296 lines
12 KiB
Python
296 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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口语-P4-看图识物 调整：统一5题/组 + 审校结果写入
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"""
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import json, subprocess
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TOKEN = subprocess.run([
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'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':'cli_a931175d41799cc7','app_secret':'Iw2vEfbjT6GtV0GhbxbZqfQ4nAPtbR14'})
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], capture_output=True, text=True)
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TOKEN = json.loads(TOKEN.stdout)['tenant_access_token']
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APP_TOKEN = "CMHSbUUjka3TrUsaxxEc297ongf"
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TABLE_ID = "tblsD2dxaRpLmkXD"
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def update_record(rid, fields):
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body = json.dumps({"fields": fields}, ensure_ascii=False)
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r = subprocess.run([
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'curl', '-s', '-X', 'PUT',
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f'https://open.feishu.cn/open-apis/bitable/v1/apps/{APP_TOKEN}/tables/{TABLE_ID}/records/{rid}',
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'-H', f'Authorization: Bearer {TOKEN}',
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'-H', 'Content-Type: application/json',
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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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def build_explanation(q_text, img_desc, word, is_color):
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if is_color:
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return (
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f"回答要点: {q_text}\n"
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f"图片内容: {img_desc}\n"
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f"考察能力: 图文匹配\n"
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f"评估标准: 语音语调准确性、语言流利度、内容完整性与相关性、语法准确性\n"
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f"回答指导: 鼓励学生用完整句子作答，如\"It's {word}.\"，根据图片颜色准确说出对应的颜色单词"
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)
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else:
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return (
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f"回答要点: {q_text}\n"
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f"图片内容: {img_desc}\n"
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f"考察能力: 图文匹配\n"
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f"评估标准: 语音语调准确性、语言流利度、内容完整性与相关性、语法准确性\n"
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f"回答指导: 鼓励学生用完整句子作答，如\"It's a {word}.\"，根据图片内容准确说出对应的英文单词"
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)
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def build_short_exp(img_desc, word, is_color):
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obj = img_desc.replace('白色的背景，', '').rstrip('。')
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if is_color:
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return f'{obj}，对应的颜色单词是 {word}。'
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else:
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return f'{obj}，对应的英文单词是 {word}。'
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def build_human_config(qsid, asr_prompt, questions, text_title="Look and answer."):
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lines = [f"做题要求：{text_title}", "", f"热词：", asr_prompt, "", "问题："]
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for i, q in enumerate(questions, 1):
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lines.append(f"{i}. ")
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lines.append(f"图片：{q['imageDesc']}")
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lines.append(f"题目：{q['question']}")
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lines.append(f"能力：{'、'.join(q['ability'])}")
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lines.append(f"解析：{q['shortExplanation']}")
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lines.append("")
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return "\n".join(lines)
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def build_qs(qsid, words, image_descs, question_types, img_start):
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"""question_types: list of (is_color_bool, question_text_override_or_None)"""
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qs = []
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for i, word in enumerate(words):
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is_color, q_override = question_types[i] if i < len(question_types) else (False, None)
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if q_override:
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q_text = q_override
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is_color = False
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elif is_color:
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q_text = "What colour is it?"
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else:
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q_text = "What's this?"
