Update app.py

app.py

@@ -4,138 +4,151 @@
 import os
 import json
 import gradio as gr
-import requests
-
-# 新版 openai>=1.0.0
 from openai import OpenAI
 
 ##############################################################################
-# 1. 从外部文件中加载 Furry 物种数据 + Gender 细节文本
+# 1. 读取外部文件: furry_species.json & gender_rules.json
 ##############################################################################
-
-# 读取 Furry 物种分类的 JSON 文件
-# 假设文件名为 furry_species.json，结构示例：
-# {
-#   "CANIDS": ["Dogs", "Wolves", "Foxes"],
-#   "FELINES": ["Lions", "Tigers", "Cheetahs"],
-#   ...
-# }
+# 假设 furry_species.json 的结构是多级字典: { "AQUATICS ...": { "Cetaceans": [], ...}, ... }
 try:
-    with open("furry_species.json", "r", encoding="utf-8") as fs_file:
-        furry_map = json.load(fs_file)
+    with open("furry_species.json", "r", encoding="utf-8") as f:
+        FURRY_DATA = json.load(f)
 except:
-    furry_map = {}  # 若文件不存在或出错，可给个空dict备用
-
-def flatten_furry_species(map_data):
-    """
-    将 {分类: [物种列表]} 展开为 ["分类 - 物种"] 形式的扁平列表
-    """
-    result = []
-    for category, species_list in map_data.items():
-        for sp in species_list:
-            result.append(f"{category} - {sp}")
-    return sorted(result)
+    FURRY_DATA = {}
 
-ALL_FURRY_SPECIES = flatten_furry_species(furry_map)
-
-# 读取 Gender 细节长文本 (记忆库)
+# gender_rules.json: { "male": "...", "female": "...", "intersex": "...", "genderless": "..." }
 try:
-    with open("gender_details.txt", "r", encoding="utf-8") as gd_file:
-        GENDER_DETAILS_TEXT = gd_file.read()
+    with open("gender_rules.json", "r", encoding="utf-8") as f:
+        GENDER_RULES = json.load(f)
 except:
-    GENDER_DETAILS_TEXT = (
-        "Gender conversion rules are missing. Please provide gender_details.txt."
-    )
+    GENDER_RULES = {}
 
 ##############################################################################
-# 2. 核心函数：根据选择调用 GPT 或 DeepSeek
+# 2. 构造多级下拉菜单：先选“主分类”，再选“子分类”
 ##############################################################################
+def get_top_categories(furry_data):
+    """获取所有顶级分类 (keys)"""
+    return sorted(list(furry_data.keys()))
+
+def get_sub_categories(furry_data, top_category):
+    """
+    根据所选 top_category, 返回二级分类列表
+    furry_data[top_category] -> { "Cetaceans": [...], "FishFurs": [...], ... }
+    """
+    if top_category in furry_data:
+        return sorted(list(furry_data[top_category].keys()))
+    return []
 
-def generate_description_with_tags(prompt, gender_option, furry_species, api_mode, api_key):
+def get_species_list(furry_data, top_category, sub_category):
     """
-    1) 构造 tags（包含 gender/furry/base_prompt）。
-    2) 调用 GPT 或 DeepSeek，带上记忆库信息 (GENDER_DETAILS_TEXT) 作为系统提示。
-    3) 返回 (tags + 自然语言描述) 的输出形式。
+    返回最终物种列表
+    furry_data[top_category][sub_category] -> list
     """
+    if (
+        top_category in furry_data
+        and sub_category in furry_data[top_category]
+    ):
+        return sorted(furry_data[top_category][sub_category])
+    return []
 
+##############################################################################
+# 3. 调用逻辑：GPT 或 DeepSeek
+##############################################################################
+def generate_tags_and_description(prompt, gender_option, top_cat, sub_cat, species_item, api_mode, api_key):
+    """
+    1) 构造 tags: gender, base_prompt, furry_species
+    2) 读取 gender_rules 并拼入 system prompt
+    3) 调用 GPT/DeepSeek
+    4) 输出 (tags + 自然语言描述)
+    """
     if not api_key:
         return "Error: No API Key provided."
-
-    # 组装 tags
+    
+    # 性别
     tags = {}
     if gender_option == "Trans_to_Male":
         tags["gender"] = "male"
+        rule_text = GENDER_RULES.get("male", "")
     elif gender_option == "Trans_to_Female":
         tags["gender"] = "female"
+        rule_text = GENDER_RULES.get("female", "")
     elif gender_option == "Trans_to_Mannequin":
         tags["gender"] = "genderless"
+        rule_text = GENDER_RULES.get("genderless", "")
     elif gender_option == "Trans_to_Intersex":
         tags["gender"] = "intersex"
-    elif gender_option == "Trans_to_Furry":
+        rule_text = GENDER_RULES.get("intersex", "")
+    else:
+        # Furry
         tags["gender"] = "furry"
-        tags["furry_species"] = furry_species or "unknown"
-
+        rule_text = (
+            GENDER_RULES.get("male", "") + "\n\n"  # 你可以根据自己的业务需求处理
+            + GENDER_RULES.get("female", "") + "\n\n"
+            + GENDER_RULES.get("intersex", "") + "\n\n"
+            + GENDER_RULES.get("genderless", "")
+        )  # 或者只给一个简要 furry rule
+
+        # 选定物种
+        final_species = "unknown"
+        if top_cat and sub_cat and species_item:
+            final_species = f"{top_cat} > {sub_cat} > {species_item}"
+        tags["furry_species"] = final_species
+
+    # 原始提示词
     tags["base_prompt"] = prompt
 
