Create app.py

app.py

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+import json
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import xgrammar as xgr
+
+# ----------------------------
+# 模型、XGrammar初始化
+# ----------------------------
+# 注意：模型名称可以根据你的实际情况替换为合适的模型（建议使用较小模型测试，正式场景可换大模型）
+model_name = "Qwen/Qwen1.5-0.5B-Chat"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+print("Loading tokenizer and model...")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
+model.config.pad_token_id = tokenizer.eos_token_id  # 设置 pad_token_id 避免警告
+
+# 初始化 XGrammar 的基本组件
+tokenizer_info = xgr.TokenizerInfo.from_huggingface(tokenizer, vocab_size=model.config.vocab_size)
+grammar_compiler = xgr.GrammarCompiler(tokenizer_info)
+
+# 默认 JSON schema（以 Person 结构为示例）
+default_schema = {
+    "type": "object",
+    "properties": {
+        "name": {"type": "string"},
+        "age": {"type": "integer"}
+    },
+    "required": ["name", "age"]
+}
+default_schema_text = json.dumps(default_schema, indent=2)
+
+
+# ----------------------------
+# 主转换函数
+# ----------------------------
+def convert_xml_to_json(xml_input: str, schema_input: str) -> str:
+    # 如果用户未提供 JSON schema，则使用默认 schema
+    if not schema_input.strip():
+        schema_str = default_schema_text
+    else:
+        schema_str = schema_input.strip()
+
+    # 尝试加载 JSON schema
+    try:
+        schema = json.loads(schema_str)
+    except Exception as e:
+        return f"JSON schema 解析错误：{str(e)}"
+
+    # 编译 JSON schema 为 XGrammar Grammar
+    try:
+        compiled_grammar = grammar_compiler.compile_json_schema(schema)
+    except Exception as e:
+        return f"编译 JSON schema 出错：{str(e)}"
+
+    # 构造 XGrammar 的 logits processor
+    logits_processor = xgr.contrib.hf.LogitsProcessor(compiled_grammar)
+
+    # 构造转换提示，要求 LLM 解析 XML 并输出符合 schema 的 JSON
+    prompt = (
+        "You are a JSON converter that converts XML data to a structured JSON object.\n"
+        "The output must strictly conform to the following JSON schema (and nothing else):\n\n"
+        f"{schema_str}\n\n"
+        "Convert the following XML to JSON:\n"
+        f"{xml_input}\n\n"
+        "Output:"
+    )
+
+    # 编码 prompt
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    # 调用 generate，并传入 XGrammar logits processor，使生成过程中非法 token 被屏蔽
+    generated_ids = model.generate(
+        **inputs,
+        max_new_tokens=256,
+        logits_processor=[logits_processor],
+        pad_token_id=tokenizer.eos_token_id,
+    )
+    # 提取生成部分
+    output_text = tokenizer.decode(
+        generated_ids[0][inputs.input_ids.shape[1]:],
+        skip_special_tokens=True
+    )
+    return output_text.strip()
+
+
+# ----------------------------
+# 构建 Gradio 界面
+# ----------------------------
+title = "📄 XML to JSON Converter with XGrammar Structure Check"
+description = (
+    "将任意 XML 转换为 JSON。\n\n"
+    "在左侧粘贴 XML 文本，并可选地提供 JSON schema（如果留空，则使用默认结构，示例 schema 为 Person 模式）；\n"
+    "系统将调用 LLM 将 XML 转为 JSON，同时利用 XGrammar 限制输出结构，确保生成的 JSON 严格符合 schema。"
+)
+
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"## {title}\n\n{description}")
+    
+    with gr.Row():
+        with gr.Column():
+            xml_input = gr.Textbox(lines=12, label="XML 输入", placeholder="在此粘贴 XML 内容…")
+            schema_input = gr.Textbox(lines=8, label="JSON Schema（可选）", value=default_schema_text,
+                                        placeholder="可提供自定义 JSON schema，否则使用默认 schema")
+            convert_btn = gr.Button("转换 XML → JSON")
+        with gr.Column():
+            json_output = gr.Textbox(lines=12, label="生成的结构化 JSON")
+    
+    convert_btn.click(
+        fn=convert_xml_to_json,
+        inputs=[xml_input, schema_input],
+        outputs=json_output
+    )
+
+if __name__ == "__main__":
+    demo.launch()