import os
import json
import re
import gradio as gr
from openai import OpenAI

##############################################################################
# 1. 读取外部文件
##############################################################################
try:
    with open("furry_species.json", "r", encoding="utf-8") as ff:
        FURRY_DATA = json.load(ff)
except:
    FURRY_DATA = {}

try:
    with open("gender_rules.json", "r", encoding="utf-8") as gf:
        GENDER_RULES = json.load(gf)
except:
    GENDER_RULES = {}

try:
    with open("transform_rules.json", "r", encoding="utf-8") as tf:
        TRANSFORM_DICT = json.load(tf)
except:
    TRANSFORM_DICT = {}

##############################################################################
# 2. 多级菜单函数
##############################################################################
def get_top_categories(furry_data):
    return sorted(list(furry_data.keys()))

def get_sub_categories(furry_data, top_category):
    if top_category in furry_data:
        return sorted(list(furry_data[top_category].keys()))
    return []

def get_species_list(furry_data, top_category, sub_category):
    if top_category in furry_data and sub_category in furry_data[top_category]:
        return sorted(furry_data[top_category][sub_category])
    return []

##############################################################################
# 3. 合并规则文本
##############################################################################
def merge_transform_rules_into_prompt(rules_json):
    """
    将 transform_rules.json 中的相关字段转为统一文本，便于放到 system_prompt。
    你也可以分段加入。
    """
    if not rules_json:
        return "(No transform rules loaded)"
    
    # 1) 读取 gender_transform
    gt = rules_json.get("gender_transform", {})
    # 注意：这里不一定要用 male_tag_rules, replacements 等字段，
    # 仅做一个演示将 gt 转成文本
    text_parts = []

    text_parts.append("==== GENDER TRANSFORM RULES ====")
    text_parts.append(str(gt))  # 直接转为字符串或更有条理地拼写

    # 2) shared_preferences
    sp = rules_json.get("shared_preferences", {})
    text_parts.append("==== SHARED PREFERENCES ====")
    text_parts.append(str(sp))

    # 3) table_details
    td = rules_json.get("table_details", {})
    text_parts.append("==== TABLE DETAILS (PRO ACTIONS) ====")
    text_parts.append(str(td))

    return "\n".join(text_parts)

RULES_TEXT_FULL = merge_transform_rules_into_prompt(TRANSFORM_DICT)

##############################################################################
# 4. 强制替换逻辑
##############################################################################

# 建立一个映射：用户在前端选 "Trans_to_Male" -> 我们用 transform_rules.json["override_conflicting_descriptors"]["female_to_male"] 等
transform_map = {
    "Trans_to_Male": "female_to_male",
    "Trans_to_Female": "male_to_female",
    "Trans_to_Mannequin": "any_to_genderless",
    "Trans_to_Intersex": "any_to_intersex",
    "Trans_to_Furry": "trans_to_furry",  # 也可在这里加:  if we want forced replacement for "she->anthro_female" etc.
}

def forced_replace(prompt, direction):
    """
    根据 TRANSFORM_DICT["override_conflicting_descriptors"] 下的映射，
    对 prompt 中的词做强制替换。
    """
    # direction 是 "female_to_male" / "male_to_female" / ...
    override_section = TRANSFORM_DICT.get("override_conflicting_descriptors", {})
    replacements = override_section.get(direction, {})
    if not replacements:
        # 该方向没有映射，直接返回
        return prompt

    # 逐条用正则整词替换
    for old, new in replacements.items():
        # \b 表示单词边界，(?i) 表示不区分大小写
        pattern = r"(?i)\b" + re.escape(old) + r"\b"
        prompt = re.sub(pattern, new, prompt)
    return prompt

##############################################################################
# 5. 核心 GPT/DeepSeek 调用
##############################################################################
def generate_transformed_output(prompt, gender_option, top_cat, sub_cat, species_item, api_mode, api_key):
    """
    读取 transform_rules.json / GENDER_RULES / FurryData:
    只输出两段：(tags)\n\n(description)
    """
    if not api_key:
        return "Error: No API Key provided."

