# utils/image_handling.py

import aiohttp
import base64

import globals
from utils.core import switch_model
from utils.core import miku_vectorstore


async def download_and_encode_image(url):
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as resp:
            if resp.status != 200:
                return None
            img_bytes = await resp.read()
            return base64.b64encode(img_bytes).decode('utf-8')

async def analyze_image_with_qwen(base64_img):
    await switch_model("moondream")

    payload = {
        "model": "moondream",
        "prompt": "Describe this image in detail.",
        "images": [base64_img],
        "stream": False
    }

    headers = {"Content-Type": "application/json"}

    async with aiohttp.ClientSession() as session:
        async with session.post(f"{globals.OLLAMA_URL}/api/generate", json=payload, headers=headers) as response:
            if response.status == 200:
                data = await response.json()
                return data.get("response", "No description.")
            else:
                return f"Error: {response.status}"

async def rephrase_as_miku(qwen_output, user_prompt):
    await switch_model(globals.OLLAMA_MODEL)  # likely llama3

    with open("miku_prompt.txt", "r", encoding="utf-8") as f:
        system_prompt = f.read()

    relevant_docs_lore = miku_vectorstore.similarity_search(qwen_output, k=3)
    context = "\n\n".join([doc.page_content for doc in relevant_docs_lore])

    full_prompt = (
        f"{context}\n\n"
        f"The user asked: \"{user_prompt}\"\n"
        f"The image contains: \"{qwen_output}\"\n\n"
        f"Respond like Miku: cheerful, helpful, and opinionated when asked.\n\n"
        f"Miku is currently feeling: {globals.CURRENT_MOOD}\n Please respond in a way that reflects this emotional tone.\n\n"
        f"Miku:"
    )

    payload = {
        "model": globals.OLLAMA_MODEL,
        "prompt": full_prompt,
        "system": system_prompt,
        "stream": False
    }

    headers = {"Content-Type": "application/json"}

    async with aiohttp.ClientSession() as session:
        async with session.post(f"{globals.OLLAMA_URL}/api/generate", json=payload, headers=headers) as response:
            if response.status == 200:
                data = await response.json()
                return data.get("response", "No response.")
            else:
                return f"Error: {response.status}"