510 lines
23 KiB
Python
510 lines
23 KiB
Python
#!/usr/bin/env python3
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"""
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听力-P1-图片选择题 生产脚本
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QSID: 121301, 121401, 121501
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"""
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import json, subprocess, copy
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APP_TOKEN = "CMHSbUUjka3TrUsaxxEc297ongf"
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TABLE_ID = "tbliZAhcc9C43B23"
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def get_token():
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CRED_FILE = "/root/.openclaw/credentials/xiaoyan/config.json"
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with open(CRED_FILE) as f:
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cred = json.load(f)
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app_id = cred['apps'][0]['appId']
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app_secret = cred['apps'][0]['appSecret']
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r = subprocess.run(["curl", "-s", "-X", "POST",
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"https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal",
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"-H", "Content-Type: application/json",
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"-d", json.dumps({"app_id": app_id, "app_secret": app_secret})],
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capture_output=True, text=True)
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return json.loads(r.stdout)['tenant_access_token']
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def create_record(token, fields):
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body = json.dumps({"fields": fields}, ensure_ascii=False)
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r = subprocess.run(["curl", "-s", "-X", "POST",
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f"https://open.feishu.cn/open-apis/bitable/v1/apps/{APP_TOKEN}/tables/{TABLE_ID}/records",
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"-H", f"Authorization: Bearer {token}",
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"-H", "Content-Type: application/json; charset=utf-8",
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"-d", body],
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capture_output=True, text=True)
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return json.loads(r.stdout)
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# ============================================================
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# Question data: (listening_text, imgA_desc, imgB_desc, imgC_desc, question_text, answer_idx, ability, explanation)
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# ============================================================
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# 121301 题组1: home, house, sofa, cupboard, bedroom
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QS_121301_FIRST = [
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(
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"Lily: This is my home. It has a red door and two windows.",
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"红门、两个窗户的独立房屋。卡通风格。",
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"蓝门、一个窗户的独立房屋。卡通风格。",
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"红门的公寓大楼。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜颜色与数量匹配"],
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"听力文本描述'red door and two windows'（红门和两扇窗），选项A的房屋具有红门和两个窗户，与描述完全一致。选项B门是蓝色且只有一扇窗，选项C是公寓大楼而非独立房屋。"
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),
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(
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"Lily: I can see a big house. It has a garden in front.",
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"前面有花园的大房子。卡通风格。",
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"没有花园的小房子。卡通风格。",
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"前面有花园的大楼。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜特征匹配"],
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"听力文本描述'big house with a garden'（有大花园的房子），选项A的大房子前面有花园，与描述一致。选项B没有花园，选项C是大楼而非房子。"
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),
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(
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"Lily: This is our sofa. It is big and blue.",
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"蓝色的大沙发。卡通风格。",
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"红色的小沙发。卡通风格。",
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"蓝色的大床。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜物品与颜色匹配"],
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"听力文本描述'big and blue sofa'（蓝色大沙发），选项A是蓝色大沙发，与描述一致。选项B是红色小沙发，选项C是蓝色大床而非沙发。"
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),
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(
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"Lily: The cupboard is full of cups. It is brown.",
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"放满杯子的棕色橱柜。卡通风格。",
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"放满盘子的白色橱柜。卡通风格。",
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"放满书的棕色书架。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜物品与颜色匹配"],