    if api_mode == "GPT":
        base_url = None
        model_name = "gpt-3.5-turbo"
    else:
        base_url = "https://api.deepseek.com"
        model_name = "deepseek-chat"

    client = OpenAI(api_key=api_key)
    if base_url:
        client.base_url = base_url

    # 如果选 Furry:
    if gender_option == "Trans_to_Furry":
        furry_path = f"{top_cat} > {sub_cat} > {species_item}" if (top_cat and sub_cat and species_item) else "unknown"
        extra_line = f"\nFurry chosen: {furry_path}\n"
    else:
        extra_line = ""

    # 根据 gender_option，取对应 GENDER_RULES
    gender_specific_rule = ""
    if gender_option == "Trans_to_Male":
        gender_specific_rule = GENDER_RULES.get("male", "")
    elif gender_option == "Trans_to_Female":
        gender_specific_rule = GENDER_RULES.get("female", "")
    elif gender_option == "Trans_to_Mannequin":
        gender_specific_rule = GENDER_RULES.get("genderless", "")
    elif gender_option == "Trans_to_Intersex":
        gender_specific_rule = GENDER_RULES.get("intersex", "")

    system_prompt = f"""
You are a creative assistant that transforms the user's base prompt 
to reflect correct gender/furry transformations. Follow these references:

1) Detailed Transform Rules (transform_rules.json):
{RULES_TEXT_FULL}

2) Additional short gender rules (gender_rules.json):
{gender_specific_rule}

{extra_line}
Instructions:
- Original prompt tags: {prompt}
- Convert them into NEW combined tags, removing or replacing conflicting ones.
- Only output two parts:
  1) One line of final tags in parentheses, e.g. (male, short hair, dynamic pose, ...)
  2) A blank line.
  3) Then 3~6 sentences of imaginative scene description in English.
- No extra lines, no headings, no 'gender:' or 'base_prompt:'.
- End of instructions.
""".strip()

    try:
        resp = client.chat.completions.create(
            model=model_name,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user",  "content": "Generate final tags and description now."}
            ],
        )
        return resp.choices[0].message.content.strip()

    except Exception as e:
        return f"{api_mode} generation failed. Error: {e}"

##############################################################################
# 6. 翻译函数
##############################################################################
def translate_text(content, lang, api_mode, api_key):
    if not api_key:
        return "Error: No API Key provided."
    if not content.strip():
        return ""

    if api_mode == "GPT":
        base_url = None
        model_name = "gpt-3.5-turbo"
    else:
        base_url = "https://api.deepseek.com"
        model_name = "deepseek-chat"

    client = OpenAI(api_key=api_key)
    if base_url:
        client.base_url = base_url

    translate_system_prompt = f"""
You are a translator. Translate the following text to {lang}, 
keeping parentheses line and blank line if present. 
No extra headings.
""".strip()

    try:
        resp = client.chat.completions.create(
            model=model_name,
            messages=[
                {"role": "system", "content": translate_system_prompt},
                {"role": "user",   "content": content}
            ],
        )
        return resp.choices[0].message.content.strip()
    except Exception as e:
        return f"{api_mode} translation failed. Error: {e}"

