import openai import gradio as gr import PyPDF2 import docx from docx import Document import os from docx import Document from docx.shared import Pt from docx.enum.text import WD_UNDERLINE from docx.enum.text import WD_ALIGN_PARAGRAPH openai.api_key = "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" messages = [] latest_resume_text = "" desired_position = "" default_text_1 = "请帮我总结我上传的简历" default_text_2 = "请帮我把我上传的简历进行脱敏处理。" default_text_3 = "请帮我阅读候选人简历并总结出以下几个部分。第一个板块是“个人信息”，请总结出此后选人的姓名，性别，工作经验（多少年），最高学历，毕业院校，专业，和毕业时间。第二个部分是此候选人的本人评价，放进一个自然段里。第三个部分是具体得工作经历。第四个部分是做过的项目经验。请根据你对于这位候选人简历的最细致的阅读排出以上几个部分。每个部分由自己的标题：五个标题为 个人信息，本人评价，工作经历，和项目经验。请把每一个小项写得细致一点，并且用数字排序！" def set_text_1(): return default_text_1 def set_text_2(): return default_text_2 def set_text_3(): return default_text_3 # # extracting text from pdf # def extract_text_from_pdf(file): # reader = PyPDF2.PdfReader(file) # text = "" # for page in reader.pages: # text += page.extract_text() # return text #write a function that extract text from a pdf that could contain multiple pages def extract_text_from_pdf(file): reader = PyPDF2.PdfReader(file) text = "" for page in reader.pages: text += page.extract_text() return text # extracting text from doc def extract_text_from_docx(file): doc = docx.Document(file) all_text = [] for para in doc.paragraphs: # Extract the text from the current paragraph paragraph_text = para.text all_text.append(paragraph_text) combined_text = " ".join(all_text) return combined_text def handle_file_upload(uploaded_files): combined_text = "" if uploaded_files is None: return "" for uploaded_file in uploaded_files: file_type = uploaded_file.name.split('.')[-1].lower() if file_type == 'pdf': combined_text += extract_text_from_pdf(uploaded_file) elif file_type in ['docx', 'doc']: combined_text += extract_text_from_docx(uploaded_file) else: combined_text += "Unsupported file format, please upload a PDF or Word document.\n" return combined_text def clear_inputs(): messages = [] return "", None def log_conversation(input, reply): with open("log.csv", "a") as log_file: log_file.write(f"user_input: {input},\n\n Chatgpt: {reply}\n\n") import re from docx import Document from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.shared import RGBColor from docx.enum.text import WD_PARAGRAPH_ALIGNMENT def is_chinese_char(char): """Check if a given character is a Chinese character.""" return '\u4e00' <= char <= '\u9fff' def count_chinese_chars(text): """Count the number of Chinese characters in a string.""" return sum(is_chinese_char(char) for char in text) def generate_resume_document(latest_resume_text): # Define keywords that indicate the start of a new section section_keywords = ["个人信息", "本人评价", "工作经历", "项目经验"] # Create a new Document doc = Document() # Add a title to the document heading = doc.add_heading('候选人简历', level=0) run = heading.add_run() run.underline = WD_UNDERLINE.SINGLE # Center-align the heading paragraph_format = heading.paragraph_format paragraph_format.alignment = WD_ALIGN_PARAGRAPH.CENTER # Check if the text is empty if not latest_resume_text.strip(): doc.add_paragraph("No resume text found") return None # Remove non-word characters from the text except for spaces and new lines # Remove non-word characters from the text except for "-", ",", "." and numbers cleaned_text = re.sub(r'[^\w\s\-\,\.\d]', '', latest_resume_text) # Split the resume text into lines lines = cleaned_text.split('\n') # Variables to store the current section title and its content current_section = None current_content = [] # Function to add a section to the document with bullet points def add_section_to_doc(section, content): global name if section and content: sec = doc.add_heading(section, level=0) for run in sec.runs: run.font.size = Pt(14) if section == "个人信息": # Combine all content lines into a single line separated by a semicolon combined_content = '；'.join(content) p = doc.add_paragraph() # Create a new paragraph for combined content last_index = 0 # Keep track of the last index processed for keyword in ["姓名", "性别", "工作经验", "最高学历", "毕业院校", "专业", "毕业时间"]: if keyword in combined_content: start_index = combined_content.index(keyword, last_index) # Add text before the keyword as a normal run p.add_run(combined_content[last_index:start_index]) # Add the keyword and the colon as a bold run bold_run = p.add_run(keyword + '：') bold_run.bold = True # Update the last index processed last_index = start_index + len(keyword) if keyword == "姓名": name_start_index = last_index try: # Try to find the start index of the next keyword "性别" name_end_index = combined_content.index("性别", name_start_index) # Store the content into name variable name = combined_content[name_start_index:name_end_index] # Remove any non-Chinese characters name = re.sub(r'[^\u4e00-\u9fff]', '', name) except ValueError: # Handle the case where "性别" is not found name = "需手动填写" # Add any remaining text after the last keyword p.add_run(combined_content[last_index:]) else: for line in content: # # Count Chinese characters in the line # chinese_char_count = count_chinese_chars(line) # if chinese_char_count < 5: # doc.add_heading(line, level=0) # p = doc.add_paragraph() # p.