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ReadAPI源码完全破解,适用所有SoapUI版本

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下载需积分: 50 | 978KB | 更新于2025-08-28 | 27 浏览量 | 31 下载量 举报 收藏
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【标题】和【描述】提到的内容涉及软件破解,这通常是违法的行为,违反了软件许可协议,可能会侵犯版权法和软件许可法。在此情境下,我将忽略破解方面的内容,而是专注于合法的知识点,比如API(应用程序接口)、soapui这款软件的基本概念以及license(许可)相关知识。 ### 知识点:API(应用程序接口) API是应用程序编程接口(Application Programming Interface)的缩写,它是一套用于构建软件应用程序的定义和协议。API定义了不同软件组件间交互的接口规范,使得开发者可以通过这些接口来使用特定功能,而无需了解这些功能是如何实现的内部细节。 #### API的类型 1. **公共API**:对公众公开,任何人都可以调用接口。 2. **私有API**:只供公司内部使用,不对外公开。 3. **合作伙伴API**:仅限公司合作伙伴使用。 4. **托管API**:由第三方提供,通过互联网托管。 5. **本地API**:安装在本地的机器或网络上。 #### API的工作原理 API允许不同应用程序之间的互操作性。在调用API时,通常涉及发送HTTP请求到API端点,并接收响应,该响应可能包含数据、状态码或其他信息。 ### 知识点:soapui soapui是一个开源的API测试工具,它可以用来测试各种类型的Web服务,包括SOAP、REST、HTTP等。soapui提供了一套完整的接口测试解决方案,用户可以通过它进行模拟、测试和验证Web服务的功能性。 #### soapui的主要功能 1. **API功能测试**:验证Web服务是否按照预期工作。 2. **负载测试**:检查Web服务在高负载下的表现。 3. **安全性测试**:检测Web服务的安全漏洞。 4. **模拟服务**:在没有真实后端服务的情况下模拟服务响应。 #### soapui的使用场景 - 开发者在开发阶段可以使用soapui进行单元测试。 - 测试人员在软件测试周期内进行集成测试。 - 项目经理或产品经理在验收阶段进行功能验证。 - 运维团队进行服务监控。 ### 知识点:license(许可) License在IT领域通常指的是软件许可,这是一种法律许可,允许用户在特定的条件和限制下使用软件产品。软件许可的种类很多,下面是一些常见的类型。 #### 软件许可的主要类型 1. **专有许可证**:用户需要支付费用才能使用软件,并且通常会有版权和使用的限制。 2. **开源许可证**:允许用户使用、修改和分发软件,但需遵守许可证条款。 3. **免费许可证**:允许用户在不需要支付费用的情况下使用软件。 4. **试用许可证**:用户可以在限定时间内免费使用软件,通常有功能或时间限制。 5. **教育许可证**:特定用户群体,如学生和教育机构,可以使用软件。 #### 许可证的限制 - **用户数限制**:许可证可能限制同时使用软件的最大用户数。 - **用途限制**:软件可能只能用于非商业目的。 - **复制限制**:禁止未经授权复制或分发软件。 - **修改限制**:禁止用户修改软件源代码。 - **再许可限制**:禁止用户将软件许可授予第三方。 在实际应用中,了解和遵守许可证条款是非常重要的。违反这些条款不仅可能产生法律问题,而且会损害软件开发者的利益,影响整个软件生态系统的健康运作。 ### 总结 标题和描述中提到的“ReadAPI破解KEYS”和“通杀所有soapui版本”涉及的内容是不合法的。作为IT行业专业人士,我们应该注重软件的合法使用,尊重开发者的劳动成果,通过正当途径获取和使用软件许可。软件API、soapui工具的正确使用以及许可证的理解,可以帮助我们更好地在合法合规的基础上进行开发和测试工作。

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下面的代码运行完后,显示请求失败: 'status' import pandas as pd import requests import time import math # WGS84转GCJ02坐标转换函数 def wgs84_to_gcj02(lng, lat): a = 6378245.0 ee = 0.00669342162296594323 def transform_lat(x, y): ret = -100.0 + 2.0*x + 3.0*y + 0.2*y*y + 0.1*x*y + 0.2*math.sqrt(abs(x)) ret += (20.0*math.sin(6.0*x*math.pi) + 20.0*math.sin(2.0*x*math.pi)) * 2.0 / 3.0 ret += (20.0*math.sin(y*math.pi) + 40.0*math.sin(y/3.0*math.pi)) * 2.0 / 3.0 ret += (160.0*math.sin(y/12.0*math.pi) + 320*math.sin(y*math.pi/30.0)) * 2.0 / 3.0 return ret def transform_lng(x, y): ret = 300.0 + x + 2.0*y + 0.1*x*x + 0.1*x*y + 0.1*math.sqrt(abs(x)) ret += (20.0*math.sin(6.0*x*math.pi) + 20.0*math.sin(2.0*x*math.pi)) * 2.0 / 3.0 ret += (20.0*math.sin(x*math.pi) + 40.0*math.sin(x/3.0*math.pi)) * 2.0 / 3.0 ret += (150.0*math.sin(x/12.0*math.pi) + 300.0*math.sin(x/30.0*math.pi)) * 2.0 / 3.0 return ret dlat = transform_lat(lng - 105.0, lat - 35.0) dlng = transform_lng(lng - 105.0, lat - 35.0) radlat = lat / 180.0 * math.pi magic = math.sin(radlat) magic = 1 - ee * magic * magic sqrtmagic = math.sqrt(magic) dlat = (dlat * 180.0) / ((a * (1 - ee)) / (magic * sqrtmagic) * math.pi) dlng = (dlng * 180.0) / (a / sqrtmagic * math.cos(radlat) * math.pi) mglat = lat + dlat mglng = lng + dlng return mglng, mglat # 高德API配置 API_KEYS = ["your_key1", "your_key2", "your_key3"] # 需替换为实际API Key CURRENT_KEY_INDEX = 0 TRANSPORT_CONFIG = { 'bicycle': {'type': 3, 'speed': 10}, 'car': {'type': 1, 'speed': 15}, 'bus': {'type': 0, 'speed': 12} } def