from dora import DoraStatus
import os
import pyarrow as pa


import requests

import os

import base64
import requests
from io import BytesIO
import numpy as np
import cv2


def encode_numpy_image(np_image):
    # Convert the NumPy array to a PIL Image
    cv2.resize(np_image, (512, 512))
    _, buffer = cv2.imencode(
        ".png", np_image
    )  # You can change '.png' to another format if needed

    # Convert the buffer to a byte stream
    byte_stream = BytesIO(buffer)

    # Encode the byte stream to base64
    base64_encoded_image = base64.b64encode(byte_stream.getvalue()).decode("utf-8")
    return base64_encoded_image


CAMERA_WIDTH = 640
CAMERA_HEIGHT = 480

API_KEY = os.getenv("OPENAI_API_KEY")


MESSAGE_SENDER_TEMPLATE = """
You control a robot. Don't get too close to objects.

{user_message}

Respond with only one of the following actions:
- FORWARD
- BACKWARD
- TURN_RIGHT
- TURN_LEFT
- NOD_YES
- NOD_NO
- STOP

You're last 5 actions where: 
{actions}
"""


import time


def understand_image(image, user_message, actions):
    # Getting the base64 string
    base64_image = encode_numpy_image(image)
    headers = {"Content-Type": "application/json", "Authorization": f"Bearer {API_KEY}"}

    now = time.time()
    payload = {
        "model": "gpt-4-vision-preview",
        "messages": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": MESSAGE_SENDER_TEMPLATE.format(
                            user_message="\n".join(user_message),
                            actions="\n".join(actions[:-5]),
                        ),
                    },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{base64_image}",
                            "detail": "low",
                        },
                    },
                ],
            }
        ],
        "max_tokens": 50,
    }

    response = requests.post(
        "https://api.openai.com/v1/chat/completions", headers=headers, json=payload
    )

    print("resp:", time.time() - now)
    return response.json()["choices"][0]["message"]["content"]


class Operator:
    def __init__(self):
        self.actions = []
        self.instruction = []

    def on_event(
        self,
        dora_event,
        send_output,
    ) -> DoraStatus:
        if dora_event["type"] == "INPUT":
            if dora_event["id"] == "image":
                image = (
                    dora_event["value"]
                    .to_numpy()
                    .reshape((CAMERA_HEIGHT, CAMERA_WIDTH, 3))
                    .copy()
                )
                output = understand_image(image, self.instruction, self.actions)
                self.actions.append(output)
                print("response: ", output, flush=True)

                send_output(
                    "assistant_message",
                    pa.array([f"{output}"]),
                    dora_event["metadata"],
                )
            elif dora_event["id"] == "instruction":
                self.instruction.append(dora_event["value"][0].as_py())
                print("instructions: ", self.instruction, flush=True)
        return DoraStatus.CONTINUE


if __name__ == "__main__":
    op = Operator()

    # Path to the current file
    current_file_path = __file__

    # Directory of the current file
    current_directory = os.path.dirname(current_file_path)

    path = current_directory + "/test_image.jpg"

    op.on_event(
        {
            "type": "INPUT",
            "id": "code_modifier",
            "value": pa.array(
                [
                    {
                        "path": path,
                        "user_message": "change planning to make gimbal follow bounding box ",
                    },
                ]
            ),
            "metadata": [],
        },
        print,
    )