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app.py

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+# -*- coding: utf-8 -*-
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import PeftModel
+import torch
+
+# --- 配置 (Configuration) ---
+
+# 您上传到Hub的仓库ID (基础模型 + LoRA适配器)
+# Your repository ID on the Hugging Face Hub (containing the LoRA adapter)
+hub_repo_id = "yxccai/text-style"
+
+# ==============================================================================
+# !! 性能问题的核心原因与解决方案 !!
+# !! CORE PERFORMANCE ISSUE & SOLUTIONS !!
+#
+# 您遇到的5分钟耗时问题，是因为在免费的Hugging Face Space (CPU环境)上运行了
+# 一个1.8B参数的大模型。CPU进行这种规模的计算非常缓慢。
+# The 5-minute delay is caused by running a large 1.8B parameter model on a
+# free Hugging Face Space, which uses a CPU. CPU inference for a model of this
+# size is inherently very slow.
+#
+# --- 解决方案 (Solutions) ---
+#
+# 1. (推荐/免费) 使用更小的基础模型 (Recommended/Free):
+#    将下面的 `base_model_name` 从 "Qwen/Qwen1.5-1.8B-Chat"
+#    更改为 "Qwen/Qwen1.5-0.5B-Chat"。这是最直接有效的免费解决方案。
+#    Change `base_model_name` below from "Qwen/Qwen1.5-1.8B-Chat" to
+#    "Qwen/Qwen1.5-0.5B-Chat". This is the most effective free solution.
+#
+# 2. (最佳性能) 升级到GPU硬件 (Best Performance):
+#    在Hugging Face Spaces的设置中，将硬件从免费的CPU升级到付费的GPU
+#    （例如 T4 small）。这将提供数量级的速度提升。
+#    In your Hugging Face Space settings, upgrade the hardware from the free CPU
+#    to a paid GPU (e.g., T4 small). This will provide a massive speed-up.
+#
+# 3. (进阶) 使用量化 (Advanced):
+#    加载模型时使用4位或8位量化可以减少内存占用并加速CPU推理，但这会略微
+#    牺牲模型精度。这需要更复杂的代码，如下面的 quantization_config 所示。
+#    Using 4-bit or 8-bit quantization can reduce memory usage and speed up CPU
+#    inference, at a slight cost to model precision. This requires more complex
+#    code, like the `quantization_config` shown below.
+# ==============================================================================
+
+# --- 应用解决方案1：使用更小的模型 ---
+# Applying Solution 1: Use a smaller model
+base_model_name = "Qwen/Qwen1.5-0.5B-Chat" # <--- 已修改为0.5B版本以提速
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Gradio App: Using device: {device}")
+
+# --- (可选) 量化配置示例 (Optional) Quantization Config Example ---
+# 如果您想尝试方案3，可以取消下面的注释。注意：这在CPU上可能仍然不够快。
+# If you want to try Solution 3, you can uncomment the following lines.
+# Note: This might still not be fast enough on a CPU.
+# bnb_config = BitsAndBytesConfig(
+#     load_in_4bit=True,
+#     bnb_4bit_use_double_quant=True,
+#     bnb_4bit_quant_type="nf4",
+#     bnb_4bit_compute_dtype=torch.bfloat16
+# )
+
+# --- 加载模型和Tokenizer (Loading Model and Tokenizer) ---
+print(f"Gradio App: Loading base model: {base_model_name}")
+# 1. 加载基础模型 (Load base model)
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_name,
+    torch_dtype="auto",
+    device_map="auto", # 使用 "auto" 让transformers库自动处理设备映射
+    trust_remote_code=True
+    # quantization_config=bnb_config # 如果使用量化，请添加此行
+)
+# base_model.to(device) # device_map="auto" 时不需要手动 .to(device)
+
+print(f"Gradio App: Loading tokenizer from: {hub_repo_id}")
+# 2. 加载Tokenizer (Load Tokenizer)
+tokenizer = AutoTokenizer.from_pretrained(hub_repo_id, trust_remote_code=True)
+if tokenizer.pad_token is None:
+    print("Gradio App: Tokenizer does not have a pad_token, setting it to eos_token.")
+    tokenizer.pad_token = tokenizer.eos_token
+    base_model.config.pad_token_id = tokenizer.eos_token_id
+
+print(f"Gradio App: Loading LoRA adapter from: {hub_repo_id}")
+# 3. 加载并应用LoRA适配器 (Load and apply LoRA adapter)
+model = PeftModel.from_pretrained(base_model, hub_repo_id)
+
+model.eval() # 设置为评估模式 (Set to evaluation mode)
+print("Gradio App: Model and tokenizer loaded successfully.")
+
+# --- 推理函数 (Inference Function) ---
+def chat(input_text):
+    if not input_text:
+        return "请输入一些文本。"
+
+    print(f"Gradio App: Received input: {input_text}")
+    # 构建符合Qwen Chat模板的输入
+    # Construct input according to the Qwen Chat template
+    messages = [
+        {"role": "system", "content": "你是一个文本风格转换助手。请严格按照要求，仅将以下书面文本转换为自然、口语化的简洁表达方式，不要添加任何额外的解释、扩展信息或重复原文。"},
+        {"role": "user", "content": input_text}
+    ]
+
+    try:
+        prompt = tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True
+        )
+    except Exception as e:
+        print(f"Error applying chat template: {e}")
+        # 如果模板应用失败，提供一个明确的错误信息
+        return f"错误：无法应用聊天模板。{e}"
+
+    print(f"Gradio App: Formatted prompt for model:\n{prompt}")
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1024).to(device)
+
+    # 生成时禁用梯度计算以节省资源
+    # Disable gradient calculations during generation to save resources
+    with torch.no_grad():
+        generated_ids = model.generate(
+            **inputs,
+            max_new_tokens=512,  # 减少最大生成长度以加快响应
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.95,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    # 从生成结果中提取回复
+    # Extract the reply from the generated result
+    response_ids = generated_ids[0][inputs.input_ids.shape[-1]:]
+    result = tokenizer.decode(response_ids, skip_special_tokens=True).strip()
+
+    print(f"Gradio App: Extracted result: {result}")
+    return result
+
+# --- 创建Gradio界面 (Create Gradio Interface) ---
+iface = gr.Interface(
+    fn=chat,
+    inputs=gr.Textbox(lines=5, label="输入书面文本 (Input Formal Text)"),
+    outputs=gr.Textbox(lines=5, label="输出口语化文本 (Output Casual Text)"),
+    title="文本风格转换器 (Text Style Converter)",
+    description="输入一段书面化的中文文本，模型会尝试将其转换为更自然、口语化的表达方式。由Qwen-0.5B模型微调。(已优化速度)",
+    examples=[
+        ["乙醇的检测方法包括以下几项: 1. 酸碱度检查:取20ml乙醇加20ml水,加2滴酚酞指示剂应无色,再加1ml 0.01mol/L氢氧化钠应显粉红色."],
+        ["本公司今日发布了最新的财务业绩报告，数据显示本季度利润实现了显著增长。"]
+    ],
+    allow_flagging="never" # 禁用flagging以简化界面
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+transformers
+torch
+accelerate
+peft
+gradio
+bitsandbytes # 如果您的基础Qwen模型加载时或PEFT需要，请取消注释
+sentencepiece # Qwen tokenizer 可能需要