基于AIGC的自动化内容生成与应用

import openai

# 设置API密钥
openai.api_key = 'your-api-key'

# 调用GPT-4生成文本
response = openai.Completion.create(
    engine="gpt-4",
    prompt="写一篇关于人工智能未来发展的文章。",
    max_tokens=500,
    temperature=0.7
)

# 输出生成的文本
print(response.choices[0].text.strip())

from transformers import pipeline
import openai

# 设置API密钥
openai.api_key = 'your-api-key'

# 加载预训练的BERT模型
nlp = pipeline("question-answering")

# 问答函数
def ask_question(context, question):
    # 使用BERT进行问答
    answer_bert = nlp(question=question, context=context)['answer']
   
    # 使用GPT-4生成回答
    response = openai.Completion.create(
        engine="gpt-4",
        prompt=f"基于以下内容回答问题：\n{context}\n问题：{question}\n回答：{answer_bert}\n详细解释：",
        max_tokens=200,
        temperature=0.7
    )
    return response.choices[0].text.strip()
    
# 示例上下文和问题
context = "人工智能是指通过计算机模拟人类智能的技术。"
question = "什么是人工智能？"

# 获取回答
answer = ask_question(context, question)
print(answer)

import openai
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# 设置API密钥
openai.api_key = 'your-api-key'

# 示例内容生成
content_list = []
for i in range(5):
    response = openai.Completion.create(
        engine="gpt-4",
        prompt=f"生成第{i+1}个推荐内容。",
        max_tokens=100,
        temperature=0.7
    )
    content_list.append(response.choices[0].text.strip())
    
# 示例用户兴趣
user_interest = "我喜欢科技和人工智能方面的内容。"

# 内容推荐
vectorizer = TfidfVectorizer()
content_matrix = vectorizer.fit_transform(content_list + [user_interest])
similarity_scores = cosine_similarity(content_matrix[-1], content_matrix[:-1]).flatten()
recommended_content_index = similarity_scores.argmax()
recommended_content = content_list[recommended_content_index]

print("推荐的内容：", recommended_content)

import openai

# 设置API密钥
openai.api_key = 'your-api-key'

# 自然语言描述生成代码
response = openai.Completion.create(
    engine="code-davinci-002",
    prompt="编写一个Python函数，计算两个数的和。",
    max_tokens=100,
    temperature=0.7
)

# 输出生成的代码
print(response.choices[0].text.strip())

import torch
from torchvision.utils import save_image
from stylegan2_pytorch import Model

# 加载StyleGAN模型
model = Model.load('path/to/pretrained/model.pth')

# 生成图像
z = torch.randn(1, 512).cuda()
generated_image = model.generate(z)
save_image(generated_image, 'generated_image.png')

import openai
from transformers import pipeline
import torch
from torchvision.utils import save_image
from stylegan2_pytorch import Model

# 设置API密钥
openai.api_key = 'your-api-key'

# 文本生成
def generate_text(prompt):
    response = openai.Completion.create(
        engine="gpt-4",
        prompt=prompt,
        max_tokens=100,
        temperature=0.7
    )
    return response.choices[0].text.strip()
    
# 文本理解
def analyze_sentiment(text):
    nlp = pipeline("sentiment-analysis")
    result = nlp(text)
    return result[0]['label']
    
# 图像生成
def generate_image():
    model = Model.load('path/to/pretrained/model.pth')
    z = torch.randn(1, 512).cuda()
    generated_image = model.generate(z)
    save_image(generated_image, 'generated_image.png')
    return 'generated_image.png'
    
# 综合应用示例
def main():
    prompt = "生成一段关于人工智能的介绍。"
    text = generate_text(prompt)
    sentiment = analyze_sentiment(text)
    image_path = generate_image()
    
    print("生成的文本：", text)
    print("情感分析结果：", sentiment)
    print("生成的图像保存于：", image_path)
    
if __name__ == "__main__":
    main()