Update app.py

app.py

@@ -1,33 +1,25 @@
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
-import gradio as gr
-import spaces
-
-@spaces.GPU
-def dummy(): # just a dummy
-    pass
-
-# 模型和 tokenizer 的名称
-model_name = "./"  # 假设你有权限访问Llama 3.1 8B模型
 
 # 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]'})
-# 将模型移动到GPU（如果可用）
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model.to(device)
 
 # 定义生成函数
-def query(data):
-    inputs = tokenizer(data["inputs"], return_tensors="pt", padding=True, truncation=True).to(device)
+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
 
-# 测试用例：可以替换为动态输入
-data = '''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.
+# 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.
@@ -60,10 +52,11 @@ Transformer Models: These models, such as BERT and GPT, have revolutionized NLP
     '''
 
 
-# 直接调用生成函数并输出结果
-# 使用 Gradio 创建前端界面
-output = query(data)
+# 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)
+    f.write(output)
+
+print("Generated Summary saved to output.txt")