import os
import requests
import base64
from openai import OpenAI

def image_caption(image_url: str) -> str:
    """
    Describe an image or answer a specific question about it.
    If OPENAI_API_KEY is set, uses GPT-4V (or configured VLM_MODEL_ID) for visual QA.
    Otherwise falls back to HuggingFace BLIP captioning.
    """
    # 检查是否使用 OpenAI VLM
    openai_api_key = os.getenv("OPENAI_API_KEY")
    vlm_model = os.getenv("VLM_MODEL_ID", "gpt-4-vision-preview")
    use_openai = openai_api_key and vlm_model.startswith("gpt-4")

    if use_openai:
        try:
            # 下载图片并转为 base64
            headers = {"Authorization": f"Bearer {os.getenv('HF_TOKEN')}"} if "huggingface.co" in image_url else {}
            img_response = requests.get(image_url, headers=headers, timeout=15)
            img_response.raise_for_status()
            img_b64 = base64.b64encode(img_response.content).decode()
            
            client = OpenAI(api_key=openai_api_key)
            response = client.chat.completions.create(
                model=vlm_model,
                messages=[
                    {
                        "role": "user",
                        "content": [
                            {"type": "text", "text": "Describe the chess position and provide the best next move for black (algebraic notation). If it's not chess, just describe the image."},
                            {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img_b64}"}}
                        ]
                    }
                ],
                max_tokens=256,
                temperature=0.0,
            )
            answer = response.choices[0].message.content.strip()
            return answer if answer else "No answer from VLM."
        except Exception as e:
            return f"VLM error: {str(e)}"
    else:
        # 降级到 BLIP 描述
        try:
            headers = {"Authorization": f"Bearer {os.getenv('HF_TOKEN')}"}
            img_data = requests.get(image_url, headers=headers, timeout=10).content
            img_b64 = base64.b64encode(img_data).decode()
            api_url = "https://api-inference.huggingface.co/models/Salesforce/blip-image-captioning-base"
            api_headers = {"Authorization": f"Bearer {os.getenv('HF_TOKEN')}"}
            response = requests.post(api_url, headers=api_headers, json={"inputs": img_b64}, timeout=30)
            if response.status_code == 200:
                result = response.json()
                if isinstance(result, list) and len(result) > 0:
                    return result[0].get('generated_text', 'No caption')
                return str(result)
            else:
                return f"Caption API error: {response.status_code}"
        except Exception as e:
            return f"Image processing error: {str(e)}"