From c4deec0d3f094642fe84cda7307186d4bcd3ba5f Mon Sep 17 00:00:00 2001 From: "majiahui@haimaqingfan.com" Date: Thu, 31 Aug 2023 11:07:35 +0800 Subject: [PATCH] =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E5=8D=95=E5=8F=A5=E5=A4=9A?= =?UTF-8?q?=E7=AF=87=E5=B9=85=E6=A3=80=E6=B5=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- flask_check_bert_test.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/flask_check_bert_test.py b/flask_check_bert_test.py index 30b398a..a132420 100644 --- a/flask_check_bert_test.py +++ b/flask_check_bert_test.py @@ -808,7 +808,7 @@ def ulit_recall_paper(recall_data_list_dict): return data -def recall_10(title, abst_zh, content) -> dict: +def recall_10(queue_uuid, title, abst_zh, content) -> dict: ''' 宇鹏召回接口 :param paper_name: @@ -816,6 +816,7 @@ def recall_10(title, abst_zh, content) -> dict: ''' request_json = { + "uuid": queue_uuid, "title": title, "abst_zh": abst_zh, "content": content @@ -958,8 +959,8 @@ def classify(): # 调用模型，设置最大batch_size # 加载文件的对象 data_dict = json.load(f1) - query_id = data_dict['id'] - print(query_id) + queue_uuid = data_dict['id'] + print(queue_uuid) dataBases = data_dict['dataBases'] minSimilarity = data_dict['minSimilarity'] minWords = data_dict['minWords'] @@ -973,12 +974,11 @@ def classify(): # 调用模型，设置最大batch_size callbackUrl = data_dict['callbackUrl'] # 调用宇鹏查询相似十篇 - # recall_data_list_dict = recall_10(title, abst_zh, content) + recall_data_list_dict = recall_10(queue_uuid, title, abst_zh, content) - t1 = time.time() - print("查找相似的50篇完成") - with open("data/rell_json.txt") as f: - recall_data_list_dict = eval(f.read()) + # print("查找相似的50篇完成") + # with open("data/rell_json.txt") as f: + # recall_data_list_dict = eval(f.read()) # 读取文章转化成格式数据 recall_data_list = ulit_recall_paper(recall_data_list_dict)