-    # 确定 base_url + model
+    # BaseURL & 模型
     if api_mode == "GPT":
         base_url = None
-        model_name = "gpt-3.5-turbo"  # or "gpt-4", "gpt-4o", etc.
+        model_name = "gpt-3.5-turbo"
     else:
-        # DeepSeek
         base_url = "https://api.deepseek.com"
         model_name = "deepseek-chat"
 
-    # 创建 OpenAI Client
     client = OpenAI(api_key=api_key)
     if base_url:
         client.base_url = base_url
 
-    # 将tags拼成便于阅读的字符串
+    # 将 tags 拼为字符串
     tags_str = "\n".join([f"{k}: {v}" for k, v in tags.items() if v])
 
-    # 准备对话消息：把 gender_details 放在系统提示中，以便 AI 参考
+    # system prompt 带上Gender Rules
     system_prompt = (
         "You are a creative assistant that generates detailed and imaginative scene descriptions "
         "for AI generation prompts. Focus on the details provided, incorporate them into a cohesive narrative, "
-        "and obey the following gender transformation & furry rules:\n\n"
-        f"{GENDER_DETAILS_TEXT}\n\n"
-        "After forming your final description, return it in no more than five sentences. Thank you!"
+        "and follow these gender/furry rules:\n\n"
+        f"{rule_text}\n\n"
+        "When you respond, do not exceed five sentences. Return your final text in English or relevant language.\n"
     )
 
+    # Chat
     try:
-        response = client.chat.completions.create(
+        resp = client.chat.completions.create(
             model=model_name,
             messages=[
                 {"role": "system", "content": system_prompt},
                 {
                     "role": "user",
-                    "content": (
-                        f"Here are the tags:\n{tags_str}\n"
-                        f"Please generate a vivid, imaginative scene description."
-                    ),
+                    "content": f"Here are the tags:\n{tags_str}\nPlease generate a vivid, imaginative scene description."
                 },
             ],
         )
-        description = response.choices[0].message.content.strip()
-
-        # 输出形式：(tags + 自然语言描述)
-        output_text = f"=== Tags ===\n{tags_str}\n\n=== Description ===\n{description}"
-        return output_text
+        desc_text = resp.choices[0].message.content.strip()
+        # 输出 (tags + desc)
+        return f"=== Tags ===\n{tags_str}\n\n=== Description ===\n{desc_text}"
 
     except Exception as e:
         return f"{api_mode} generation failed. Error: {e}"
 
-def translate_text(text, translate_language, api_mode, api_key):
+def translate_text(content, lang, api_mode, api_key):
     """
-    仅做一次简单翻译。若不需要翻译可以省去。也可用系统提示让 AI 翻译。
+    调用 GPT 或 DeepSeek 做翻译
     """
     if not api_key:
         return "Error: No API Key provided."
-    if not text.strip():
+    if not content.strip():
         return ""
 
-    # GPT vs DeepSeek
     if api_mode == "GPT":
         base_url = None
         model_name = "gpt-3.5-turbo"
@@ -147,40 +160,39 @@ def translate_text(text, translate_language, api_mode, api_key):
     if base_url:
         client.base_url = base_url
 
-    system_prompt = f"You are a professional translator. Translate the following text to {translate_language}:"
+    system_prompt = f"You are a translator. Translate the following text to {lang}:"
     try:
         resp = client.chat.completions.create(
             model=model_name,
             messages=[
                 {"role": "system", "content": system_prompt},
-                {"role": "user",   "content": text},
-            ]
+                {"role": "user",   "content": content},
+            ],
         )
         return resp.choices[0].message.content.strip()
     except Exception as e:
         return f"{api_mode} translation failed. Error: {e}"
 
