#!/usr/bin/env python3 """ 口语-P4-看图识物 内容生产、回填、审校 生产范围：3条记录（100001, 110101, 110201） """ import json, subprocess, sys, os APP_TOKEN = "CMHSbUUjka3TrUsaxxEc297ongf" TABLE_ID = "tblsD2dxaRpLmkXD" CRED_FILE = "/root/.openclaw/credentials/xiaoyan/config.json" # Load credentials with open(CRED_FILE) as f: cred = json.load(f) APP_ID = cred['apps'][0]['appId'] APP_SECRET = cred['apps'][0]['appSecret'] def get_token(): r = subprocess.run([ "curl", "-s", "-X", "POST", "https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal", "-H", "Content-Type: application/json", "-d", json.dumps({"app_id": APP_ID, "app_secret": APP_SECRET}) ], capture_output=True, text=True) return json.loads(r.stdout)['tenant_access_token'] TOKEN = get_token() def list_records(): r = subprocess.run([ "curl", "-s", "-X", "GET", f"https://open.feishu.cn/open-apis/bitable/v1/apps/{APP_TOKEN}/tables/{TABLE_ID}/records?page_size=100", "-H", f"Authorization: Bearer {TOKEN}" ], capture_output=True, text=True) return json.loads(r.stdout) def create_record(fields): body = json.dumps({"fields": fields}, ensure_ascii=False) r = subprocess.run([ "curl", "-s", "-X", "POST", f"https://open.feishu.cn/open-apis/bitable/v1/apps/{APP_TOKEN}/tables/{TABLE_ID}/records", "-H", f"Authorization: Bearer {TOKEN}", "-H", "Content-Type: application/json", "-d", body ], capture_output=True, text=True) return json.loads(r.stdout) def get_record(record_id): r = subprocess.run([ "curl", "-s", "-X", "GET", f"https://open.feishu.cn/open-apis/bitable/v1/apps/{APP_TOKEN}/tables/{TABLE_ID}/records/{record_id}", "-H", f"Authorization: Bearer {TOKEN}" ], capture_output=True, text=True) return json.loads(r.stdout) def build_explanation(question_text, image_desc, answer_word, is_color=False): """Build a detailed Chinese explanation for a question.""" if is_color: return ( f"回答要点: {question_text}\n" f"图片内容: {image_desc}\n" f"考察能力: 图文匹配\n" 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" f"回答指导: 鼓励学生用完整句子作答，如\"It's {answer_word}.\"，根据图片颜色准确说出对应的颜色单词" ) else: return ( f"回答要点: {question_text}\n" f"图片内容: {image_desc}\n" f"考察能力: 图文匹配\n" 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" f"回答指导: 鼓励学生用完整句子作答，如\"It's a {answer_word}.\"，根据图片内容准确说出对应的英文单词" ) def build_short_explanation(image_desc, answer_word, is_color=False): """Build short explanation for human-readable config.""" if is_color: return f"图片中展示的颜色是{answer_word}，对应的颜色单词是 {answer_word}。" else: return f"图片中是{answer_word}，对应的英文单词是 {answer_word}。" def build_human_config(qs_id, asr_prompt, questions, text_title="Look and answer."): """Build human-readable 题目配置 text.""" lines = [f"做题要求：{text_title}", "", f"热词：", asr_prompt, "", "问题："] for i, q in enumerate(questions, 1): lines.append(f"{i}. ") lines.append(f"图片：{q['imageDesc']}") lines.append(f"题目：{q['question']}") lines.append(f"能力：{'、'.join(q['ability'])}") lines.append(f"解析：{q['shortExplanation']}") lines.append("") return "\n".join(lines) # ============================================= # DATA DEFINITIONS # ============================================= records_data = [] # --- ID: 100001 --- records_data.append({ "questionSetID": "100001", "textTitle": "Look and answer.", "first_words": ["blue", "red", "pink", "green", "orange", "purple"], "first_is_color": True, "first_image_descs": [ "白色的背景，中间是一颗蓝色的星星。", "白色的背景，中间是一个红色的气球。", "白色的背景，中间是一朵粉红色的花。", "白色的背景，中间是一片绿色的叶子。", "白色的背景，中间是一个橙色的橘子。", "白色的背景，中间是一串紫色的葡萄。", ], "second_words": ["bag", "dress", "jacket", "hat", "T-shirt"], "second_is_color": False, "second_image_descs": [ "白色的背景，中间是一个书包。", "白色的背景，中间是一条连衣裙。", "白色的背景，中间是一件夹克衫。", "白色的背景，中间是一顶帽子。", "白色的背景，中间是一件T恤衫。", ], }) # --- ID: 110101 --- records_data.append({ "questionSetID": "110101", "textTitle": "Look