# https://platform.openai.com/docs/api-reference/assistants/createAssistant
# https://platform.openai.com/docs/assistants/tools

import gradio as gr
import yfinance as yf

import json, openai, os, time

from datetime import date
from openai import OpenAI
from utils import function_to_schema, show_json

client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))

assistant, thread = None, None

def today_tool(text: str) -> str:
    """Returns today's date. 
       Use this function for any questions related to knowing today's date. 
       There is no input. 
       This function always returns today's date."""
    return str(date.today())

def yfinance_download_tool(tickers: List[str], start: date, end: date) -> DataFrame:
    """Returns stock data for given tickers from a start date to an end date using yfinance download. 
       Use this function for any questions related to getting stock data. 
       The input should be the tickers as a list of strings, a start date, and an end date. 
       This function always returns a Pandas DataFrame."""
    return yf.download(tickers, start=start_date, end=end_date)

tools = {
    "today_tool": today_tool,
    "yf_download_tool": yf_download_tool,
}

def create_assistant(client):
    assistant = client.beta.assistants.create(
        name="Python Code Generator",
        instructions=(
                         "You are a Python programming language expert that "
                         "generates Pylint-compliant code and explains it. "
                         "Execute code when explicitly asked to."
                     ),
        model="gpt-4o",
        tools=[
            {"type": "code_interpreter"},
            {"type": "function", "function": function_to_schema(today_tool)},
            {"type": "function", "function": function_to_schema(yf_download_tool)},
        ],
    )
    
    #show_json("assistant", assistant)
    
    return assistant

def load_assistant(client):
    ASSISTANT_ID = "asst_TpZgBd2QYaxUxCwUy8J9m3Bq"
    
    assistant = client.beta.assistants.retrieve(ASSISTANT_ID)

    #show_json("assistant", assistant)
    
    return assistant

def create_thread(client):
    thread = client.beta.threads.create()
    
    #show_json("thread", thread)
    
    return thread

def create_message(client, thread, msg):        
    message = client.beta.threads.messages.create(
        role="user",
        thread_id=thread.id,
        content=msg,
    )
    
    #show_json("message", message)
    
    return message

def create_run(client, assistant, thread):
    run = client.beta.threads.runs.create(
        assistant_id=assistant.id,
        thread_id=thread.id,
    )
    
    #show_json("run", run)
    
    return run

def wait_on_run(client, thread, run):
    while run.status == "queued" or run.status == "in_progress":
        print("### " + run.status)
        run = client.beta.threads.runs.retrieve(
            thread_id=thread.id,
            run_id=run.id,
        )
    
        time.sleep(0.5)
    
    #show_json("run", run)
    
    return run

def get_run_steps(client, thread, run):
    run_steps = client.beta.threads.runs.steps.list(
        thread_id=thread.id,
        run_id=run.id,
        order="asc",
    )

    #show_json("run_steps", run_steps)
    
    return run_steps

def execute_tool_call(tool_call):
    name = tool_call.function.name
    args = json.loads(tool_call.function.arguments)
    
    return tools[name](**args)

def execute_tool_calls(run_steps):
    run_step_details = []

    tool_call_id = ""
    tool_call_result = ""
    
    for step in run_steps.data:
        step_details = step.step_details
        run_step_details.append(step_details)
        show_json("step_details", step_details)
        
        if hasattr(step_details, "tool_calls"):
            for tool_call in step_details.tool_calls:
                show_json("tool_call", tool_call)
                
                if hasattr(tool_call, "function"):
                    tool_call_id = tool_call.id
                    tool_call_result = execute_tool_call(tool_call)

    return tool_call_id, tool_call_result

def get_messages(client, thread):
    messages = client.beta.threads.messages.list(
        thread_id=thread.id
    )
    
    #show_json("messages", messages)
    
    return messages
                        
def extract_content_values(data):
    text_values, image_values = [], []
    
    for item in data.data:
        for content in item.content:
            if content.type == "text":
                text_value = content.text.value
                text_values.append(text_value)
            if content.type == "image_file":
                image_value = content.image_file.file_id
                image_values.append(image_value)
    
    return text_values, image_values

def chat(message, history):
    if not message:
        raise gr.Error("Message is required.")
    
    global client, assistant, thread     
    
    if assistant == None:
        assistant = create_assistant(client)
    
    if thread == None or len(history) == 0:
        thread = create_thread(client)
        
    create_message(client, thread, message)

    run = create_run(client, assistant, thread)
    run = wait_on_run(client, thread, run)

    run_steps = get_run_steps(client, thread, run)

    tool_call_id, tool_call_result = execute_tool_calls(run_steps)

    ### TODO
    if tool_call_result:
        print("### tool_call_id=" + tool_call_id)
        print("### tool_call_result=" + tool_call_result)

        # https://platform.openai.com/docs/api-reference/runs/submitToolOutputs
        run = client.beta.threads.runs.submit_tool_outputs(
            thread_id=thread.id,
            run_id=run.id,
            tool_outputs=[
                {
                  "tool_call_id": tool_call_id,
                  "output": tool_call_result
                }
            ]
        )
    
        run = wait_on_run(client, thread, run)
        run_steps = get_run_steps(client, thread, run)
        tool_call_id, tool_call_result = execute_tool_calls(run_steps)
    ###
    if tool_call_result:
        print("### tool_call_id=" + tool_call_id)
        print("### tool_call_result=" + tool_call_result)

        # https://platform.openai.com/docs/api-reference/runs/submitToolOutputs
        run = client.beta.threads.runs.submit_tool_outputs(
            thread_id=thread.id,
            run_id=run.id,
            tool_outputs=[
                {
                  "tool_call_id": tool_call_id,
                  "output": tool_call_result
                }
            ]
        )
    
        run = wait_on_run(client, thread, run)
        run_steps = get_run_steps(client, thread, run)
        tool_call_id, tool_call_result = execute_tool_calls(run_steps)
    ###
    
    messages = get_messages(client, thread)

    text_values, image_values = extract_content_values(messages)

    download_link = ""
    
    if len(image_values) > 0:
        download_link = f"<p>Download: https://platform.openai.com/storage/files/{image_values[0]}</p>"
    
    return f"{text_values[0]}{download_link}"

gr.ChatInterface(
        fn=chat,
        chatbot=gr.Chatbot(height=350),
        textbox=gr.Textbox(placeholder="Ask anything", container=False, scale=7),
        title="Python Code Generator",
        description="The assistant can generate, explain, fix, optimize, document, and test code. It can also execute code.",
        clear_btn="Clear",
        retry_btn=None,
        undo_btn=None,
        examples=[
                  ["Generate: Python code to fine-tune model meta-llama/Meta-Llama-3.1-8B on dataset gretelai/synthetic_text_to_sql using QLoRA"],
                  ["Explain: r\"^(?=.*[A-Z])(?=.*[a-z])(?=.*[0-9])(?=.*[\\W]).{8,}$\""],
                  ["Fix: x = [5, 2, 1, 3, 4]; print(x.sort())"],
                  ["Optimize: x = []; for i in range(0, 10000): x.append(i)"],
                  ["Execute: First 25 Fibbonaci numbers"],
                  ["Get today's date, then generate a chart using mock data, showing stock gain YTD for NVDA, MSFT, AAPL, and GOOG, x-axis is 'Day' and y-axis is 'YTD Gain %'"]
                 ],
        cache_examples=False,
    ).launch()