##############################################################################
# 7. Gradio 界面
##############################################################################
def build_interface():
    with gr.Blocks() as demo:
        gr.Markdown("## Prompt Trans-Tool - 提示词物种性别转换器")

        with gr.Row():
            with gr.Column():
                api_mode = gr.Radio(
                    label="Select API 选择API厂商 (GPT/DeepSeek)",
                    choices=["GPT", "DeepSeek"],
                    value="GPT"
                )
                api_key = gr.Textbox(
                    label="API Key",
                    type="password",
                    placeholder="Input your GPT or DeepSeek Key"
                )

                gender_option = gr.Radio(
                    label="Trans-Option 选择转换目标",
                    choices=[
                        "Trans_to_Male",
                        "Trans_to_Female",
                        "Trans_to_Mannequin",
                        "Trans_to_Intersex",
                        "Trans_to_Furry"
                    ],
                    value="Trans_to_Male"
                )

                top_cat_dd = gr.Dropdown(
                    label="Furry: Top Category",
                    choices=get_top_categories(FURRY_DATA),
                    value=None,
                    visible=False
                )
                sub_cat_dd = gr.Dropdown(
                    label="Furry: Sub Category",
                    choices=[],
                    value=None,
                    visible=False
                )
                species_dd = gr.Dropdown(
                    label="Furry: Species",
                    choices=[],
                    value=None,
                    visible=False
                )

                def show_furry_options(opt):
                    if opt == "Trans_to_Furry":
                        return (gr.update(visible=True),
                                gr.update(visible=True),
                                gr.update(visible=True))
                    else:
                        return (gr.update(visible=False),
                                gr.update(visible=False),
                                gr.update(visible=False))

                gender_option.change(
                    fn=show_furry_options,
                    inputs=[gender_option],
                    outputs=[top_cat_dd, sub_cat_dd, species_dd]
                )

                def on_top_cat_select(selected):
                    subs = get_sub_categories(FURRY_DATA, selected)
                    return gr.update(choices=subs, value=None)

                top_cat_dd.change(
                    fn=on_top_cat_select,
                    inputs=[top_cat_dd],
                    outputs=[sub_cat_dd]
                )

                def on_sub_cat_select(top_c, sub_c):
                    sp = get_species_list(FURRY_DATA, top_c, sub_c)
                    return gr.update(choices=sp, value=None)

                sub_cat_dd.change(
                    fn=on_sub_cat_select,
                    inputs=[top_cat_dd, sub_cat_dd],
                    outputs=[species_dd]
                )

            with gr.Column():
                user_prompt = gr.Textbox(
                    label="Original Prompt 原始提示词 (e.g. 1girl, butterfly, solo, ...)",
                    lines=5
                )
                final_output = gr.Textbox(
                    label="Transformed Output 翻译结果 (tags + description)",
                    lines=10
                )

        with gr.Row():
            translate_lang = gr.Dropdown(
                label="Translate to Language 翻译语言",
                choices=["English", "Chinese", "Japanese", "French", "German", 
                         "Italian", "Spanish", "Russian", "Dutch", "Persian", "Arabic", "Thai"],
                value="English"
            )
            translated_text = gr.Textbox(
                label="Translated Result",
                lines=10
            )

        ######################################################################
        # 生成
        ######################################################################
        def on_generate(prompt, gender, tc, sc, spc, mode, key, lang):
            # 1) 先根据 "gender" 选项判断要执行哪种 forced_replace
            direction = transform_map.get(gender, None)
            if direction:
                # 在提交给 GPT 之前，对 prompt 做强制替换
                prompt = forced_replace(prompt, direction)

            # 2) 再调用原先的 generate_transformed_output
            merged = generate_transformed_output(prompt, gender, tc, sc, spc, mode, key)

            # 3) 翻译
            trans = translate_text(merged, lang, mode, key)
            return merged, trans

        user_prompt.submit(
            fn=on_generate,
            inputs=[user_prompt, gender_option, top_cat_dd, sub_cat_dd, species_dd, api_mode, api_key, translate_lang],
            outputs=[final_output, translated_text]
        )

        gen_btn = gr.Button("Generate")
        gen_btn.click(
            fn=on_generate,
            inputs=[user_prompt, gender_option, top_cat_dd, sub_cat_dd, species_dd, api_mode, api_key, translate_lang],
            outputs=[final_output, translated_text]
        )

    return demo

if __name__ == "__main__":
    demo = build_interface()
    demo.launch()