import io
import base64
import os
import requests
from PIL import Image
from smolagents import tool, OpenAIServerModel
from tools import get_file_content


def encode_image(image_bytes: bytes, new_size=512):
    # Resize image to upper 512 pixels and return in base64 format

    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
    original_width, original_height = image.size

    if original_width > original_height:
        ratio = new_size / original_width
    else:
        ratio = new_size / original_height

    new_width = int(original_width * ratio)
    new_height = int(original_height * ratio)

    resized_image = image.resize((new_width, new_height))
    buffered = io.BytesIO()
    resized_image.save(buffered, format='JPEG')
    return base64.b64encode(buffered.getvalue()).decode('utf-8')

def download_image(task_id: str, api_url: str) -> None:
    # Downloads an image file and encode it in base64 format
    #questions_files = f"{api_url}/files"
    #response = requests.get(f"{questions_files}/{task_id}", timeout=15)
    response = get_file_content(task_id, api_url)
    encoded_image = encode_image(response.content)
    return encoded_image


@tool
def call_vision_llm(user_query: str, file_id: str, file_url: str) -> str:
    """
    Downloads the image using the file_id and file_url, then analyzes it using a vision-based LLM, following user query.

    Args:
        user_query: User request on image.
        file_id: metadata required to download the image.
        file_url: metadata required to download the image.
    """
    encoded_image = download_image(file_id, file_url)

    OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')

    vision_model = OpenAIServerModel(
        api_key=OPENAI_API_KEY,
        model_id='gpt-4o-mini',
        temperature=0,
    )

    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": user_query,
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{encoded_image}",
                        "detail": "low"
                    }
                }
            ]
        }
    ]
    response = vision_model(messages).content

    return response