263 lines
9.9 KiB
Python
263 lines
9.9 KiB
Python
'''
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* @file DeepSeekPromptGenerator.py
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* @brief Generate optimized image prompts using DeepSeek API
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*
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* @author WuYingwen
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* @GitHub @wuyingwen10
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* @Contact 2211537@mail.nankai.edu.cn
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* @date 2025-06-10
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* @version v1.0.0
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*
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* @note
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* - Uses DeepSeek API for prompt optimization
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* - Supports multiple API configurations
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*
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* @copyright
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* (c) 2025 Nankai University
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'''
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import requests
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import json
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import os
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import sys
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from datetime import datetime
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SYSTEM_PROMPT_TEMPLATE = """
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As an AI prompt engineering expert, please generate a prompt that meets the following requirements based on the topic I provide. Note: You should follow the style of the example (emulating the writing style only, not the content) and transform the user's request into a detailed prompt.
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Requirements (strictly adhere to all six):
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1: Use simple natural language sentences to describe the scene. Avoid overly long or complex sentence structures. Do not use special symbols like *.
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2: Express in English.
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3: Provide only the prompt content without any explanations or notes.
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4: Each prompt must be at least 50 words but no more than 200 words.
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5: Avoid ambiguous expressions.
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6: Add "no text, no AI style" at the beginning.
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Example:
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no text, no AI style
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Cartoon-Style Nankai University Main Building,
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vividly depicted with rounded edges and pastel gradients, the iconic Gothic-Revival structure stands majestically. Crimson brick façade contrasts with golden-glowing arched windows, while whimsical cloud-shaped eaves drip melted clock details. A giant smiling sun hangs low, casting honey-golden rays through simplified pine trees, creating striped shadows dancing on marble stairs.
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Translucent ghostly scholars from 1919 float near pillars holding glowing books, their outlines shimmering like liquid mercury. Oversized autumn leaves (stylized as maple-red origami) spiral around twin bell towers chiming visible musical notes. Puddles on the ground mirror upside-down building reflections rippling with calculus formulas.
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Environment:
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Surreal candy-pink sunset gradients blend into starry indigo sky above. Playful squirrels wearing tiny graduation caps scamper across emerald lawns textured like green velvet.
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"""
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API_CONFIGS = [
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{
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"name": "DeepSeek Official Chat API",
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"model": "deepseek-chat",
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"api_key": "sk-36de33a3827f43398ed027e733dbd74a",
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"base_url": "https://api.deepseek.com"
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},
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{
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"name": "DeepSeek-R1 Backup",
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"model": "deepseek-r1",
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"api_key": "sk-36de33a3827f43398ed027e733dbd74a",
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"base_url": "https://cloud.infini-ai.com/maas/v1/"
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},
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{
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"name": "DeepSeek-V3 Backup",
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"model": "deepseek-v3",
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"api_key": "sk-36de33a3827f43398ed027e733dbd74a",
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"base_url": "https://cloud.infini-ai.com/maas/v1/"
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}
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]
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def generate_prompt(user_topic):
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"""Generate optimized prompt using DeepSeek API"""
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full_prompt = SYSTEM_PROMPT_TEMPLATE + f"\n\nTopic: {user_topic}"
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for config in API_CONFIGS:
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try:
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print(f"Attempting API: {config['name']}...")