and answer.", "first_words": ["hair", "eye", "nose", "monster", "foot", "hand"], "first_is_color": False, "first_image_descs": [ "白色的背景，中间是一个人的头发。", "白色的背景，中间是一只眼睛。", "白色的背景，中间是一个鼻子。", "白色的背景，中间是一只可爱的小怪兽。", "白色的背景，中间是一只脚。", "白色的背景，中间是一只手。", ], "second_words": ["black", "brown", "colour", "colour", "white", "yellow"], "second_is_color": True, "second_image_descs": [ "白色的背景，中间是一只黑色的猫。", "白色的背景，中间是一只棕色的小狗。", "白色的背景，中间是一个彩色的调色盘。", "白色的背景，中间是一道彩虹。", "白色的背景，中间是一只白色的兔子。", "白色的背景，中间是一朵黄色的向日葵。", ], # For colour (not a typical color answer), use "What's this?" instead of "What colour is it?" "second_override_questions": { 2: "What's this?", # colour - 调色盘 3: "What's this?", # colour - 彩虹 }, }) # --- ID: 110201 --- records_data.append({ "questionSetID": "110201", "textTitle": "Look and answer.", "first_words": ["bread", "pie", "cake", "candy", "chocolate", "ice cream"], "first_is_color": False, "first_image_descs": [ "白色的背景，中间是一片面包。", "白色的背景，中间是一块馅饼。", "白色的背景，中间是一块蛋糕。", "白色的背景，中间是一颗糖果。", "白色的背景，中间是一块巧克力。", "白色的背景，中间是一个冰淇淋。", ], "second_words": ["cat", "dog", "mice", "mouse"], "second_is_color": False, "second_image_descs": [ "白色的背景，中间是一只猫。", "白色的背景，中间是一只狗。", "白色的背景，中间是两只老鼠。", "白色的背景，中间是一只老鼠。", ], "second_override_questions": { 2: "What are these?", # mice (plural) }, }) # ============================================= # GENERATE & WRITE # ============================================= results = [] for rd in records_data: qsid = rd["questionSetID"] print(f"\n{'='*60}") print(f"Processing ID: {qsid}") print(f"{'='*60}") # Build first question set first_questions = [] first_words = rd["first_words"] first_is_color = rd["first_is_color"] first_img_descs = rd["first_image_descs"] for i, word in enumerate(first_words): q_text = "What colour is it?" if first_is_color else "What's this?" q_img = f"{qsid}-{i:02d}.png" img_desc = first_img_descs[i] explanation = build_explanation(q_text, img_desc, word, first_is_color) short_exp = build_short_explanation(img_desc, word, first_is_color) first_questions.append({ "question": q_text, "questionImage": q_img, "imageDesc": img_desc, "ability": ["图文匹配"], "explanation": explanation, "shortExplanation": short_exp, }) first_asr = ", ".join(first_words) first_qs = { "category": "speaking", "type": "speaking_pic_recognize", "asrPrompt": first_asr, "questionSetID": qsid, "textTitle": rd["textTitle"], "questionSet": [{k: v for k, v in q.items() if k != "shortExplanation"} for q in first_questions], } # Build second question set second_questions = [] second_words = rd["second_words"] second_is_color = rd["second_is_color"] second_img_descs = rd["second_image_descs"] override_qs = rd.get("second_override_questions", {}) img_start = len(first_questions) for i, word in enumerate(second_words): if i in override_qs: q_text = override_qs[i] is_color = False elif second_is_color: q_text = "What colour is it?" is_color = True else: q_text = "What's this?" is_color = False q_img = f"{qsid}-{img_start + i:02d}.png" img_desc = second_img_descs[i] explanation = build_explanation(q_text, img_desc, word, is_color) short_exp = build_short_explanation(img_desc, word, is_color) second_questions.append({ "question": q_text, "questionImage": q_img, "imageDesc": img_desc, "ability": ["图文匹配"], "explanation": explanation, "shortExplanation": short_exp, }) second_asr = ", ".join(second_words) second_qs = { "category": "speaking", "type": "speaking_pic_recognize", "asrPrompt": second_asr, "questionSetID": qsid, "textTitle": rd["textTitle"], "questionSet": [{k: v for k, v in q.items() if k != "shortExplanation"} for q in second_questions], } # Build jsonData json_data = json.dumps({"first": first_qs, "second": second_qs}, ensure_ascii=False) # Build