"""
Image agent backend — multimodal agent with HuggingFace image generation tools.

Uses the same tool-calling loop pattern as agent.py:
  LLM call → parse tool_calls → execute → update history → repeat

Key difference: maintains a figure store (Dict[str, str]) mapping names like
"figure_T1_1" to base64 data, so the VLM can reference images across tool calls
without passing huge base64 strings in arguments.
"""
import base64
import json
import logging
import re
from typing import List, Dict, Optional

from .tools import (
    generate_image, edit_image, read_image, save_image,
    execute_generate_image, execute_edit_image, execute_read_image,
)

logger = logging.getLogger(__name__)

TOOLS = [generate_image, edit_image, read_image, save_image]

# Max dimension for images sent to the VLM context (keeps token count manageable)
VLM_IMAGE_MAX_DIM = 512
VLM_IMAGE_JPEG_QUALITY = 70


def resize_image_for_vlm(base64_png: str) -> str:
    """Resize and compress an image for VLM context to avoid token overflow.

    Takes a full-res base64 PNG and returns a smaller base64 JPEG thumbnail
    that fits within VLM_IMAGE_MAX_DIM on its longest side.
    """
    try:
        from PIL import Image
        import io as _io

        img_bytes = base64.b64decode(base64_png)
        img = Image.open(_io.BytesIO(img_bytes))

        # Resize if larger than max dimension
        if max(img.size) > VLM_IMAGE_MAX_DIM:
            img.thumbnail((VLM_IMAGE_MAX_DIM, VLM_IMAGE_MAX_DIM), Image.LANCZOS)

        # Convert to RGB (JPEG doesn't support alpha)
        if img.mode in ("RGBA", "P"):
            img = img.convert("RGB")

        # Save as JPEG for much smaller base64
        buffer = _io.BytesIO()
        img.save(buffer, format="JPEG", quality=VLM_IMAGE_JPEG_QUALITY)
        return base64.b64encode(buffer.getvalue()).decode("utf-8")
    except Exception as e:
        logger.error(f"Failed to resize image for VLM: {e}")
        # Fall back to original — better to try than to lose the image entirely
        return base64_png

MAX_TURNS = 20


def execute_tool(tool_name: str, args: dict, hf_token: str, image_store: dict, image_counter: int, default_gen_model: str = None, default_edit_model: str = None, files_root: str = None, image_prefix: str = "figure_") -> dict:
    """
    Execute a tool by name and return result dict.

    Returns:
        dict with keys:
        - "content": str result for the LLM
        - "image": optional base64 PNG
        - "image_name": optional image reference name (e.g., "image_1")
        - "display": dict with display-friendly data for frontend
        - "image_counter": updated counter
    """
    if tool_name == "generate_image":
        prompt = args.get("prompt", "")
        model = args.get("model") or default_gen_model or "black-forest-labs/FLUX.1-schnell"
        base64_png, error = execute_generate_image(prompt, hf_token, model)

        if base64_png:
            image_counter += 1
            name = f"{image_prefix}{image_counter}"
            image_store[name] = {"type": "png", "data": base64_png}
            return {
                "content": f"Image generated successfully as '{name}'. The image is attached.",
                "image": base64_png,
                "image_name": name,
                "display": {"type": "generate", "prompt": prompt, "model": model, "image_name": name},
                "image_counter": image_counter,
            }
        else:
            return {
                "content": f"Failed to generate image: {error}",
                "display": {"type": "generate_error", "prompt": prompt},
                "image_counter": image_counter,
            }

    elif tool_name == "edit_image":
        prompt = args.get("prompt", "")
        source = args.get("source", "")
        model = args.get("model") or default_edit_model or "black-forest-labs/FLUX.1-Kontext-dev"

        # Resolve source: image store reference, URL, or local path
        source_bytes = None
        if source in image_store:
            source_bytes = base64.b64decode(image_store[source]["data"])
        else:
            source_base64 = execute_read_image(source, files_root=files_root)
            if source_base64:
                source_bytes = base64.b64decode(source_base64)

        if source_bytes is None:
            return {
                "content": f"Could not resolve image source '{source}'. Use a URL or a reference from a previous tool call (e.g., 'figure_T1_1').",
                "display": {"type": "edit_error", "source": source},
                "image_counter": image_counter,
            }

        base64_png, error = execute_edit_image(prompt, source_bytes, hf_token, model)

        if base64_png:
            image_counter += 1
            name = f"{image_prefix}{image_counter}"
            image_store[name] = {"type": "png", "data": base64_png}
            return {
                "content": f"Image edited successfully as '{name}'. The image is attached.",
                "image": base64_png,
                "image_name": name,
                "display": {"type": "edit", "prompt": prompt, "source": source, "model": model, "image_name": name},
                "image_counter": image_counter,
            }
        else:
            return {
                "content": f"Failed to edit image: {error}",
                "display": {"type": "edit_error", "source": source},
                "image_counter": image_counter,
            }

    elif tool_name == "save_image":
        source = args.get("source", "")
        filename = args.get("filename", "image.png")