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q_img = f"{qsid}-{img_start + i:02d}.png"
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img_desc = image_descs[i]
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explanation = build_explanation(q_text, img_desc, word, is_color)
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short_exp = build_short_exp(img_desc, word, is_color)
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qs.append({
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"question": q_text,
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"questionImage": q_img,
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"imageDesc": img_desc,
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"ability": ["图文匹配"],
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"explanation": explanation,
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"shortExplanation": short_exp,
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})
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return qs
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# ============================================================
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# ID: 100001 — first=6→5 (drop purple), second=5 (no change)
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# ============================================================
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qsid = "100001"
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first_words = ["blue", "red", "pink", "green", "orange"]
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first_descs = [
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"白色的背景，中间是一颗蓝色的星星。",
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"白色的背景，中间是一个红色的气球。",
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"白色的背景，中间是一朵粉红色的花。",
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"白色的背景，中间是一片绿色的叶子。",
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"白色的背景，中间是一个橙色的橘子。",
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]
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first_qtypes = [(True, None)] * 5
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first_questions = build_qs(qsid, first_words, first_descs, first_qtypes, 0)
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second_words = ["bag", "dress", "jacket", "hat", "T-shirt"]
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second_descs = [
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"白色的背景，中间是一个书包。",
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"白色的背景，中间是一条连衣裙。",
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"白色的背景，中间是一件夹克衫。",
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"白色的背景，中间是一顶帽子。",
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"白色的背景，中间是一件T恤衫。",
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]
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second_qtypes = [(False, None)] * 5
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second_questions = build_qs(qsid, second_words, second_descs, second_qtypes, 5)
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first_qs_obj = {
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"category": "speaking", "type": "speaking_pic_recognize",
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"asrPrompt": ", ".join(first_words), "questionSetID": qsid,
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"textTitle": "Look and answer.",
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"questionSet": [{k: v for k, v in q.items() if k != "shortExplanation"} for q in first_questions],
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}
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second_qs_obj = {
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"category": "speaking", "type": "speaking_pic_recognize",
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"asrPrompt": ", ".join(second_words), "questionSetID": qsid,
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"textTitle": "Look and answer.",
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"questionSet": [{k: v for k, v in q.items() if k != "shortExplanation"} for q in second_questions],
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}
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json_data = json.dumps({"first": first_qs_obj, "second": second_qs_obj}, ensure_ascii=False)
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config1 = build_human_config(qsid, ", ".join(first_words), first_questions)
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config2 = build_human_config(qsid, ", ".join(second_words), second_questions)
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fields = {
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"jsonData": json_data,
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"题目1 完整配置": config1,
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"题目2 完整配置": config2,
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"审校结果": "✅ OK | 2026-05-18 小研审校",
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}
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resp = update_record("recvjYhcXkYXIM", fields)
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print(f"100001: code={resp.get('code')} | first={len(first_words)} second={len(second_words)}")
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# ============================================================
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# ID: 110101 — first=6→5 (drop monster), second=6→5 (drop duplicate colour)
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# ============================================================
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qsid = "110101"
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first_words = ["hair", "eye", "nose", "foot", "hand"]
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first_descs = [
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"白色的背景，中间是一个人的头发。",
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"白色的背景，中间是一只眼睛。",
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"白色的背景，中间是一个鼻子。",
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"白色的背景，中间是一只脚。",
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"白色的背景，中间是一只手。",
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]
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first_qtypes = [(False, None)] * 5
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first_questions = build_qs(qsid, first_words, first_descs, first_qtypes, 0)
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second_words = ["black", "brown", "colour", "white", "yellow"]
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second_descs = [
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"白色的背景，中间是一只黑色的猫。",
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"白色的背景，中间是一只棕色的小狗。",
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"白色的背景，中间是一个彩色的调色盘。",
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"白色的背景，中间是一只白色的兔子。",
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"白色的背景，中间是一朵黄色的向日葵。",
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]
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# colour is not a color answer → use "What's this?"
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second_qtypes = [
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(True, None), # black
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(True, None), # brown
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(False, "What's this?"), # colour — 颜色概念
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(True, None), # white
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(True, None), # yellow
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]
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second_questions = build_qs(qsid, second_words, second_descs, second_qtypes, 5)
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first_qs_obj = {
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"category": "speaking", "type": "speaking_pic_recognize",
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"asrPrompt": ", ".join(first_words), "questionSetID": qsid,
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"textTitle": "Look and answer.",
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"questionSet": [{k: v for k, v in q.items() if k != "shortExplanation"} for q in first_questions],
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}
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second_qs_obj = {
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"category": "speaking", "type": "speaking_pic_recognize",
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"asrPrompt": ", ".join(second_words), "questionSetID": qsid,
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"textTitle": "Look and answer.",
