Update app.py

app.py

@@ -1,5 +1,6 @@
 import os
 import json
+import re
 import gradio as gr
 from openai import OpenAI
 
@@ -53,25 +54,20 @@ def merge_transform_rules_into_prompt(rules_json):
     
     # 1) 读取 gender_transform
     gt = rules_json.get("gender_transform", {})
-    male_tag_rules = gt.get("male_tag_rules", {})
-    replacements = gt.get("tag_replacements", {})
-    additional = gt.get("additional_reminders", {})
-    
-    # 2) shared_preferences
-    sp = rules_json.get("shared_preferences", {})
-    
-    # 3) table_details
-    td = rules_json.get("table_details", {})
-
-    # 这只是一个简单示例，将 key-value 拼起来，真实项目可更精细
+    # 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))
 
@@ -80,7 +76,39 @@ def merge_transform_rules_into_prompt(rules_json):
 RULES_TEXT_FULL = merge_transform_rules_into_prompt(TRANSFORM_DICT)
 
 ##############################################################################
-# 4. 核心 GPT/DeepSeek 调用
+# 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):
     """
@@ -103,14 +131,12 @@ def generate_transformed_output(prompt, gender_option, top_cat, sub_cat, species
 
     # 如果选 Furry:
     if gender_option == "Trans_to_Furry":
-        # 在 system prompt 内也可写点提示：
         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 = ""
 
-    # 这里把 "RULES_TEXT_FULL" (来自 transform_rules.json) + GENDER_RULES + 
-    # "extra_line" 一并放到 system_prompt
+    # 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", "")
@@ -148,13 +174,17 @@ Instructions:
             model=model_name,
             messages=[
                 {"role": "system", "content": system_prompt},
-                {"role": "user", "content": "Generate final tags and description now."}
+                {"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."
@@ -183,7 +213,7 @@ No extra headings.
             model=model_name,
             messages=[
                 {"role": "system", "content": translate_system_prompt},
-                {"role": "user", "content": content}
+                {"role": "user",   "content": content}
             ],
         )
         return resp.choices[0].message.content.strip()
@@ -191,7 +221,7 @@ No extra headings.
         return f"{api_mode} translation failed. Error: {e}"
 
 ##############################################################################
-# 5. Gradio 界面
+# 7. Gradio 界面
 ##############################################################################
 def build_interface():
     with gr.Blocks() as demo:
@@ -290,7 +320,8 @@ def build_interface():
         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"],
+                choices=["English", "Chinese", "Japanese", "French", "German", 
+                         "Italian", "Spanish", "Russian", "Dutch", "Persian", "Arabic", "Thai"],
                 value="English"
             )
             translated_text = gr.Textbox(
@@ -302,7 +333,16 @@ def build_interface():
         # 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