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import os
import gradio as gr
import openai
##############################################################################
# 1. GPT 或 DeepSeek 调用示例函数
##############################################################################
def generate_natural_language_description_gpt(tags, api_key, base_url=None, model="gpt-4"):
"""
使用 OpenAI GPT 生成自然语言描述的示例函数。
"""
if not api_key:
return "Error: GPT API Key not provided."
# 设置 API
openai.api_key = api_key
if base_url:
openai.api_base = base_url
# 将 dict 转成可读字符串
tag_descriptions = "\n".join([
f"{key}: {', '.join(value) if isinstance(value, list) else value}"
for key, value in tags.items() if value
])
try:
response = openai.ChatCompletion.create(
model=model,
messages=[
{
"role": "system",
"content": (
"You are a creative assistant that generates detailed and imaginative scene descriptions "
"for AI generation prompts. Focus on the details provided and incorporate them into a "
"cohesive narrative. Use at least three sentences but no more than five sentences."
),
},
{
"role": "user",
"content": f"Here are the tags and details:\n{tag_descriptions}\nPlease generate a vivid, imaginative scene description.",
},
]
)
return response.choices[0].message.content.strip()
except Exception as e:
return f"GPT generation failed. Error: {e}"
def generate_natural_language_description_deepseek(tags, api_key, base_url=None):
"""
使用 DeepSeek API 生成自然语言描述的示例函数。
这里演示伪代码,你需要根据实际 DeepSeek 的文档进行实现。
"""
if not api_key:
return "Error: DeepSeek API Key not provided."
# 伪代码示例(需根据你的 DeepSeek API 文档做实际实现)
# import requests
# response = requests.post(
# url=base_url or "https://api.deepseek.com/xxx",
# headers={"Authorization": f"Bearer {api_key}"},
# json={"tags": tags}
# )
# return response.json()["description"]
return "DeepSeek 生成的描述(此处为示例伪代码)"
##############################################################################
# 2. 翻译示例函数(使用 GPT 或 DeepSeek)
##############################################################################
def translate_text_with_gpt(text, target_language, api_key, base_url=None, model="gpt-4"):
"""
使用 GPT 来进行翻译的简单示例。
"""
if not api_key:
return "Error: GPT Translation Key not provided."
openai.api_key = api_key
if base_url:
openai.api_base = base_url
try:
system_prompt = f"You are a professional translator. Translate the following text to {target_language}:"
response = openai.ChatCompletion.create(
model=model,
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": text},
]
)
return response.choices[0].message.content.strip()
except Exception as e:
return f"GPT translation failed. Error: {e}"
def translate_text_with_deepseek(text, target_language, api_key, base_url=None):
"""
使用 DeepSeek 来进行翻译的简单示例(伪代码)。
"""
if not api_key:
return "Error: DeepSeek Translation Key not provided."
# 同样需要根据 DeepSeek 的文档来实现
return f"DeepSeek翻译后的文本(示例)。目标语言:{target_language}"
##############################################################################
# 3. 根据用户选择进行提示词转换并调用 GPT/DeepSeek 生成描述
##############################################################################
def transform_prompt(prompt, gender_option, furry_species, api_mode, api_key):
"""
性别/物种转换的简单示例逻辑,然后调用相应 API。
你可在此处结合“关于Male/Female/ambiguous/intersex的details”添加更复杂的处理。
"""
tags = {}
# 根据选择设置性别或物种标签
if gender_option == "Trans_to_Male":
# 这里可以参考你的细节 rules 做更加复杂的转换
tags["gender"] = "male"
elif gender_option == "Trans_to_Female":
tags["gender"] = "female"
elif gender_option == "Trans_to_Mannequin":
# 无明显二性征
tags["gender"] = "genderless"
elif gender_option == "Trans_to_Intersex":
tags["gender"] = "intersex"
elif gender_option == "Trans_to_Furry":
tags["gender"] = "furry"
tags["furry_species"] = furry_species or "unknown"
# 原始提示词
tags["base_prompt"] = prompt
