# storyverse_weaver/core/image_services.py
import os
import requests # For generic API calls
import base64
from io import BytesIO
from PIL import Image
# from dotenv import load_dotenv
# load_dotenv()

# --- API Key Configuration (Use specific names for StoryVerse) ---
STABILITY_API_KEY = os.getenv("STORYVERSE_STABILITY_API_KEY")
OPENAI_API_KEY = os.getenv("STORYVERSE_OPENAI_API_KEY") # For DALL-E
# HUGGINGFACE_TOKEN also used by llm_services, can be reused if using HF image models

STABILITY_API_CONFIGURED = bool(STABILITY_API_KEY and STABILITY_API_KEY.strip())
OPENAI_DALLE_CONFIGURED = bool(OPENAI_API_KEY and OPENAI_API_KEY.strip())
# HF_IMAGE_CONFIGURED = bool(HF_TOKEN and HF_TOKEN.strip()) # Assuming HF_TOKEN is also for image models

class ImageGenResponse:
    def __init__(self, image: Image.Image = None, image_url: str = None, error: str = None, success: bool = True, provider: str = "unknown"):
        self.image = image # PIL Image object
        self.image_url = image_url # If API returns a URL
        self.error = error
        self.success = success
        self.provider = provider

def initialize_image_llms(): # Simple function to print status
    print("INFO: image_services.py - Initializing Image Generation services...")
    if STABILITY_API_CONFIGURED: print("SUCCESS: image_services.py - Stability AI API Key detected.")
    else: print("WARNING: image_services.py - STORYVERSE_STABILITY_API_KEY not found. Stability AI disabled.")
    if OPENAI_DALLE_CONFIGURED: print("SUCCESS: image_services.py - OpenAI API Key detected (for DALL-E).")
    else: print("WARNING: image_services.py - STORYVERSE_OPENAI_API_KEY not found. DALL-E disabled.")
    # if HF_IMAGE_CONFIGURED: print("INFO: image_services.py - Hugging Face Token detected (can be used for HF image models).")
    print("INFO: image_services.py - Image LLM Init complete.")


# --- Stability AI (Example) ---
def generate_image_stabilityai(prompt: str, style_preset: str = None, negative_prompt: str = None, 
                               engine_id: str = "stable-diffusion-xl-1024-v1-0", 
                               steps: int = 30, cfg_scale: float = 7.0, 
                               width: int = 1024, height: int = 1024) -> ImageGenResponse:
    if not STABILITY_API_CONFIGURED:
        return ImageGenResponse(error="Stability AI API key not configured.", success=False, provider="StabilityAI")

    api_host = os.getenv('API_HOST', 'https://api.stability.ai')
    request_url = f"{api_host}/v1/generation/{engine_id}/text-to-image"

    payload = {
        "text_prompts": [{"text": prompt}],
        "cfg_scale": cfg_scale,
        "height": height,
        "width": width,
        "steps": steps,
        "samples": 1,
    }
    if style_preset: payload["style_preset"] = style_preset
    if negative_prompt: payload["text_prompts"].append({"text": negative_prompt, "weight": -1.0})

    headers = {
        "Accept": "application/json",
        "Content-Type": "application/json",
        "Authorization": f"Bearer {STABILITY_API_KEY}"
    }
    print(f"DEBUG: image_services.py - Calling Stability AI with prompt: {prompt[:50]}...")
    try:
        response = requests.post(request_url, headers=headers, json=payload, timeout=60) # Increased timeout
        response.raise_for_status() # Will raise an HTTPError if the HTTP request returned an unsuccessful status code
        
        artifacts = response.json().get("artifacts")
        if not artifacts:
            return ImageGenResponse(error="No image artifacts found in Stability AI response.", success=False, provider="StabilityAI", raw_response=response.text)

        image_data = base64.b64decode(artifacts[0]["base64"])
        image = Image.open(BytesIO(image_data))
        print("DEBUG: image_services.py - Stability AI image generated successfully.")
        return ImageGenResponse(image=image, provider="StabilityAI")
    except requests.exceptions.RequestException as e:
        error_msg = f"Stability AI API request failed: {type(e).__name__} - {str(e)}"
        if hasattr(e, 'response') and e.response is not None: error_msg += f" - Response: {e.response.text[:200]}"
        print(f"ERROR: image_services.py - {error_msg}")
        return ImageGenResponse(error=error_msg, success=False, provider="StabilityAI", raw_response=e)
    except Exception as e: # Catch other potential errors like JSON decoding
        error_msg = f"Error processing Stability AI response: {type(e).__name__} - {str(e)}"
        print(f"ERROR: image_services.py - {error_msg}")
        return ImageGenResponse(error=error_msg, success=False, provider="StabilityAI", raw_response=e)


# --- DALL-E (Conceptual - you'd need 'openai' library and setup) ---
def generate_image_dalle(prompt: str, size="1024x1024", quality="standard", n=1) -> ImageGenResponse:
    if not OPENAI_DALLE_CONFIGURED:
        return ImageGenResponse(error="OpenAI DALL-E API key not configured.", success=False, provider="DALL-E")
    try:
        # from openai import OpenAI # Would be imported at top level
        # client = OpenAI(api_key=OPENAI_API_KEY)
        # response = client.images.generate(
        # model="dall-e-3", # or "dall-e-2"
        # prompt=prompt,
        # size=size,
        # quality=quality,
        # n=n,
        # response_format="url" # or "b64_json"
        # )
        # image_url = response.data[0].url
        # image_content = requests.get(image_url).content
        # image = Image.open(BytesIO(image_content))
        # return ImageGenResponse(image=image, image_url=image_url, provider="DALL-E")
        print("DEBUG: image_services.py - DALL-E call placeholder.") # Placeholder
        # Simulate an image for now
        dummy_image = Image.new('RGB', (512, 512), color = 'skyblue')
        return ImageGenResponse(image=dummy_image, provider="DALL-E (Simulated)")
    except Exception as e:
        return ImageGenResponse(error=f"DALL-E API Error: {type(e).__name__} - {str(e)}", success=False, provider="DALL-E")


# --- Hugging Face Image Model (Conceptual - via Inference API or local Diffusers) ---
# def generate_image_hf_model(prompt: str, model_id="stabilityai/stable-diffusion-xl-base-1.0") -> ImageGenResponse:
# if not HF_IMAGE_CONFIGURED:
# return ImageGenResponse(error="HF Token not configured for image models.", success=False, provider="HF")
# try:
# You might use a client similar to hf_inference_text_client but for image generation task
# Or if it's a diffusers pipeline, you'd load and run it.
# This requires the `diffusers` library and often significant compute.
# response_bytes = hf_some_image_client.text_to_image(prompt, model=model_id) # Hypothetical client method
# image = Image.open(BytesIO(response_bytes))
# return ImageGenResponse(image=image, provider=f"HF ({model_id})")
# print("DEBUG: image_services.py - HF Image Model call placeholder.")
# dummy_image = Image.new('RGB', (512, 512), color = 'lightgreen')
# return ImageGenResponse(image=dummy_image, provider=f"HF ({model_id} - Simulated)")
# except Exception as e:
# return ImageGenResponse(error=f"HF Image Model Error: {type(e).__name__} - {str(e)}", success=False, provider=f"HF ({model_id})")

print("DEBUG: core.image_services (for StoryVerseWeaver) - Module defined.")