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER # for run in sec.runs: # run.font.size = Pt(14) # else: doc.add_paragraph(line) for line in lines: # Check for section titles (considering case-insensitivity) if any(line.strip().upper() == keyword for keyword in section_keywords): # Add the previous section to the document before starting a new one add_section_to_doc(current_section, current_content) current_section = line.strip().title() # Title Case for headings current_content = [] else: # Clean line and add to current content clean_line = line.strip() if clean_line: # Ignore empty lines # Append clean_line to current_content, removing any additional internal line breaks current_content.append(re.sub(r'\s+', ' ', clean_line)) # Add the last section to the document add_section_to_doc(current_section, current_content) # Save the document word_filename = "博网科技-" + name + "-" + desired_position + ".docx" doc.save(word_filename) return word_filename # create a function that automatically convert a docx file to a pdf file def convert_to_pdf(word_filename): pdf_filename = word_filename.replace(".docx", ".pdf") doc = docx.Document(word_filename) doc.save(pdf_filename) return pdf_filename def CustomChatGPT(user_input, uploaded_file): global messages, latest_resume_text, desired_position, name # Declare latest_resume_text as global if it's used globally resume_text = "" resume_text = handle_file_upload(uploaded_file) print("Resume text from the file:", resume_text) if resume_text: messages.append({"role": "system", "content": resume_text}) # if the resume text contains "期望职位", extract the desired position and store it in desired_position if "期望职位" in resume_text: match = re.search(r'期望职位：(.+?)\s', resume_text) if match: desired_position = match.group(1) # Remove non-Chinese characters desired_position = re.sub(r'[^\u4e00-\u9fff]', '', desired_position) else: desired_position = "需手动填写" messages.append({"role": "user", "content": user_input}) response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=messages ) ChatGPT_reply = response.choices[0].message.content messages.append({"role": "assistant", "content": ChatGPT_reply}) combined_input = user_input + " " + resume_text log_conversation(combined_input, ChatGPT_reply) latest_resume_text = ChatGPT_reply if "请帮我阅读候选人简历" in user_input: word_filename = generate_resume_document(latest_resume_text) pdf_filename = convert_to_pdf(word_filename) ChatGPT_reply = "Resume generated successfully. Please download the file using the link below." messages = [] else: doc = Document() doc.add_paragraph(ChatGPT_reply) word_filename = "generated_response.docx" doc.save(word_filename) # reset the messages to start a new conversation messages = [] return ChatGPT_reply, word_filename def get_log_file(): return "log.csv" def read_log_file(): if os.path.exists("log.csv"): with open("log.csv", "r") as file: return file.read() else: return "Log file is empty or doesn't exist." def clear_log_file(): if not os.path.exists("log.csv"): return "file not exist" else: with open("log.csv", "w") as log_file: log_file.write("") return "Log cleared" def main(): custom_css = """ """ with gr.Blocks(css=custom_css) as demo: gr.Markdown("# 简历分析系统", elem_classes=["centered-title"]) with gr.Row(): with gr.Column(): text_input = gr.Textbox() file_input = gr.File(file_count="multiple", label="Upload Resume") with gr.Row(): btn1 = gr.Button("总结简历", elem_classes=["custom-button"]) btn2 = gr.Button("脱敏处理", elem_classes=["custom-button"]) btn3 = gr.Button("简历生成", elem_classes=["custom-button"]) btn1.click(fn=set_text_1, inputs=[], outputs=text_input) btn2.click(fn=set_text_2, inputs=[], outputs=text_input) btn3.click(fn=set_text_3, inputs=[], outputs=text_input) with gr.Row(): submit_btn = gr.Button("提交", elem_classes=["custom-button"]) clear_btn = gr.Button("清楚", elem_classes=["custom-button"]) clear_btn.click(fn=clear_inputs, inputs=[], outputs=[]) with gr.Column(): with gr.Row(): output_text = gr.Textbox(label="ChatGPT回复", interactive=True, lines=1) with gr.Row(): output_word = gr.File(label="下载word文件") # output_pdf = gr.File(label="Download PDF File") submit_btn.click(fn=CustomChatGPT, inputs=[text_input, file_input], outputs=[output_text, output_word]) with gr.Row(): log_text = gr.Textbox(label="Log Content", interactive=True, lines=1) with gr.Row(): view_log_button = gr.Button("对话记录", elem_classes=["custom-button"]) download_log_button = gr.Button("下载对话记录", elem_classes=["custom-button"]) clear_log_button = gr.Button("清楚对话记录", elem_classes=["custom-button"]) view_log_button.click(fn=read_log_file, inputs=[], outputs=log_text) download_log_button.click(fn=get_log_file, inputs=[], outputs=[]) clear_log_button.click(fn=clear_log_file, inputs=[], outputs=[]) demo.launch() if __name__ == "__main__": main()