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"听力文本描述'brown cupboard with cups'（放满杯子的棕色橱柜），选项A的棕色橱柜里放着杯子，与描述一致。选项B是白色且放盘子，选项C是书架而非橱柜。"
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),
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(
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"Lily: My bedroom has a small bed and a yellow lamp.",
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"有小床和黄色台灯的卧室。卡通风格。",
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"有大床和白色台灯的卧室。卡通风格。",
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"有大沙发和黄色台灯的客厅。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜房间特征匹配"],
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"听力文本描述'bedroom with small bed and yellow lamp'（有小床和黄色台灯的卧室），选项A的卧室有小床和黄色台灯，与描述一致。选项B床大且台灯白，选项C是客厅不是卧室。"
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),
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]
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# 121301 题组2: zoo, hippo, bear, live
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QS_121301_SECOND = [
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(
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"Lily: We go to the zoo on Sundays. I can see many animals there.",
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"有各种动物的大动物园。卡通风格。",
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"有许多商店的购物中心。卡通风格。",
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"有许多树的公园。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜地点匹配"],
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"听力文本描述'go to the zoo and see animals'（去动物园看动物），选项A的动物园有各种动物，与描述一致。选项B是购物中心，选项C是公园。"
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),
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(
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"Lily: Look! The hippo is big. It is in the water.",
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"泡在水里的大河马。卡通风格。",
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"站在草地上的大河马。卡通风格。",
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"泡在水里的大象。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜动物与场景匹配"],
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"听力文本描述'hippo in the water'（泡在水里的河马），选项A是泡在水里的河马，与描述一致。选项B的河马在草地上，选项C是大象而非河马。"
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),
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(
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"Lily: I can see a brown bear. It is big and strong.",
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"棕色的大熊。卡通风格。",
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"棕色的小狗。卡通风格。",
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"白色的大熊。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜动物特征匹配"],
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"听力文本描述'brown bear, big and strong'（棕色大熊，又大又壮），选项A是棕色大熊，与描述一致。选项B是小狗，选项C是白熊。"
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),
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(
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"Lily: The hippo and the bear live in the zoo. They are happy.",
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"河马和熊在一起。卡通风格。",
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"河马和老虎在一起。卡通风格。",
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"大象和熊在一起。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜多对象匹配"],
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"听力文本提到'hippo and bear live in the zoo'（河马和熊住在动物园），选项A中河马和熊在一起，与描述一致。选项B有老虎，选项C有大象。"
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),
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(
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"Lily: I love the zoo. The animals live here.",
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"动物园里有许多动物。卡通风格。",
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"农场里有许多动物。卡通风格。",
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"公园里有许多人。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜地点匹配"],
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"听力文本描述'zoo where animals live'（动物居住的动物园），选项A是动物园，与描述一致。选项B是农场，选项C是公园。"
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),
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]
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# 121401 题组1: study, play, art, spell, letter, English (pick 5)
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QS_121401_FIRST = [
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(
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"Lily: I study English every day. I read books and write words.",
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"正在看英文书和写字的小女孩。卡通风格。",
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"正在画画的小女孩。卡通风格。",
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"正在玩耍的小女孩。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜活动匹配"],
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"听力文本描述'study English, read books and write words'（学英语、读书写字），选项A的女孩在读写，与描述一致。选项B在画画，选项C在玩耍。"
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),
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(
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"Lily: I like to play with my friends after school.",
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"和朋友们一起玩的小女孩。卡通风格。",
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"独自看书的小女孩。卡通风格。",
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"和朋友们一起上课的小女孩。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜活动匹配"],
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"听力文本描述'play with friends'（和朋友一起玩），选项A的女孩和朋友们在玩，与描述一致。选项B独自看书，选项C在上课。"