 ##############################################################################
-# 3. Gradio 界面
+# 4. Gradio 界面
 ##############################################################################
 def build_interface():
     with gr.Blocks() as demo:
-        gr.Markdown("## Prompt Transformer (GPT / DeepSeek) - (tags + 自然语言描述)")
+        gr.Markdown("## Prompt Furry/Gender Transformer (GPT / DeepSeek)")
 
         with gr.Row():
             with gr.Column():
-                # 选择 API
                 api_mode = gr.Radio(
                     label="选择API (GPT or DeepSeek)",
                     choices=["GPT", "DeepSeek"],
-                    value="GPT",
+                    value="GPT"
                 )
                 api_key = gr.Textbox(
-                    label="API密钥 (API Key)",
-                    type="password",
-                    placeholder="在此输入 GPT / DeepSeek 的API Key"
+                    label="API Key",
+                    type="password"
                 )
-                # 性别 / Furry
+
+                # 性别
                 gender_option = gr.Radio(
                     label="转换目标",
                     choices=[
@@ -188,62 +200,110 @@ def build_interface():
                         "Trans_to_Female",
                         "Trans_to_Mannequin",
                         "Trans_to_Intersex",
-                        "Trans_to_Furry",
+                        "Trans_to_Furry"
                     ],
-                    value="Trans_to_Male",
+                    value="Trans_to_Male"
+                )
+
+                # 顶级分类
+                top_cat_dd = gr.Dropdown(
+                    label="Furry: 主分类 (Top Category)",
+                    choices=get_top_categories(FURRY_DATA),
+                    value=None,
+                    visible=False
+                )
+                # 二级分类
+                sub_cat_dd = gr.Dropdown(
+                    label="Furry: 子分类 (Sub-Category)",
+                    choices=[],
+                    value=None,
+                    visible=False
                 )
-                furry_species = gr.Dropdown(
-                    label="Furry 物种选择",
-                    choices=ALL_FURRY_SPECIES,
+                # 物种
+                species_dd = gr.Dropdown(
+                    label="Furry: 物种 (Species)",
+                    choices=[],
                     value=None,
                     visible=False
                 )
 
-                def show_furry_species(choice):
-                    return gr.update(visible=(choice == "Trans_to_Furry"))
+                # 性别选项变化 -> 显示或隐藏 Furry 下拉
+                def show_furry_options(chosen):
+                    if chosen == "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]
+                )
 
-                gender_option.change(show_furry_species, inputs=[gender_option], outputs=[furry_species])
+                # 顶级分类 -> 更新子分类
+                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]
+                )
+
+                # 子分类 -> 更新物种
+                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="提示词 (Prompt)",
-                    lines=4,
-                    placeholder="示例：一位穿着蓝色长裙的少女，坐在海边..."
+                    lines=4
                 )
-                output_tags_and_desc = gr.Textbox(
+                output_result = gr.Textbox(
                     label="(tags + 自然语言描述)",
                     lines=10
                 )
 
         with gr.Row():
-            translate_language = gr.Dropdown(
-                label="翻译语言 (Translation Language)",
+            translate_lang = gr.Dropdown(
+                label="翻译语言",
                 choices=["English", "Chinese", "Japanese", "French", "German", "Spanish"],
                 value="English"
             )
-            translate_output = gr.Textbox(
+            translate_result = gr.Textbox(
                 label="翻译结果",
                 lines=10
             )
 
-        # 点击生成 -> 同时调用生成描述 & 翻译
-        def on_generate(prompt, gender, f_species, mode, key, lang):
-            # 1. 生成 (tags + 自然语言描述)
-            result_text = generate_description_with_tags(prompt, gender, f_species, mode, key)
-            # 2. 翻译
-            translated = translate_text(result_text, lang, mode, key)
-            return result_text, translated
+        ######################################################################
+        # 生成
+        ######################################################################
+        def on_generate(prompt, gender, tc, sc, spc, mode, key, lang):
+            # 1) 生成
+            tags_desc = generate_tags_and_description(prompt, gender, tc, sc, spc, mode, key)
+            # 2) 翻译
+            trans_txt = translate_text(tags_desc, lang, mode, key)
+            return tags_desc, trans_txt
 
         user_prompt.submit(
             fn=on_generate,
-            inputs=[user_prompt, gender_option, furry_species, api_mode, api_key, translate_language],
-            outputs=[output_tags_and_desc, translate_output]
+            inputs=[user_prompt, gender_option, top_cat_dd, sub_cat_dd, species_dd, api_mode, api_key, translate_lang],
+            outputs=[output_result, translate_result]
         )
-        generate_btn = gr.Button("生成 / Generate")
-        generate_btn.click(
+
+        gen_btn = gr.Button("生成 / Generate")
+        gen_btn.click(
             fn=on_generate,
-            inputs=[user_prompt, gender_option, furry_species, api_mode, api_key, translate_language],
-            outputs=[output_tags_and_desc, translate_output]
+            inputs=[user_prompt, gender_option, top_cat_dd, sub_cat_dd, species_dd, api_mode, api_key, translate_lang],
+            outputs=[output_result, translate_result]
         )
 
     return demo