        # Ensure .png extension
        if not filename.lower().endswith(".png"):
            filename += ".png"

        # Resolve source from image store or URL
        image_data = None
        if source in image_store:
            image_data = base64.b64decode(image_store[source]["data"])
        else:
            source_base64 = execute_read_image(source, files_root=files_root)
            if source_base64:
                image_data = base64.b64decode(source_base64)

        if image_data is None:
            return {
                "content": f"Could not resolve image source '{source}'. Use a reference (e.g., 'figure_T1_1') or a URL.",
                "display": {"type": "save_error", "source": source},
                "image_counter": image_counter,
            }

        # Save to files_root
        import os
        save_dir = files_root or "."
        os.makedirs(save_dir, exist_ok=True)
        # Sanitize filename
        filename = os.path.basename(filename)
        save_path = os.path.join(save_dir, filename)
        with open(save_path, "wb") as f:
            f.write(image_data)

        # Include base64 so frontend can show a preview of the saved image
        saved_base64 = base64.b64encode(image_data).decode("utf-8")
        return {
            "content": f"Image saved as '{filename}'.",
            "image": saved_base64,
            "display": {"type": "save_image", "filename": filename, "source": source},
            "image_counter": image_counter,
        }

    elif tool_name in ("read_image", "read_image_url"):
        source = args.get("source") or args.get("url", "")
        base64_png = execute_read_image(source, files_root=files_root)

        if base64_png:
            image_counter += 1
            name = f"{image_prefix}{image_counter}"
            image_store[name] = {"type": "png", "data": base64_png}
            return {
                "content": f"Image loaded successfully as '{name}'. The image is attached.",
                "image": base64_png,
                "image_name": name,
                "display": {"type": "read_image", "url": source, "image_name": name},
                "image_counter": image_counter,
            }
        else:
            # Provide more specific error for SVG files
            is_svg = source.lower().endswith(".svg") or "/svg" in source.lower()
            if is_svg:
                error_msg = f"Failed to load image from '{source}'. SVG format is not supported — only raster formats (PNG, JPEG, GIF, WebP, BMP) are accepted. Ask the user for a raster version of the image."
            else:
                error_msg = f"Failed to load image from '{source}'. Check that the path or URL is correct and that it is a raster image (PNG, JPEG, GIF, WebP, BMP)."
            return {
                "content": error_msg,
                "display": {"type": "read_image_error", "url": source},
                "image_counter": image_counter,
            }

    return {
        "content": f"Unknown tool: {tool_name}",
        "display": {"type": "error"},
        "image_counter": image_counter,
    }


def stream_image_execution(
    client,
    model: str,
    messages: List[Dict],
    hf_token: str,
    image_gen_model: Optional[str] = None,
    image_edit_model: Optional[str] = None,
    extra_params: Optional[Dict] = None,
    abort_event=None,
    files_root: str = None,
    multimodal: bool = False,
    tab_id: str = "0",
    image_store: Optional[Dict[str, dict]] = None,
    image_counter: int = 0,
):
    """
    Run the image agent tool-calling loop.

    Yields dicts with SSE event types:
      - thinking: { content }
      - content: { content }
      - tool_start: { tool, args }
      - tool_result: { tool, result, image? }
      - result_preview: { content }
      - result: { content, figures? }
      - generating: {}
      - retry: { attempt, max_attempts, delay, message }
      - error: { content }
      - done: {}
    """
    from .agents import call_llm

    turns = 0
    done = False
    image_prefix = f"figure_T{tab_id}_"

    # Use provided persistent store, or create a local one as fallback
    if image_store is None:
        image_store = {}
    result_sent = False
    debug_call_number = 0

    while not done and turns < MAX_TURNS:
        # Check abort before each turn
        if abort_event and abort_event.is_set():
            yield {"type": "aborted"}
            return

        turns += 1

        # LLM call with retries and debug events
        response = None
        for event in call_llm(client, model, messages, tools=TOOLS, extra_params=extra_params, abort_event=abort_event, call_number=debug_call_number):
            if "_response" in event:
                response = event["_response"]
                debug_call_number = event["_call_number"]
            else:
                yield event
                if event.get("type") in ("error", "aborted"):
                    return

        if response is None:
            return

        # --- Parse response ---
        assistant_message = response.choices[0].message
        content = assistant_message.content or ""
        tool_calls = assistant_message.tool_calls or []

