app.py

import gradio as gr
import random
import openai
import os

# ========== 默认选项和数据 ==========
EXPRESSIONS = ["smiling", "determined", "surprised", "serene"]
ITEMS = ["magic wand", "sword", "flower", "book of spells", "ancient scroll", "music instrument"]
OTHER_DETAILS = ["sparkles", "magical aura", "lens flare", "fireworks in the background"]
SCENES = ["sunset beach", "rainy city street at night", "fantasy forest with glowing mushrooms", "futuristic skyline at dawn"]
CAMERA_ANGLES = ["low-angle shot", "close-up shot", "bird's-eye view", "wide-angle shot"]
QUALITY_PROMPTS = ["8k", "ultra-realistic", "high detail", "cinematic lighting", "award-winning"]

# ========== 工具函数 ==========
def load_candidates_from_files(files):
    """
    从多个文件中加载候选项。
    """
    all_lines = []
    if files:
        for file in files:
            # 如果 `file` 是路径
            if isinstance(file, str):
                with open(file, "r", encoding="utf-8") as f:
                    all_lines.extend([line.strip() for line in f if line.strip()])
            # 如果 `file` 是 Gradio 的文件对象
            elif hasattr(file, "name"):
                with open(file.name, "r", encoding="utf-8") as f:
                    all_lines.extend([line.strip() for line in f if line.strip()])
    return all_lines

def get_random_item(candidates):
    """
    随机选取候选项。
    """
    return random.choice(candidates) if candidates else ""

def generate_natural_language_description(tags, api_key=None):
    """
    生成自然语言描述，可以选择使用 GPT 或手写逻辑。
    """
    if api_key:
        try:
            openai.api_key = api_key.strip()
            response = openai.ChatCompletion.create(
                model="gpt-4",
                messages=[
                    {"role": "system", "content": (
                        "You are a helpful assistant that generates creative painting/prompt descriptions. "
                        "Write at least three sentences in English, separated by periods."
                    )},
                    {"role": "user", "content": f"Here are the tags: {tags}\nPlease generate a vivid, imaginative scene description."}
                ],
                max_tokens=300,
                temperature=0.7,
            )
            return response["choices"][0]["message"]["content"].strip()
        except Exception as e:
            return f"GPT generation failed. Error: {e}"
    else:
        # 本地逻辑生成
        return (
            f"In this scene, {tags.get('character_name', 'the character')} appears in a {tags.get('scene', 'mysterious place')}, "
            f"captured from a {tags.get('camera_angle', 'unique angle')} perspective. "
            f"The overall style combines {tags.get('artist_prompt', 'an unknown artist')} and {tags.get('style', 'a distinctive aesthetic')}, "
            f"bringing a captivating atmosphere to the artwork. They are {tags.get('action', 'doing something')} "
            f"with a {tags.get('expression', 'neutral expression')}, holding {tags.get('items', 'something')} among {tags.get('other_details', 'subtle details')}."
        )

def generate_prompt(action_file, style_file, artist_files, character_files, api_key, selected_categories):
    """
    生成随机提示词和描述。
    """
    # 从文件加载候选项
    actions = load_candidates_from_files([action_file]) if action_file else []
    styles = load_candidates_from_files([style_file]) if style_file else []
    artists = load_candidates_from_files(artist_files) if artist_files else []
    characters = load_candidates_from_files(character_files) if character_files else []

    # 确定角色类型
    number_of_characters = ", ".join(selected_categories) if selected_categories else random.choice(["1girl", "1boy"])

    # 随机生成提示词
    tags = {
        "number_of_characters": number_of_characters,
        "character_name": get_random_item(characters),
        "artist_prompt": get_random_item(artists),
        "style": get_random_item(styles),
        "scene": get_random_item(SCENES),
        "camera_angle": get_random_item(CAMERA_ANGLES),
        "action": get_random_item(actions),
        "expression": get_random_item(EXPRESSIONS),
        "items": get_random_item(ITEMS),
        "other_details": get_random_item(OTHER_DETAILS),
        "quality_prompts": get_random_item(QUALITY_PROMPTS),
    }

    # 生成描述
    description = generate_natural_language_description(tags, api_key)

    # 返回结果
    tags_list = [value for value in tags.values() if value]
    final_tags = ", ".join(tags_list)
    return final_tags, description

# ========== Gradio 界面 ==========
def gradio_interface():
    """
    定义 Gradio 应用界面。
    """
    with gr.Blocks() as demo:
        gr.Markdown("## Random Prompt Generator with User-Provided GPT API Key")

        # API Key 输入区
        api_key_input = gr.Textbox(
            label="Enter your OpenAI API Key (Optional)",
            placeholder="sk-...",
            type="password"
        )

        # 文件上传
        with gr.Row():
            action_file = gr.File(label="Upload Action File (Optional)", file_types=[".txt"])
            style_file = gr.File(label="Upload Style File (Optional)", file_types=[".txt"])
        
        with gr.Row():
            artist_files = gr.File(label="Upload Artist Files (Multiple Allowed)", file_types=[".txt"], file_types_description="Text Files", multiple=True)
            character_files = gr.File(label="Upload Character Files (Multiple Allowed)", file_types=[".txt"], file_types_description="Text Files", multiple=True)

        # 角色类型选择
        selected_categories = gr.CheckboxGroup(
            ["1boy", "1girl", "furry", "mecha", "fantasy monster", "animal", "still life"],
            label="Choose Character Categories (Optional)"
        )

        # 输出区域
        with gr.Row():
            tags_output = gr.Textbox(label="Generated Tags")
            description_output = gr.Textbox(label="Generated Description")

        # 按钮
        generate_button = gr.Button("Generate Prompt")

        # 按钮动作
        generate_button.click(
            generate_prompt,
            inputs=[action_file, style_file, artist_files, character_files, api_key_input, selected_categories],
            outputs=[tags_output, description_output],
        )

    return demo

# 启动 Gradio 应用
if __name__ == "__main__":
    gradio_interface().launch(share=True)