app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import os

# 加载模型和分词器，使用环境变量中的令牌
model_name = "meta-llama/Llama-2-7b-hf"
token = os.getenv("HF_TOKEN")  # 从环境变量中获取 Token

# 检查是否可以使用 GPU
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")

try:
    tokenizer = AutoTokenizer.from_pretrained(model_name, token=token)
    model = AutoModelForCausalLM.from_pretrained(
        model_name, 
        device_map="auto",  # 自动分配设备
        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
        token=token
    )
    print("Model and tokenizer loaded successfully.")
except Exception as e:
    print(f"Error loading model or tokenizer: {e}")
    raise e

# 定义生成功能的教学内容，保证可重复性

def generate_content_with_parameters(prompt, temperature, max_length):
    try:
        # 将输入移动到模型所在设备
        inputs = tokenizer(prompt, return_tensors="pt", truncation=True).to(device)

        # 生成文本
        outputs = model.generate(
            **inputs,
            max_length=max_length,
            temperature=temperature,  # 格式化传入的温度
            num_return_sequences=1,
            repetition_penalty=1.2  # 增加重复惩罚
        )

        # 解码输出
        return tokenizer.decode(outputs[0], skip_special_tokens=True)
    except Exception as e:
        return f"Error during generation: {e}"

# 创建 Gradio 界面
interface = gr.Interface(
    fn=generate_content_with_parameters,
    inputs=[
        gr.Textbox(label="Prompt", placeholder="Enter your prompt here."),
        gr.Slider(label="Temperature", minimum=0, maximum=1, step=0.1, value=0.7),
        gr.Slider(label="Max Length", minimum=10, maximum=2048, step=10, value=512),
    ],
    outputs="text",
    title="Customizable Text Generator",
    description="Enter a prompt, adjust the temperature and max length, and generate consistent outputs.",
)

# 启动 Gradio 应用
if __name__ == "__main__":
    interface.launch()