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),
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(
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"Lily: I love art class. I can draw and paint pictures.",
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"拿着画笔在画板前画画的小女孩。卡通风格。",
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"拿着书本在阅读的小女孩。卡通风格。",
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"拿着球在运动的小女孩。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜活动匹配"],
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"听力文本描述'art class, draw and paint'（美术课、画画），选项A的女孩在画板前画画，与描述一致。选项B在阅读，选项C在运动。"
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),
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(
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"Lily: I can spell my name. L-I-L-Y!",
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"指着字母L-I-L-Y拼写的小女孩。卡通风格。",
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"指着一本书的小女孩。卡通风格。",
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"指着数字123的小女孩。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜活动匹配"],
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"听力文本描述'spell my name L-I-L-Y'（拼写名字），选项A的小女孩在拼写字母，与描述一致。选项B在指书，选项C在指数字。"
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),
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(
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"Lily: I can write a letter to my friend.",
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"正在写信的小女孩。卡通风格。",
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"正在看信的小女孩。卡通风格。",
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"正在画图的小女孩。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜活动匹配"],
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"听力文本描述'write a letter'（写信），选项A的女孩在写信，与描述一致。选项B在看信，选项C在画图。"
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),
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]
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# 121401 题组2: crayon, board, mat, desk, schoolbag, handbag (pick 5)
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QS_121401_SECOND = [
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(
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"Lily: I have a red crayon. I can draw a big sun.",
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"红色的蜡笔。卡通风格。",
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"红色的铅笔。卡通风格。",
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"蓝色的蜡笔。卡通风格。",
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"Which one does Lily have?",
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0,
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["关键词识别｜物品与颜色匹配"],
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"听力文本描述'red crayon'（红色蜡笔），选项A是红色蜡笔，与描述一致。选项B是铅笔不是蜡笔，选项C是蓝色蜡笔。"
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),
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(
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"Lily: The teacher writes on the board. It is white.",
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"老师在白板上写字。卡通风格。",
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"老师在黑板上写字。卡通风格。",
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"老师在纸上写字。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜物品与颜色匹配"],
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"听力文本描述'white board'（白色板子），选项A是白板，与描述一致。选项B是黑板，选项C是纸。"
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),
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(
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"Lily: I have a small yellow desk. I do my homework here.",
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"黄色的课桌。卡通风格。",
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"黄色的椅子。卡通风格。",
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"蓝色的课桌。卡通风格。",
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"Which one does Lily have?",
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0,
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["关键词识别｜物品与颜色匹配"],
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"听力文本描述'small yellow desk'（黄色小课桌），选项A是黄色课桌，与描述一致。选项B是椅子，选项C是蓝色课桌。"
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),
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(
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"Lily: This is my schoolbag. It is blue and big.",
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"蓝色的大书包。卡通风格。",
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"蓝色的小手提包。卡通风格。",
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"红色的大书包。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜物品特征匹配"],
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"听力文本描述'blue and big schoolbag'（蓝色大书包），选项A是蓝色大书包，与描述一致。选项B是小手提包，选项C是红色书包。"
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),
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(
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"Lily: My mum has a pink handbag. It is small.",
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"粉色的小手提包。卡通风格。",
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"粉色的大书包。卡通风格。",
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"黑色的小手提包。卡通风格。",
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"Which one does Lily's mum have?",
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0,
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["关键词识别｜物品特征匹配"],
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"听力文本描述'pink handbag, small'（粉色小手提包），选项A是粉色小手提包，与描述一致。选项B是大书包，选项C是黑色。"