request_with_key_rotation(url, params): global CURRENT_KEY_INDEX max_retries = len(API_KEYS) * 2 for _ in range(max_retries): params['key'] = API_KEYS[CURRENT_KEY_INDEX] try: response = requests.get(url, params=params, timeout=10) data = response.json() if data['status'] == '0' and 'DAILY_QUERY_OVER_LIMIT' in data.get('info', ''): CURRENT_KEY_INDEX = (CURRENT_KEY_INDEX + 1) % len(API_KEYS) continue return data except Exception as e: print(f"请求失败: {str(e)}") CURRENT_KEY_INDEX = (CURRENT_KEY_INDEX + 1) % len(API_KEYS) time.sleep(0.1) return None def get_reachable_area(origin, transport_type): url = "https://restapi.amap.com/v4/direction/reachable" params = { 'origin': origin, 'time': 900, # 15分钟=900秒 'type': TRANSPORT_CONFIG[transport_type]['type'], 'speed': TRANSPORT_CONFIG[transport_type]['speed'] } data = request_with_key_rotation(url, params) if data and data.get('status') == '1': return data['data']['reachable'][0]['area'] return None # 主处理流程 def process_data(input_file): df = pd.read_csv(input_file) results = [] for _, row in df.iterrows(): gcj_lng, gcj_lat = wgs84_to_gcj02(row['lng'], row['lat']) origin = f"{gcj_lng},{gcj_lat}" areas = {} for transport in TRANSPORT_CONFIG: areas[transport] = get_reachable_area(origin, transport) time.sleep(0.1) # 控制请求频率 results.append({ '小区名称': row['小区名称'], '自行车面积(平方米)': areas['bicycle'], '小汽车面积(平方米)': areas['car'], '公交车面积(平方米)': areas['bus'] }) result_df = pd.DataFrame(results) result_df.to_csv('output.csv', index=False) return result_df # 执行程序 if __name__ == "__main__": process_data('input.csv')

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import os import gradio as gr import pandas as pd import time from pathlib import Path from datetime import datetime, date ,timedelta import tempfile import shutil from concurrent.futures import ThreadPoolExecutor, as_completed import threading import json from urllib.parse import quote from config import available_models, default_model, degrees, default_degree, GENDER, DEFAULT_GENDER, api_keys, IMAP_HOST, PORT from extract_utils import extract_resume_info, read_html_content from extract_foxmail import EmailResumeDownloader JOB_JSON_PATH = "job_descriptions.json" def update_job_description(selected_job_name): try: with open(JOB_JSON_PATH, "r", encoding="utf-8") as f: job_descriptions_latest = json.load(f) return job_descriptions_latest.get(selected_job_name, "") except Exception as e: print(f"读取岗位描述失败: {e}") return "" def download_resumes_from_mail(start_date_str=None, end_date_str=None): downloader = EmailResumeDownloader( host=IMAP_HOST, port=PORT, user=api_keys["email_user"], password=api_keys["email_pass"] ) downloader.process_emails(since_date=start_date_str, before_date=end_date_str) def process_single_resume(model_name, selected_job, job_description_input, city, file): suffix = Path(file.name).suffix.lower() content = "" temp_path = f"tmp_{threading.get_ident()}{suffix}" shutil.copy(file.name, temp_path) today_date = datetime.today().strftime("%Y-%m-%d") output_folder = os.path.join(os.path.expanduser("~"), 'Desktop', 'processed_resumes', today_date, selected_job) file_path = os.path.join(output_folder, file.name) try: if suffix == ".html": content = read_html_content(temp_path) else: return None if not content.strip(): return None if city: city = f"是否有意愿来{city}发展" job_description_input += city info = extract_resume_info(content, model_name, selected_job, job_description_input) # info["文件名"] = Path(file.name).name