from paddlenlp.transformers import ErnieForTokenClassification, ErnieTokenizer
import paddle

# 1. 加载模型和 tokenizer
model_path = R"E:\workingSpace\PycharmProjects\Intention_dev\uie\output\checkpoint-16920"  # 你的模型路径
model = ErnieForTokenClassification.from_pretrained(model_path)
tokenizer = ErnieTokenizer.from_pretrained(model_path)

# 2. 处理输入文本
text = "今天杨柳220kV变电站220kV南坪间隔扩建工程有多少作业计划"
inputs = tokenizer(text, max_len=512, return_tensors="pd")

# 3. 进行预测
model.eval()
with paddle.no_grad():
    logits = model(**inputs)
    predictions = paddle.argmax(logits, axis=-1)

# 4. 标签映射
label_map = {
    0: 'O',  # 非实体
    1: 'B-date', 12: 'I-date',
    2: 'B-project_name', 13: 'I-project_name',
    3: 'B-project_type', 14: 'I-project_type',
    4: 'B-construction_unit', 15: 'I-construction_unit',
    5: 'B-implementation_organization', 16: 'I-implementation_organization',
    6: 'B-project_department', 17: 'I-project_department',
    7: 'B-project_manager', 18: 'I-project_manager',
    8: 'B-subcontractor', 19: 'I-subcontractor',
    9: 'B-team_leader', 20: 'I-team_leader',
    10: 'B-risk_level', 21: 'I-risk_level',
    11: 'B-page', 22: 'I-page',
}

# 5. 解析预测结果
predicted_labels = predictions.numpy()[0]
tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0].numpy())

entities = []
current_entity = None
current_label = None

for token, label_id in zip(tokens, predicted_labels):
    label = label_map.get(label_id, "O")

    if label.startswith("B-"):  # 开始新实体
        if current_entity:
            entities.append({"text": "".join(current_entity), "label": current_label})
        current_entity = [token]
        current_label = label[2:]  # 去掉 B-

    elif label.startswith("I-") and current_entity and label[2:] == current_label:
        current_entity.append(token)  # 继续合并同一实体

    else:  # 非实体
        if current_entity:
            entities.append({"text": "".join(current_entity), "label": current_label})
            current_entity = None
            current_label = None

# 处理最后一个实体
if current_entity:
    entities.append({"text": "".join(current_entity), "label": current_label})

# 输出最终的实体
for entity in entities:
    print(f"Entity: {entity['text']}, Label: {entity['label']}")