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

# 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 pandas as pd
import yfinance as yf

import json, openai, os, time

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

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

assistant_id = "asst_ypbcWnilAd60bc2DQ8haDL5P"

assistant, thread = None, None

def today_tool() -> 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 yf_download_tool(tickers: List[str], start_date: date, end_date: date) -> pd.DataFrame:
    """Returns historical stock data for given tickers from a start date to an end date 
       using the yfinance library download function. 
       Use this function for any questions related to getting historical 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 = 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,
        parallel_tool_calls=False,
    )
    
    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_ids = []
    tool_call_results = []
    
    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_ids.append(tool_call.id)
                    tool_call_results.append(execute_tool_call(tool_call))

    return tool_call_ids, tool_call_results

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 generate_tool_outputs(tool_call_ids, tool_call_results):
    tool_outputs = []
    
    for tool_call_id, tool_call_result in zip(tool_call_ids, tool_call_results):
        tool_output = {}
        
        try:
            tool_output = {
                "tool_call_id": tool_call_id,
                "output": tool_call_result.to_json()
            }

            print("###")
            print(tool_call_id)
            print(tool_call_result.to_json())
            print("###")
        except AttributeError:
            tool_output = {
                "tool_call_id": tool_call_id,
                "output": tool_call_result
            }

            print("###")
            print(tool_call_id)
            print(tool_call_result)
            print("###")
            
        tool_outputs.append(tool_output)
    
    return tool_outputs
###

def chat(message, history):
    if not message:
        raise gr.Error("Message is required.")
    
    global client, assistant, thread     
    
    if assistant == None:
        assistant = load_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_ids, tool_call_results = execute_tool_calls(run_steps)

    ### TODO
    print("###")
    print(len(tool_call_ids))
    print(tool_call_ids)
    print(tool_call_ids[0])
    print(tool_call_results)
    print(tool_call_results[0])
    print("###")
    
    if tool_call_ids[0]:
        # 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=generate_tool_outputs(tool_call_ids, tool_call_results)
            tool_outputs=[
                {
                    "tool_call_id": tool_call_ids[0],
                    "output": tool_call_results[0]
                }
            ]
        )
    
        run = wait_on_run(client, thread, run)
        run_steps = get_run_steps(client, thread, run)
    ###
    tool_call_ids, tool_call_results = execute_tool_calls(run_steps)

    print("###")
    print(len(tool_call_ids))
    print(tool_call_ids)
    print(tool_call_ids[1])
    print(tool_call_results)
    print(tool_call_results[1])
    print("###")
    
    if tool_call_ids[1]:
        # 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=generate_tool_outputs(tool_call_ids, tool_call_results)
            tool_outputs=[
                {
                    "tool_call_id": tool_call_ids[1],
                    "output": tool_call_results[1].to_json()
                }
            ]
        )
    
        run = wait_on_run(client, thread, run)
        run_steps = get_run_steps(client, thread, run)    
    ###
    
    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}"
    return f"{'<hr>'.join(reversed(text_values))}{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 <b>execute code</b>. "
            "It has access to <b>today tool</b> (to get current date) and "
            "<b>yfinance download tool</b> (to get stock data)."
        ),
        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"],
                  ["Create a plot showing stock gain QTD for NVDA and MSFT, x-axis is 'Day' and y-axis is 'QTD Gain %'"]
                 ],
        cache_examples=False,
    ).launch()