info["文件路径"] = file.name if not len(job_description_input): info["辅助匹配"] = 1 print(info) print("="*100) return info finally: if os.path.exists(temp_path): try: os.remove(temp_path) except Exception as e: print(f"删除临时文件 {temp_path} 失败: {e}") def dataframe_to_html_with_links(df: pd.DataFrame) -> str: df_copy = df.copy() if "文件地址" in df_copy.columns: df_copy["文件名"] = df_copy["文件地址"] df_copy.drop(columns=["文件地址"], inplace=True, errors="ignore") return df_copy.to_html(escape=False, index=False) def save_csv_to_folder(df, folder_name, save_dir): if df.empty: return None os.makedirs(save_dir, exist_ok=True) save_path = os.path.join(save_dir, f"{folder_name}.csv") with open(save_path, mode='w', encoding='utf-8-sig', newline='') as f: df.to_csv(f, index=False) temp_download_path = os.path.join(tempfile.gettempdir(), f"{folder_name}.csv") shutil.copy(save_path, temp_download_path) return temp_download_path def process_resumes_mult(model_name, selected_job, degree, job_description_input, work_experience, files, resume_limit, gender, age_min, age_max, city): start_time = time.time() degree_levels = {"大专": 1, "本科": 2, "硕士": 3, "博士": 4, "不限": 0} results, pdf_docx_files, doc_files = [], [], [] today_date = datetime.today().strftime("%Y-%m-%d") output_folder = os.path.join(os.path.expanduser("~"), 'Desktop', 'processed_resumes', today_date, selected_job) os.makedirs(output_folder, exist_ok=True) with ThreadPoolExecutor(max_workers=4) as executor: futures = [ executor.submit(process_single_resume, model_name, selected_job, job_description_input, city, file) for file in files ] for future in as_completed(futures): try: res = future.result() if res: results.append(res) except Exception as e: print(f"简历处理异常: {e}") df_filtered = pd.DataFrame(results) if not df_filtered.empty: if gender != "不限": df_filtered = df_filtered[df_filtered["性别"] == gender] # 年龄筛选:必须先确保有年龄字段 if "年龄" in df_filtered.columns: df_filtered = df_filtered[ (df_filtered["年龄"] >= age_min) & (df_filtered["年龄"] <= age_max) ] df_filtered = df_filtered[ (df_filtered["工作经验"] >= work_experience) & (df_filtered["岗位匹配度"] > 0.5) & (df_filtered["辅助匹配"] > 0.5) ] if degree != "其他": df_filtered = df_filtered[ df_filtered["学历"].map(lambda x: degree_levels.get(x, 0)) >= degree_levels[degree] ] # 合并岗位匹配度和辅助匹配,生成综合匹配得分(范围0~1) df_filtered["综合匹配得分"] = ( df_filtered["岗位匹配度"] / 2 + df_filtered["辅助匹配"] / 2 ).round(2) df_filtered = df_filtered.drop(columns=["岗位匹配度", "辅助匹配"]) df_filtered = df_filtered.sort_values(by="综合匹配得分", ascending=False) if resume_limit > 0: df_filtered = df_filtered.head(resume_limit) file_paths = df_filtered.get("文件路径") file_links = [] for file_path in file_paths: file_path = Path(file_path) file_name = file_path.name target_path = os.path.join(output_folder, file_name) file_path_str = str(file_path).replace("\\", "/") # 复制文件到输出文件夹 if file_path and os.path.exists(file_path): shutil.copy(file_path, target_path) file_links.append(file_path_str) df_filtered["文件地址"] = file_links if "文件路径" in df_filtered.columns: df_filtered = df_filtered.drop(columns=["文件路径"]) elapsed_time = f"{time.time() - start_time:.2f} 秒" return df_filtered, elapsed_time, output_folder def on_import_and_process(model_name, selected_job, degree, job_description_input, work_experience, resume_limit, gender, age_min, age_max, city): desktop = os.path.join(os.path.expanduser("~"), 'Desktop') base_dir = os.path.join(desktop, 'resumes') start_date_val = datetime.today().strftime("%Y-%m-%d") resume_folder = os.path.join(base_dir, start_date_val) file_paths = [] for suffix in [".pdf", ".doc", ".docx", ".html"]: file_paths.extend(Path(resume_folder).rglob(f"*{suffix}")) class UploadedFile: def __init__(self, path): self.name = str(path) files = [UploadedFile(path) for path in file_paths] df_filtered, elapsed_time, output_folder = process_resumes_mult( model_name, selected_job, degree, job_description_input, work_experience, files, resume_limit, gender, age_min, age_max, city ) export_button.interactive = not df_filtered.empty df_html = dataframe_to_html_with_links(df_filtered) return df_html, elapsed_time, df_filtered, output_folder def add_new_job(job_name, job_description): job_name = job_name.strip() job_description = job_description.strip() if not job_name: return "⚠️ 岗位名称不能为空" if not job_description: return "⚠️ 岗位描述不能为空" # 读取原始文件 try: with open("job_descriptions.json", "r", encoding="utf-8") as f: jobs = json.load(f) except Exception as e: return f"❌ 加载 job_descriptions.json 失败: {e}" # 如果岗位已存在 if job_name in jobs: return f"⚠️ 岗位【{job_name}】已存在,请勿重复添加" # 添加岗位 jobs[job_name] = job_description try: with open("job_descriptions.json", "w", encoding="utf-8") as f: json.dump(jobs, f, ensure_ascii=False, indent=2) except Exception as e: return f"❌ 保存失败: {e}" return gr.update(choices=list(jobs.keys())), f"✅ 成功添加岗位【{job_name}】..." def load_job_descriptions(): try: with open(JOB_JSON_PATH, "r", encoding="utf-8") as f: return json.load(f) except: return {} with gr.Blocks(title="📄 智能简历抽取 Test 版") as demo: gr.Markdown("# 📄 智能简历信息抽取") with gr.Row(): model_name = gr.Dropdown(choices=available_models, value=default_model, label="选择语言模型") degree = gr.Dropdown(choices=degrees, value=default_degree, label='学历') job_descriptions = load_job_descriptions() selected_job = gr.Dropdown(choices=list(job_descriptions.keys()), label="岗位") work_experience = gr.Slider(0, 10, value=0, step=1, label="工作经验(年数)") resume_limit = gr.Dropdown(choices=[0, 5, 10, 15, 20], value=0, label="筛选简历(0 不限制)") # 在原 Gradio UI 中添加年龄筛选区间组件: with gr.Row(): gender = gr.Dropdown(choices=GENDER, value=DEFAULT_GENDER, label='性别') city = gr.Textbox(label="城市", placeholder="请输入招聘城市名称,如 徐州") age_min = gr.Slider(18, 65, value=0, step=1, label="年龄下限") age_max = gr.Slider(18, 65, value=100, step=1, label="年龄上限") # city = gr.Textbox(label="城市", placeholder="请输入招聘城市名称,如 徐州") with gr.Accordion("➕ 添加新岗位", open=False): new_job_name = gr.Textbox(label="新岗位名称", placeholder="请输入岗位名称,如 销售经理") new_job_description = gr.Textbox( label="新岗位描述", lines=6, placeholder="请输入该岗位的要求、职责描述等,可用于简历辅助匹配" ) add_job_button = gr.Button("✅ 确认添加") add_job_output = gr.Markdown("") job_description_populate = gr.Textbox(label="岗位描述(可加入更多筛选需求)", placeholder="请输入岗位职责或要求,可用于辅助匹配", lines=3) add_job_button.click( fn=add_new_job, inputs=[new_job_name, new_job_description], outputs=[selected_job, add_job_output] ) today_str = str(date.today()) with gr.Row(): date_range = gr.Radio( choices=["今天", "最近三天", "最近一周", "最近一个月", "自定义时间段"], value="今天", label="筛选邮件时间范围" ) read_status = gr.Radio( choices=["全部", "未读", "已读"], value="全部", label="邮件读取状态" ) with gr.Row(visible=False) as custom_date_row: start_date = gr.Textbox(value=today_str, label="起始日期(格式:2025-07-16)") end_date = gr.Textbox(value=today_str, label="结束日期(格式:2025-07-16)") def