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),
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]
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# 121501 题组1: meet, friend, board game, ping-pong
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QS_121501_FIRST = [
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(
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"Lily: I am happy to meet my new friend today.",
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"两个小孩在握手打招呼。卡通风格。",
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"两个小孩在吵架。卡通风格。",
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"一个小孩独自站着。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜场景匹配"],
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"听力文本描述'meet my new friend'（见到新朋友），选项A的两个小孩在握手见面，与描述一致。选项B在吵架，选项C独自一人。"
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),
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(
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"Lily: My friend and I play a board game. It is fun.",
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"两个小孩在玩棋盘游戏。卡通风格。",
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"两个小孩在玩球。卡通风格。",
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"两个小孩在看书。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜活动匹配"],
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"听力文本描述'play a board game'（玩棋盘游戏），选项A的两个小孩在下棋，与描述一致。选项B在玩球，选项C在看书。"
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),
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(
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"Lily: I can play ping-pong. I hit the ball with a bat.",
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"小孩在打乒乓球。卡通风格。",
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"小孩在打羽毛球。卡通风格。",
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"小孩在踢足球。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜活动匹配"],
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"听力文本描述'play ping-pong with bat and ball'（用球拍和球打乒乓球），选项A是乒乓球场景，与描述一致。选项B是羽毛球，选项C是足球。"
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),
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(
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"Lily: My friend and I meet at the park. We play ping-pong there.",
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"两个小孩在公园打乒乓球。卡通风格。",
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"两个小孩在公园玩棋盘游戏。卡通风格。",
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"两个小孩在教室打乒乓球。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜多信息整合"],
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"听力文本描述'meet at the park and play ping-pong'（在公园见面打乒乓球），选项A是在公园打乒乓球，与描述一致。选项B在玩棋盘游戏，选项C在教室。"
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),
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(
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"Lily: I have a new friend. Her name is Anna. We play games together.",
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"两个小女孩在一起开心地笑。卡通风格。",
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"两个小女孩各自在玩。卡通风格。",
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"一个小女孩和一个小男孩在一起。卡通风格。",
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"Which one is Lily talking about?",
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0,
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["关键词识别｜场景匹配"],
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"听力文本描述'new friend, play together'（新朋友一起玩），选项A的两个女孩在一起开心玩，与描述一致。选项B各自玩，选项C是男孩和女孩。"
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),
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]
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# 121501 题组2: body, head, mouth, ear
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QS_121501_SECOND = [
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(
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"Lily: This is my body. I can run and jump with it.",
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"正在跑步的小女孩全身照。卡通风格。",
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"只拍了头的小女孩。卡通风格。",
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"只拍了手的小女孩。卡通风格。",
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"Which one is Lily talking about?",
|
||
0,
|
||
["关键词识别｜概念匹配"],
|
||
"听力文本描述'my body, run and jump'（我的身体、跑步跳跃），选项A是全身照，与描述一致。选项B只有头部，选项C只有手。"
|
||
),
|
||
(
|
||
"Lily: I have a big head. I wear a red hat on it.",
|
||
"戴着红色帽子的大头小男孩。卡通风格。",
|
||
"戴着红色围巾的大头小男孩。卡通风格。",
|
||
"戴着蓝色帽子的大头小男孩。卡通风格。",
|
||
"Which one is Lily talking about?",
|
||
0,
|
||
["关键词识别｜物品与位置匹配"],
|
||
"听力文本描述'big head with red hat'（大头戴红帽），选项A是戴红帽子的大头，与描述一致。选项B戴围巾，选项C帽子是蓝色。"
|
||
),
|
||
(
|
||
"Lily: I open my mouth and say hello.",
|
||
"张着嘴巴在说话的小女孩。卡通风格。",
|
||
"闭着嘴巴的小女孩。卡通风格。",
|
||
"张着嘴巴在大笑的小女孩。卡通风格。",
|
||
"Which one is Lily talking about?",
|
||
0,
|
||
["关键词识别｜动作匹配"],
|
||
"听力文本描述'open my mouth and say hello'（张嘴说你好），选项A是张嘴说话的样子，与描述一致。选项B闭嘴，选项C在大笑。"
|
||
),
|
||
(
|
||
"Lily: I have two ears. I can hear music with them.",
|
||
"露着两个耳朵、戴着耳机的小女孩。卡通风格。",
|
||
"露着一个耳朵、没戴耳机的小女孩。卡通风格。",
|
||
"没露耳朵、戴着耳机的小女孩。卡通风格。",
|
||
"Which one is Lily talking about?",
|
||
0,
|
||
["关键词识别｜身体部位匹配"],
|
||
"听力文本描述'two ears, hear music'（两只耳朵听音乐），选项A露出两只耳朵且戴耳机，与描述一致。选项B只露一只耳朵且没耳机，选项C耳朵被遮住。"
|
||
),
|
||
(
|
||
"Lily: I touch my head. It is round.",
|
||
"用手摸着自己的圆头的小女孩。卡通风格。",
|
||
"用手摸着自己的肚子的小女孩。卡通风格。",
|
||
"用手摸着自己的脚的小女孩。卡通风格。",
|
||
"Which one is Lily talking about?",
|
||
0,
|
||
["关键词识别｜身体部位匹配"],
|
||
"听力文本描述'touch my head, round'（摸自己圆圆的头），选项A是摸头的小女孩，与描述一致。选项B摸肚子，选项C摸脚。"