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endpoint = f"{config['base_url']}/chat/completions"
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {config['api_key']}"
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}
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payload = {
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"model": config["model"],
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"messages": [{"role": "user", "content": full_prompt}],
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"temperature": 0.7,
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"max_tokens": 2000,
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"stream": False
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}
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response = requests.post(endpoint, headers=headers, json=payload, timeout=30)
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response.raise_for_status()
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result = response.json()
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return result['choices'][0]['message']['content'].strip()
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except Exception as e:
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print(f"Error with {config['name']}: {str(e)}")
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continue
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raise Exception("All API attempts failed")
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def save_deepseek_output(prompt_content, output_prefix):
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"""Save DeepSeek output to file with the same naming pattern as images"""
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output_dir = "../outputs/deepseek_output"
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os.makedirs(output_dir, exist_ok=True)
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timestamp = datetime.now().strftime("%Y%m%d%H%M%S%f")[:-3]
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filename = f"{output_prefix}_{timestamp}.txt"
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file_path = os.path.join(output_dir, filename)
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with open(file_path, "w", encoding="utf-8") as f:
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f.write(prompt_content)
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return file_path
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def test_multiple_topics():
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"""测试多个主题的提示词生成"""
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test_topics = [
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"设计一张南开大学的海报，要求风格为卡通风格，包含南开大学的标志性建筑和校园元素。",
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"创建一个关于人工智能与未来科技的海报",
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"设计一张关于环保主题的宣传海报"
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]
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print(f"{'-'*50}\n🧪 开始测试多个主题的提示词生成\n{'-'*50}")
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results = []
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for i, topic in enumerate(test_topics, 1):
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print(f"\n📝 测试主题 {i}: {topic}")
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try:
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start_time = datetime.now()
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prompt = generate_prompt(topic)
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end_time = datetime.now()
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# 计算执行时间
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execution_time = (end_time - start_time).total_seconds()
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# 保存提示词
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prefix = f"测试_{i}"
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output_file = save_deepseek_output(prompt, prefix)
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# 截断显示（如果太长）
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display_prompt = prompt[:200] + "..." if len(prompt) > 200 else prompt
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results.append({
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"topic": topic,
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"success": True,
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"time": execution_time,
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"output_file": output_file,
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"prompt_length": len(prompt)
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})
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print(f"✅ 成功生成提示词 ({len(prompt)} 字符, {execution_time:.2f}秒)")
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print(f"📄 预览: {display_prompt}")
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print(f"💾 已保存到: {output_file}")
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except Exception as e:
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print(f"❌ 生成失败: {str(e)}")
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results.append({
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"topic": topic,
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"success": False,
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"error": str(e)
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})
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# 打印汇总结果
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print(f"\n{'-'*50}\n📊 测试结果汇总\n{'-'*50}")
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success_count = sum(1 for r in results if r.get("success", False))
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print(f"✅ 成功: {success_count}/{len(test_topics)}")
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if success_count > 0:
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avg_time = sum(r["time"] for r in results if r.get("success", False)) / success_count
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avg_length = sum(r["prompt_length"] for r in results if r.get("success", False)) / success_count
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print(f"⏱️ 平均执行时间: {avg_time:.2f}秒")
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print(f"📏 平均提示词长度: {avg_length:.0f}字符")
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return results
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def test_api_configs():
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"""测试所有API配置"""
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print(f"{'-'*50}\n🧪 测试所有API配置\n{'-'*50}")
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test_topic = "测试API配置的简单主题"
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for i, config in enumerate(API_CONFIGS, 1):
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print(f"\n🔌 测试API配置 {i}: {config['name']}")
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print(f" 模型: {config['model']}")
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print(f" URL: {config['base_url']}")
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try:
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# 构建提示词
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full_prompt = SYSTEM_PROMPT_TEMPLATE + f"\n\nTopic: {test_topic}"
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# 准备请求
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endpoint = f"{config['base_url']}/chat/completions"
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {config['api_key']}"
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}
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payload = {
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"model": config["model"],
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"messages": [{"role": "user", "content": full_prompt}],
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"temperature": 0.7,
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"max_tokens": 100, # 减少token以加快测试
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"stream": False
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}
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# 发送请求
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start_time = datetime.now()
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print(f" 🕒 发送请求...")
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response = requests.post(endpoint, headers=headers, json=payload, timeout=10)
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end_time = datetime.now()
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execution_time = (end_time - start_time).total_seconds()
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# 检查响应
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response.raise_for_status()
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result = response.json()
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# 输出结果
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print(f" ✅ API工作正常! 响应时间: {execution_time:.2f}秒")
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print(f" 🔑 响应状态码: {response.status_code}")
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print(f" 📦 模型回复: {result['choices'][0]['message']['content'][:50]}...")
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except Exception as e:
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print(f" ❌ API测试失败: {str(e)}")
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def main():
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"""主函数，运行多个测试"""
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print(f"🚀 DeepSeek提示词生成器测试 - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
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# 运行标准测试
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user_topic = "设计一张南开大学的海报，要求风格为卡通风格，包含南开大学的标志性建筑和校园元素。"
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print(f"🔍 标准测试 - 主题: {user_topic}")
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try:
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prompt = generate_prompt(user_topic)
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output_file = save_deepseek_output(prompt, "南开大学海报")
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print(f"✅ 标准测试成功!")
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print(f"📄 生成的提示词: {prompt[:100]}...")
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print(f"💾 已保存到: {output_file}")
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except Exception as e:
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print(f"❌ 标准测试失败: {str(e)}")
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# 询问用户是否运行更多测试
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print("\n是否运行更多测试? (输入数字选择)")
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print("1. 测试多个主题")
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print("2. 测试API配置")
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print("3. 运行所有测试")
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print("0. 退出")
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choice = input("请选择 (默认0): ").strip() or "0"
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if choice == "1":
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test_multiple_topics()
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elif choice == "2":
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test_api_configs()
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elif choice == "3":
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test_multiple_topics()
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test_api_configs()
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else:
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print("退出测试。")
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if __name__ == "__main__":
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main() |