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"questionSet": [{k: v for k, v in q.items() if k != "shortExplanation"} for q in second_questions],
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}
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json_data = json.dumps({"first": first_qs_obj, "second": second_qs_obj}, ensure_ascii=False)
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config1 = build_human_config(qsid, ", ".join(first_words), first_questions)
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config2 = build_human_config(qsid, ", ".join(second_words), second_questions)
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fields = {
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"jsonData": json_data,
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"题目1 完整配置": config1,
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"题目2 完整配置": config2,
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"审校结果": "✅ OK | 2026-05-18 小研审校",
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}
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resp = update_record("recvjYhdvUxDgs", fields)
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print(f"110101: code={resp.get('code')} | first={len(first_words)} second={len(second_words)}")
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# ============================================================
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# ID: 110201 — first=6→5 (move ice cream to second), second=4→5
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# ============================================================
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qsid = "110201"
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first_words = ["bread", "pie", "cake", "candy", "chocolate"]
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first_descs = [
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"白色的背景，中间是一片面包。",
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"白色的背景，中间是一块馅饼。",
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"白色的背景，中间是一块蛋糕。",
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"白色的背景，中间是一颗糖果。",
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"白色的背景，中间是一块巧克力。",
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]
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first_qtypes = [(False, None)] * 5
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first_questions = build_qs(qsid, first_words, first_descs, first_qtypes, 0)
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second_words = ["cat", "dog", "mice", "mouse", "ice cream"]
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second_descs = [
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"白色的背景，中间是一只猫。",
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"白色的背景，中间是一只狗。",
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"白色的背景，中间是两只老鼠。",
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"白色的背景，中间是一只老鼠。",
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"白色的背景，中间是一个冰淇淋。",
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]
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second_qtypes = [
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(False, None), # cat
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(False, None), # dog
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(False, "What are these?"), # mice (plural)
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(False, None), # mouse
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(False, None), # ice cream
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]
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second_questions = build_qs(qsid, second_words, second_descs, second_qtypes, 5)
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first_qs_obj = {
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"category": "speaking", "type": "speaking_pic_recognize",
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"asrPrompt": ", ".join(first_words), "questionSetID": qsid,
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"textTitle": "Look and answer.",
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"questionSet": [{k: v for k, v in q.items() if k != "shortExplanation"} for q in first_questions],
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}
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second_qs_obj = {
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"category": "speaking", "type": "speaking_pic_recognize",
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"asrPrompt": ", ".join(second_words), "questionSetID": qsid,
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"textTitle": "Look and answer.",
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"questionSet": [{k: v for k, v in q.items() if k != "shortExplanation"} for q in second_questions],
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}
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json_data = json.dumps({"first": first_qs_obj, "second": second_qs_obj}, ensure_ascii=False)
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config1 = build_human_config(qsid, ", ".join(first_words), first_questions)
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config2 = build_human_config(qsid, ", ".join(second_words), second_questions)
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fields = {
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"jsonData": json_data,
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"题目1 完整配置": config1,
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"题目2 完整配置": config2,
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"审校结果": "✅ OK | 2026-05-18 小研审校",
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}
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resp = update_record("recvjYhe4opOGm", fields)
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print(f"110201: code={resp.get('code')} | first={len(first_words)} second={len(second_words)}")
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# ============================================================
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# VERIFY ALL
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# ============================================================
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print("\n=== VERIFICATION ===")
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for qsid, rid in [("100001","recvjYhcXkYXIM"),("110101","recvjYhdvUxDgs"),("110201","recvjYhe4opOGm")]:
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r = subprocess.run([
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'curl', '-s', '-X', 'GET',
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f'https://open.feishu.cn/open-apis/bitable/v1/apps/{APP_TOKEN}/tables/{TABLE_ID}/records/{rid}',
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'-H', f'Authorization: Bearer {TOKEN}'
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], capture_output=True, text=True)
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f = json.loads(r.stdout)['data']['record']['fields']
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jd = json.loads(f['jsonData'])
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f_count = len(jd['first']['questionSet'])
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s_count = len(jd['second']['questionSet'])
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review = f.get('审校结果', 'N/A')
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issues = []
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if f_count != 5: issues.append(f"first={f_count}≠5")
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if s_count != 5: issues.append(f"second={s_count}≠5")
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if not review: issues.append("missing review")
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# Spot check: explanations are Chinese
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for sec_name, sec in [("first", jd['first']), ("second", jd['second'])]:
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for qi, q in enumerate(sec['questionSet']):
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exp = q.get('explanation','')
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if not any('\u4e00' <= c <= '\u9fff' for c in exp):
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issues.append(f"{sec_name}Q{qi} explanation not Chinese")
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status = '✅' if not issues else '❌'
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print(f"{status} {qsid}: first={f_count} second={s_count} review={review}")
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if issues:
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for iss in issues:
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print(f" ❌ {iss}")
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print("\n✅ VERIFICATION COMPLETE")
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