app.py

from transformers import GPT2LMHeadModel, GPT2Tokenizer
import gradio as gr

# Load the pre-trained GPT2 model and tokenizer
model = GPT2LMHeadModel.from_pretrained("gpt2")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# 設置填充標記 ID
tokenizer.pad_token = tokenizer.eos_token

def generate_command(prompt, max_length=100):
    full_prompt = f"Generate a Bash command for {prompt}:```bash\n"
    inputs = tokenizer.encode(full_prompt, return_tensors="pt")
    
    output = model.generate(
        inputs, 
        max_length=max_length, 
        num_return_sequences=1, 
        temperature=0.7,
        pad_token_id=tokenizer.pad_token_id
    )
    generated_text = tokenizer.decode(output[0], skip_special_tokens=False)
    
    # 提取生成的指令
    start = generated_text.find("```bash") + len("```bash")
    end = generated_text.find("```", start)
    if end == -1:
        end = len(generated_text)
    command = generated_text[start:end].strip()
    
    return command

def predict(input_text):
    output = generate_command(input_text)
    return output

iface = gr.Interface(
    fn=predict,
    inputs=gr.Textbox(lines=2, placeholder="Enter your command generation prompt here..."),
    outputs="text",
    title="Command Generation with GPT2",
    description="Generate bash commands based on your input prompt."
)

iface.launch()
```

### 使用示例

1. **生成文件操作指令**：
   ```python
   prompt = "deleting a file"
   generated_command = generate_command(prompt)
   print(generated_command)
   ```

   可能的輸出：
   ```
   rm filename.txt
   ```

2. **生成網絡操作指令**：
   ```python
   prompt = "downloading a file from a URL"
   generated_command = generate_command(prompt)
   print(generated_command)
   ```

   可能的輸出：
   ```
   wget https://example.com/file.txt
   ```

3. **生成系統管理指令**：
   ```python
   prompt = "checking system memory usage"
   generated_command = generate_command(prompt)
   print(generated_command)