# 根据选择的 API 调用对应的函数
if api_mode == "GPT":
scene_description = generate_natural_language_description_gpt(tags, api_key)
else: # DeepSeek
scene_description = generate_natural_language_description_deepseek(tags, api_key)
return scene_description
##############################################################################
# 4. 调用翻译函数
##############################################################################
def do_translation(scene_desc, translate_language, api_mode, api_key):
"""
根据选择的 API(GPT/DeepSeek)进行翻译。
"""
if not scene_desc.strip():
return "" # 无内容则不翻译
if api_mode == "GPT":
return translate_text_with_gpt(scene_desc, translate_language, api_key)
else:
return translate_text_with_deepseek(scene_desc, translate_language, api_key)
##############################################################################
# 5. 搭建 Gradio 界面
##############################################################################
def build_interface():
with gr.Blocks() as demo:
gr.Markdown("## Prompts_TransTool-提示词一键性别物种转换器")
with gr.Row():
with gr.Column():
# 选择调用哪个 API
api_mode = gr.Radio(
label="选择 API 服务 (Choose API Service)",
choices=["GPT", "DeepSeek"],
value="GPT"
)
# 输入 API Key
api_key = gr.Textbox(
label="API 密钥 (API Key)",
type="password",
placeholder="请输入你的 GPT 或 DeepSeek API 密钥"
)
# 性别 / Furry 选择
gender_option = gr.Radio(
label="性别 / Furry 选项 (Gender / Furry)",
choices=[
"Trans_to_Male",
"Trans_to_Female",
"Trans_to_Mannequin",
"Trans_to_Intersex",
"Trans_to_Furry"
],
value="Trans_to_Male",
)
# 选择 Furry 物种
furry_species = gr.Dropdown(
label="Furry 物种 (Furry Species)",
choices=["Wolf", "Fox", "Tiger", "Lion"],
value=None,
visible=False # 初始不可见
)
# 当性别选项切换时,如果选择 Furry,就显示物种下拉,否则隐藏
def show_furry_species(gender):
return gr.update(visible=(gender == "Trans_to_Furry"))
gender_option.change(
show_furry_species,
inputs=[gender_option],
outputs=[furry_species]
)
with gr.Column():
# 输入 prompt
user_prompt = gr.Textbox(
label="提示词 (Prompt)",
lines=5,
placeholder=(
"Please Enter your prompt words. \n"
"在此输入你的提示词,例如:一位穿着红色连衣裙的少女,坐在落日余晖下的草地上..."
)
)
# 输出场景描述
generated_output = gr.Textbox(
label="转换后的提示词 (Generated Trans-Description)",
lines=7
)
# 翻译区域
with gr.Row():
translate_language = gr.Dropdown(
label="翻译语言 (Translation Language)",
# 可自行添加更多语言选项
choices=["English", "Chinese", "Japanese", "French", "German", "Dutch", "Arabic", "Russian", "Persian", "Italian"],
value="English",
)
translated_text = gr.Textbox(
label="翻译结果 (Translated Result)",
lines=7
)
######################################################################
# 事件绑定
######################################################################
# 新增:生成时,直接返回「转换结果」和「翻译结果」并一起更新
def on_generate(prompt, gender, furry, mode, key, lang):
# 1) 先做性别/物种转换,拿到“转换后”提示词
trans_desc = transform_prompt(prompt, gender, furry, mode, key)
# 2) 立刻翻译
trans_result = do_translation(trans_desc, lang, mode, key)
# 返回两项
return trans_desc, trans_result
# 当用户在 prompt 输入后按回车时,触发生成场景描述 + 翻译
user_prompt.submit(
fn=on_generate,
inputs=[user_prompt, gender_option, furry_species, api_mode, api_key, translate_language],
outputs=[generated_output, translated_text],
)
# 点击按钮也触发同样的逻辑
generate_button = gr.Button("生成 / Generate")
generate_button.click(
fn=on_generate,
inputs=[user_prompt, gender_option, furry_species, api_mode, api_key, translate_language],
outputs=[generated_output, translated_text],
)
# 当用户切换翻译语言时,如果已经有转换后的内容,则再翻译一次
def on_translate(scene_desc, lang, mode, key):
return do_translation(scene_desc, lang, mode, key)
# 注意:这里只更新“翻译结果”一项
translate_language.change(
fn=on_translate,
inputs=[generated_output, translate_language, api_mode, api_key],
outputs=[translated_text]
)
return demo
# 在 Spaces 启动
if __name__ == "__main__":
demo = build_interface()
demo.launch()