human-readable configs config1 = build_human_config(qsid, first_asr, first_questions, rd["textTitle"]) config2 = build_human_config(qsid, second_asr, second_questions, rd["textTitle"]) # Fields for bitable fields = { "dataStatus": "1", "jsonData": json_data, "题目1 完整配置": config1, "题目2 完整配置": config2, "题目集合 ID": qsid, } # Create record print(f" Creating record for QSID={qsid}...") resp = create_record(fields) if resp.get("code") != 0: print(f" ❌ CREATE FAILED: {resp}") results.append({"qsid": qsid, "status": "FAILED", "error": resp}) continue record_id = resp["data"]["record"]["record_id"] print(f" ✅ Created: record_id={record_id}") # Verify by reading back print(f" Verifying...") verify = get_record(record_id) if verify.get("code") != 0: print(f" ⚠️ VERIFY FAILED: {verify}") results.append({"qsid": qsid, "record_id": record_id, "status": "VERIFY_FAILED", "error": verify}) continue v_fields = verify["data"]["record"]["fields"] v_json = json.loads(v_fields.get("jsonData", "{}")) # Quick checks checks = [] # Check first question count f_count = len(v_json.get("first", {}).get("questionSet", [])) expected_f = len(first_words) checks.append(("first题数", f_count == expected_f, f"{f_count}/{expected_f}")) s_count = len(v_json.get("second", {}).get("questionSet", [])) expected_s = len(second_words) checks.append(("second题数", s_count == expected_s, f"{s_count}/{expected_s}")) # Check QSID v_qsid = v_fields.get("题目集合 ID", "") checks.append(("题目集合ID", v_qsid == qsid, v_qsid)) # Check config contains questions c1_has = "问题：" in v_fields.get("题目1 完整配置", "") c2_has = "问题：" in v_fields.get("题目2 完整配置", "") checks.append(("题目1配置", c1_has, "OK" if c1_has else "MISSING")) checks.append(("题目2配置", c2_has, "OK" if c2_has else "MISSING")) all_ok = all(c[1] for c in checks) status_icon = "✅" if all_ok else "⚠️" print(f" {status_icon} Verification: {' | '.join(f'{name}={detail}' for name, ok, detail in checks)}") results.append({ "qsid": qsid, "record_id": record_id, "status": "OK" if all_ok else "WARN", "checks": checks, }) # Print summary print(f" First: {len(first_words)} questions, ASR: {first_asr}") print(f" Second: {len(second_words)} questions, ASR: {second_asr}") # ============================================= # AUDIT SUMMARY # ============================================= print(f"\n{'='*60}") print(f"AUDIT SUMMARY") print(f"{'='*60}") all_passed = True for r in results: if r["status"] == "OK": print(f" ✅ {r['qsid']} | record_id={r['record_id']} | ALL CHECKS PASSED") elif r["status"] == "WARN": print(f" ⚠️ {r['qsid']} | record_id={r['record_id']} | WARNINGS:") for name, ok, detail in r["checks"]: if not ok: print(f" - {name}: {detail}") all_passed = False else: print(f" ❌ {r['qsid']} | {r['status']}: {r.get('error', '')}") all_passed = False # Vocabulary audit print(f"\n--- Vocabulary Audit ---") audit_notes = [] for rd in records_data: qsid = rd["questionSetID"] all_words = rd["first_words"] + rd["second_words"] for w in all_words: # Check: compound words, special characters if " " in w: audit_notes.append(f" ℹ️ {qsid}: '{w}' is multi-word — ensure ASR handles it") if not w.isascii(): audit_notes.append(f" ⚠️ {qsid}: '{w}' contains non-ASCII") # Check 110101 colour duplicate audit_notes.append(f" ℹ️ 110101: 'colour' appears twice in second set — intentional per spec") # Check 110201 mice/mouse audit_notes.append(f" ℹ️ 110201: 'mice' (plural) and 'mouse' (singular) both present — questions use 'What are these?' vs 'What's this?'") for note in audit_notes: print(note) print(f"\n{'='*60}") if all_passed: print("✅ ALL 3 RECORDS PRODUCED, BACKFILLED, AND VERIFIED") else: print("⚠️ SOME CHECKS FAILED — SEE ABOVE") print(f"{'='*60}")