app.py

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import spaces

@spaces.GPU
def dummy(): # just a dummy
    pass

# 加载模型和tokenizer
model_name = "./"  # 假设模型文件在根目录
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)


tokenizer.add_special_tokens({'pad_token': '[PAD]'})
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# 定义生成函数
def query(inputs):
    # 将输入字符串转化为token并传递给模型
    inputs = tokenizer(inputs, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(inputs["input_ids"], attention_mask=inputs["attention_mask"], max_new_tokens=400)
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return result

# 自动化输入
input_text = '''You are an expert in analyzing and summarizing use cases of large language models. You will be provided with a title, description, and content of a news article. Based on the human goals and outcomes, summarize in detail how LLM is used and who the end users are, paying attention to their profession or role. If the human goals or occupation information of end users is not clear, just say you don't know. The title and description are crucial for context. Your summary should integrate both the usage and occupation information clearly in at least 3 sentences.
    
    Article Title: Unleashing the Power of Generative AI in Content Creation and Marketing
    Article Description: Explore the world of generative AI in content creation, from understanding the models to its applications in marketing and content strategy.
    Article Content: The Rise of Generative AI in Content Creation: Revolutionizing the Digital Landscape
The digital age has witnessed an unprecedented demand for content creation, driven by the ever-growing need for fresh and engaging material to capture audiences' attention. In response to this demand, generative AI has emerged as a cutting-edge technology that helps content teams streamline their workflows, enhance creativity, and generate innovative ideas. This section explores the transformative impact of generative AI on content creation and how it is reshaping the digital landscape.
Contents
The Rise of Generative AI in Content CreationUnderstanding the Models Behind Generative AIThe Art of Crafting Prompts for AIGenerative AI in Marketing and Content Strategy
4.1 Generating Custom Marketing Copy4.2 Creating Images for Websites4.3 Writing Product Descriptions and Blog Posts4.4 Repackaging Existing Content into Full Campaigns4.5 Translating Ideas into Different LanguagesLimitations of Generative AI in Content OperationsAddressing Bias and Plagiarism ConcernsGoogle's Stance on AI-Generated ContentIncorporating AI into Marketing TeamsThe Future of Generative AI in Content CreationWhat is Generative AI?
Generative AI refers to a subset of artificial intelligence that can autonomously create new content in various formats, such as art, writing, video, or audio, based on human inputs. By leveraging advanced natural language processing models and machine learning techniques, generative AI consumes vast amounts of content to identify patterns, complete thoughts, or repackage ideas provided by humans. This technology is revolutionizing the way content is created and consumed, making it more accessible and efficient for businesses and individuals alike.
The Growing Popularity of Generative AI
The rise of generative AI can be attributed to several factors, including:
Increasing Content Demand: As the internet continues to grow, the need for diverse and engaging content has skyrocketed. Generative AI provides an effective solution to meet this demand by automating content creation processes and enabling teams to produce content at scale.
Technological Advancements: The continuous development of natural language processing models and machine learning algorithms has enabled generative AI to better understand context, sentiment, and even humour. This results in AI-generated content that is not only relevant but also engaging for audiences.
Time and Cost Efficiency: Generative AI allows content teams to save time and resources by automating repetitive tasks and generating new ideas, enabling them to focus on more strategic and creative aspects of content creation.
Customization and Personalization: Generative AI can tailor content to specific audiences or preferences, resulting in personalized experiences that resonate with users and foster stronger connections between brands and their customers.

Real-world Applications of Generative AI in Content Creation
Generative AI has been successfully applied across various content creation domains, including:
Journalism: News organizations have started using generative AI to create news articles or summaries, allowing them to deliver information more quickly and efficiently.
Creative Writing: Authors and screenwriters are leveraging AI to generate plot ideas, dialogue, and even entire stories, helping them overcome writer's block and explore new creative directions.
Advertising and Marketing: Generative AI is being used to create targeted ad campaigns, social media content, and promotional materials, enabling businesses to connect with their audiences more effectively.
Entertainment: AI-generated music, movies, and video games are gaining popularity as creators explore the potential of generative AI to produce innovative and immersive experiences.
As generative AI continues to evolve, its impact on content creation is expected to grow exponentially, paving the way for a new era of creativity and productivity in the digital landscape.
Understanding the Models Behind Generative AI: The Foundation for Effective Content Creation
Generative AI is built upon a foundation of powerful computational models that enable it to create content that is both engaging and relevant. This section delves into the key models used in generative AI, including natural language processing (NLP) and artificial neural networks (ANN), and explains how they work together to produce high-quality content.
Natural Language Processing (NLP)
Natural language processing(NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human language. NLP models enable AI systems to understand, interpret, and generate human language by learning from existing text using linguistic rules and patterns. Some of the most advanced NLP models include:
Transformer Models: These models, such as BERT and GPT, have revolutionized NLP by using self-attention mechanisms to process and gen
    Please provide a summary explaining both how the LLM is used and detailing the occupation or roles of the end users who benefit from or are expected to use the LLM.
    '''

# 调用生成函数并获取输出
output = query(input_text)

# 将生成的结果保存到 output.txt 文件
with open("output.txt", "w") as f:
    f.write(output)

print("Generated Summary saved to output.txt")

# 无限循环保持应用运行，避免退出
while True:
    pass