        # Check for <result> tags
        result_match = re.search(r'<result>(.*?)</result>', content, re.DOTALL | re.IGNORECASE)
        result_content = None
        thinking_content = content

        if result_match:
            result_content = result_match.group(1).strip()
            thinking_content = re.sub(r'<result>.*?</result>', '', content, flags=re.DOTALL | re.IGNORECASE).strip()

        # Send thinking/content
        if thinking_content.strip():
            if tool_calls:
                yield {"type": "thinking", "content": thinking_content}
            else:
                yield {"type": "content", "content": thinking_content}

        # Send result preview
        if result_content:
            figures = dict(image_store)
            yield {"type": "result_preview", "content": result_content, "figures": figures}

        # --- Handle tool calls ---
        if tool_calls:
            for tool_call in tool_calls:
                # Check abort between tool calls
                if abort_event and abort_event.is_set():
                    yield {"type": "aborted"}
                    return

                func_name = tool_call.function.name

                # Parse arguments
                try:
                    args = json.loads(tool_call.function.arguments)
                except json.JSONDecodeError as e:
                    output = f"Error parsing arguments: {e}"
                    messages.append({
                        "role": "assistant",
                        "content": content,
                        "tool_calls": [{"id": tool_call.id, "type": "function", "function": {"name": func_name, "arguments": tool_call.function.arguments}}]
                    })
                    messages.append({"role": "tool", "tool_call_id": tool_call.id, "content": output})
                    yield {"type": "error", "content": output}
                    continue

                # Signal tool start
                yield {
                    "type": "tool_start",
                    "tool": func_name,
                    "args": args,
                    "tool_call_id": tool_call.id,
                    "arguments": tool_call.function.arguments,
                    "thinking": content,
                }

                # Execute tool
                result = execute_tool(func_name, args, hf_token, image_store, image_counter, default_gen_model=image_gen_model, default_edit_model=image_edit_model, files_root=files_root, image_prefix=image_prefix)
                image_counter = result.get("image_counter", image_counter)

                # Build tool response content for LLM
                if result.get("image") and multimodal:
                    vlm_image = resize_image_for_vlm(result["image"])
                    tool_response_content = [
                        {"type": "text", "text": result["content"]},
                        {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{vlm_image}"}}
                    ]
                else:
                    tool_response_content = result["content"]

                # Add to message history
                messages.append({
                    "role": "assistant",
                    "content": content,
                    "tool_calls": [{"id": tool_call.id, "type": "function", "function": {"name": func_name, "arguments": tool_call.function.arguments}}]
                })
                messages.append({
                    "role": "tool",
                    "tool_call_id": tool_call.id,
                    "content": tool_response_content
                })

                # Signal tool result to frontend
                tool_result_event = {
                    "type": "tool_result",
                    "tool": func_name,
                    "tool_call_id": tool_call.id,
                    "result": result.get("display", {}),
                    "response": result.get("content", ""),
                }
                if result.get("image"):
                    tool_result_event["image"] = result["image"]
                if result.get("image_name"):
                    tool_result_event["image_name"] = result["image_name"]
                yield tool_result_event

        else:
            # No tool calls — we're done
            messages.append({"role": "assistant", "content": content})
            done = True

        # Send result if found
        if result_content:
            figures = dict(image_store)
            yield {"type": "result", "content": result_content, "figures": figures}
            result_sent = True

        # Signal between-turn processing
        if not done:
            yield {"type": "generating"}

    # If agent finished without a <result>, nudge it for one
    if not result_sent:
        from .agents import nudge_for_result
        nudge_produced_result = False
        figures = dict(image_store)
        for event in nudge_for_result(client, model, messages, extra_params=extra_params, extra_result_data={"figures": figures}, call_number=debug_call_number):
            yield event
            if event.get("type") == "result":
                nudge_produced_result = True

        # Final fallback: synthesize a result with all figures
        if not nudge_produced_result:
            fallback_parts = [f"<{name}>" for name in image_store]
            figures = dict(image_store)
            yield {"type": "result", "content": "\n\n".join(fallback_parts), "figures": figures}

    yield {"type": "done"}