toggle_date_inputs(date_range_value): return gr.update(visible=(date_range_value == "自定义时间段")) date_range.change(toggle_date_inputs, inputs=date_range, outputs=custom_date_row) with gr.Row(): import_button = gr.Button("📂 下载简历") process_button = gr.Button("🔍 开始处理") export_button = gr.Button("📥 导出筛选结果", interactive=True) download_notice = gr.Markdown(value="") # result_table = gr.Dataframe(label="筛选结果", interactive=False) result_table = gr.HTML(label="筛选结果") elapsed_time_display = gr.Textbox(label="耗时", interactive=False) output_folder_state = gr.State() result_state = gr.State() # 选岗位时更新岗位描述 def update_job_description(selected_job_name): job_descriptions = load_job_descriptions() if not selected_job_name or selected_job_name not in job_descriptions: return "" job_descriptions = load_job_descriptions() return job_descriptions[selected_job_name] selected_job.change( fn=update_job_description, inputs=[selected_job], outputs=[job_description_populate] ) def on_download_and_import(model_name, selected_job, degree, job_description_input, work_experience, resume_limit, gender, age_min, age_max, city): return on_import_and_process(model_name, selected_job, degree, job_description_input, work_experience, resume_limit, gender, age_min, age_max, city) def show_downloading_text(): return "⏳ 开始下载中..." def on_download_email(date_range_value, start_date_val, end_date_val, read_status_val): today = datetime.today().date() if date_range_value == "今天": start = today end = today elif date_range_value == "最近三天": start = today - timedelta(days=2) end = today elif date_range_value == "最近一周": start = today - timedelta(days=6) end = today elif date_range_value == "最近一个月": start = today - timedelta(days=29) end = today elif date_range_value == "自定义时间段": try: start = datetime.strptime(start_date_val, "%Y-%m-%d").date() end = datetime.strptime(end_date_val, "%Y-%m-%d").date() except Exception: return "⚠️ 自定义时间格式错误,请使用 YYYY-MM-DD" else: return "⚠️ 未知时间范围选项" # 邮件读取状态控制 unseen_only = None if read_status_val == "未读": unseen_only = True elif read_status_val == "已读": unseen_only = False downloader = EmailResumeDownloader( host=IMAP_HOST, port=PORT, user=api_keys["email_user"], password=api_keys["email_pass"] ) downloader.process_emails( since_date=start.strftime("%Y-%m-%d"), before_date=(end + timedelta(days=1)).strftime("%Y-%m-%d"), # 邮件before_date是“非包含” unseen_only=unseen_only ) return f"📥 已下载 {start} 至 {end} 区间、状态为 [{read_status_val}] 的简历 ✅" import_button.click( fn=show_downloading_text, outputs=[download_notice] ).then( fn=on_download_email, inputs=[date_range, start_date, end_date, read_status], outputs=[download_notice] ) process_button.click( fn=on_download_and_import, inputs=[model_name, selected_job, degree, job_description_populate, work_experience, resume_limit, gender, age_min, age_max, city], outputs=[result_table, elapsed_time_display, result_state, output_folder_state] ) def export_csv(df, selected_job, output_folder): return save_csv_to_folder(df, selected_job, output_folder) export_button.click( fn=export_csv, inputs=[result_state, selected_job, output_folder_state], outputs=gr.File(label="下载 CSV") ) if __name__ == "__main__": demo.launch(server_name="0.0.0.0", share=True, debug=True, allowed_paths=[os.path.join(os.path.expanduser("~"), 'Desktop')])如何在result_table = gr.HTML(label="筛选结果")每行后面添加一个按钮,按钮使用gradio库进行添加,然后绑定这一行文件位置这一数值进行触发

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