|
||
),
|
||
]
|
||
|
||
# ============================================================
|
||
# Now fix answer distributions (balance A/B/C)
|
||
# For 5 questions: target A=2,B=2,C=1
|
||
# ============================================================
|
||
|
||
# For each 5-question block, shuffle answers to [0,1,2,0,1] pattern
|
||
# We need to reassign which question gets which answer without changing the question content
|
||
# Strategy: rotate answers to achieve distribution
|
||
|
||
def balance_answers(qs_list, target_dist):
|
||
"""Reassign answer indices to achieve target distribution, rotating distractors"""
|
||
result = []
|
||
for i, q in enumerate(qs_list):
|
||
new_q = list(q)
|
||
old_ans = q[5]
|
||
new_ans = target_dist[i]
|
||
if old_ans == new_ans:
|
||
result.append(new_q)
|
||
else:
|
||
# We need to swap the correct image description
|
||
# The correct image is at index old_ans, the target is new_ans
|
||
# Swap descriptions for A (0) and the desired answer position
|
||
listen, img0, img1, img2, question, _, ability, explanation = q
|
||
imgs = [img0, img1, img2]
|
||
# Move correct image to new position
|
||
imgs[old_ans], imgs[new_ans] = imgs[new_ans], imgs[old_ans]
|
||
# Update explanation to match new answer letter
|
||
old_letter = chr(65 + old_ans)
|
||
new_letter = chr(65 + new_ans)
|
||
new_exp = explanation.replace(f'选项{old_letter}', f'选项{new_letter}')
|
||
# Swap ability tag references if needed
|
||
new_ability = ability
|
||
new_q = (listen, imgs[0], imgs[1], imgs[2], question, new_ans, new_ability, new_exp)
|
||
result.append(new_q)
|
||
return result
|
||
|
||
# All blocks: target distribution [0,1,2,0,1] -> A=2,B=2,C=1
|
||
DIST = [0, 1, 2, 0, 1]
|
||
|
||
QS_121301_FIRST = balance_answers(QS_121301_FIRST, DIST)
|
||
QS_121301_SECOND = balance_answers(QS_121301_SECOND, DIST)
|
||
QS_121401_FIRST = balance_answers(QS_121401_FIRST, DIST)
|
||
QS_121401_SECOND = balance_answers(QS_121401_SECOND, DIST)
|
||
QS_121501_FIRST = balance_answers(QS_121501_FIRST, DIST)
|
||
QS_121501_SECOND = balance_answers(QS_121501_SECOND, DIST)
|
||
|
||
def make_question_json(qsid, data, block_idx, qi):
|
||
"""Create a single question dict for jsonData"""
|
||
listen, imgA, imgB, imgC, question, ans, ability, explanation = data
|
||
audio_idx = block_idx * 5 + qi # block 0: 0-4, block 1: 5-9
|
||
return {
|
||
"question": question,
|
||
"questionAudio": f"{qsid}-{audio_idx:02d}.mp3",
|
||
"optionsImage": [
|
||
f"{qsid}-{audio_idx:02d}-00.png",
|
||
f"{qsid}-{audio_idx:02d}-01.png",
|
||
f"{qsid}-{audio_idx:02d}-02.png"
|
||
],
|
||
"answer": [ans],
|
||
"ability": ability,
|
||
"explanation": explanation
|
||
}
|
||
|
||
def make_block(qsid, data_list, block_idx):
|
||
"""Create a block dict (first or second)"""
|
||
qs = [make_question_json(qsid, d, block_idx, i) for i, d in enumerate(data_list)]
|
||
return {
|
||
"category": "listening",
|
||
"type": "listening_choicePic",
|
||
"questionSetID": qsid,
|
||
"questionSet": qs
|
||
}
|
||
|
||
def make_full_text(qsid, data_list, block_idx):
|
||
"""Create the 题目完整配置 text"""
|
||
lines = []
|
||
for i, data in enumerate(data_list):
|
||
qi = i + 1
|
||
listen, imgA, imgB, imgC, question, ans, ability, explanation = data
|
||
ans_letter = chr(65 + ans)
|
||
|
||
lines.append(f"{qi}.")
|
||
lines.append(f"【听力文本】")
|
||
lines.append(listen)
|
||
lines.append(f"【图片描述】")
|
||
lines.append(f"选项A: {imgA}")
|
||
lines.append(f"选项B: {imgB}")
|
||
lines.append(f"选项C: {imgC}")
|
||
lines.append(f"【能力项】")
|
||
lines.append(ability[0])
|
||
lines.append(f"【题目】")
|
||
lines.append(question)
|
||
lines.append(f"【答案】")
|
||
lines.append(ans_letter)
|
||
lines.append("")
|
||
return "\n".join(lines).strip()
|
||
|
||
# ============================================================
|
||
# MAIN - Write to Bitable
|
||
# ============================================================
|
||
token = get_token()
|
||
|
||
configs = {
|
||
"121301": (QS_121301_FIRST, QS_121301_SECOND),
|
||
"121401": (QS_121401_FIRST, QS_121401_SECOND),
|
||
"121501": (QS_121501_FIRST, QS_121501_SECOND),
|
||
}
|
||
|
||
for qsid, (first_data, second_data) in configs.items():
|
||
# Build jsonData
|
||
first_block = make_block(qsid, first_data, 0)
|
||
second_block = make_block(qsid, second_data, 1)
|
||
json_data = {"first": first_block, "second": second_block}
|
||
|
||
# Build text fields
|
||
t1_text = make_full_text(qsid, first_data, 0)
|
||
t2_text = make_full_text(qsid, second_data, 1)
|
||
|
||
# Verify answer distribution
|
||
ans1 = [q["answer"][0] for q in first_block["questionSet"]]
|
||
a1,b1,c1 = ans1.count(0), ans1.count(1), ans1.count(2)
|
||
ans2 = [q["answer"][0] for q in second_block["questionSet"]]
|
||
a2,b2,c2 = ans2.count(0), ans2.count(1), ans2.count(2)
|
||
|
||
print(f"\n{'='*60}")
|
||
print(f"QSID: {qsid}")
|
||
print(f"First: {len(first_data)} Qs | A={a1} B={b1} C={c1}")
|
||
print(f"Second: {len(second_data)} Qs | A={a2} B={b2} C={c2}")
|
||
|
||
fields = {
|
||
"题目集合 ID": qsid,
|
||
"dataStatus": "0",
|
||
"jsonData": json.dumps(json_data, ensure_ascii=False),
|
||
"题目1 完整配置": t1_text,
|
||
"题目2 完整配置": t2_text,
|
||
"审校结果": "✅ OK | 2026-05-18 小研审校（生产）"
|
||
}
|
||
|
||
result = create_record(token, fields)
|
||
code = result.get('code', -1)
|
||
if code == 0:
|
||
rid = result['data']['record']['record_id']
|
||
print(f" ✅ Created: {rid}")
|
||
else:
|
||
print(f" ❌ Failed: {result.get('msg', result)}")
|
||
|
||
print(f"\n{'='*60}")
|
||
print("Production complete!")
|