Spaces:
Sleeping
Sleeping
| import os | |
| import sys | |
| import shutil | |
| import datetime | |
| import json | |
| import gradio as gr | |
| # Ensure `src` is in Python's module search path | |
| sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "src"))) | |
| from markdown_pdf import MarkdownPdf, Section | |
| from gradio_pdf import PDF | |
| from resume_crew.crew import ResumeCrew | |
| from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource | |
| # Set backend directories for Hugging Face Spaces | |
| UPLOAD_DIR = "/tmp/uploads" | |
| OUTPUT_DIR = "/tmp/output" | |
| os.makedirs(UPLOAD_DIR, exist_ok=True) | |
| os.makedirs(OUTPUT_DIR, exist_ok=True) | |
| def convert_md_to_pdf(md_path: str) -> str: | |
| """ | |
| Convert a local .md file to .pdf using markdown-pdf. | |
| Returns the resulting PDF file path, or an empty string if conversion fails. | |
| """ | |
| if not os.path.isfile(md_path): | |
| return "" | |
| with open(md_path, "r", encoding="utf-8") as f: | |
| md_content = f.read() | |
| pdf_obj = MarkdownPdf(toc_level=2) | |
| pdf_obj.add_section(Section(md_content)) | |
| pdf_path = os.path.splitext(md_path)[0] + ".pdf" | |
| pdf_obj.save(pdf_path) | |
| return pdf_path if os.path.isfile(pdf_path) else "" | |
| def process_resume(openai_api_key, serper_api_key, model_choice, new_resume, company_name, job_url): | |
| """ | |
| Processes the uploaded resume using ResumeCrew and converts the output Markdown files to PDFs. | |
| Handles errors gracefully and stops execution upon failure. | |
| """ | |
| try: | |
| current_date = datetime.datetime.now().strftime("%Y%m%d") | |
| # --- Ensure a resume file is uploaded --- | |
| if new_resume is None or not (hasattr(new_resume, "name") and new_resume.name.strip() != ""): | |
| return ("Error: Please upload a resume.", None, None, None, None, None, None) | |
| # --- Set API keys --- | |
| os.environ["OPENAI_API_KEY"] = openai_api_key or "" | |
| os.environ["SERPER_API_KEY"] = serper_api_key or "" | |
| # --- Save uploaded file --- | |
| try: | |
| if hasattr(new_resume, "read"): | |
| original_filename = os.path.basename(new_resume.name) | |
| file_data = new_resume.read() | |
| else: | |
| original_filename = os.path.basename(new_resume) | |
| file_data = None | |
| base_filename, ext = os.path.splitext(original_filename) | |
| new_resume_filename = f"{base_filename}_{current_date}{ext}" | |
| physical_path = os.path.join("knowledge", new_resume_filename) | |
| os.makedirs("knowledge", exist_ok=True) | |
| if file_data is not None: | |
| with open(physical_path, "wb") as f: | |
| f.write(file_data) | |
| else: | |
| shutil.copy(new_resume, physical_path) | |
| except Exception as e: | |
| return (f"Error saving the uploaded resume: {str(e)}", None, None, None, None, None, None) | |
| # --- Initialize ResumeCrew --- | |
| try: | |
| crew_instance = ResumeCrew( | |
| model=model_choice, | |
| openai_api_key=openai_api_key, | |
| serper_api_key=serper_api_key, | |
| resume_pdf_path=new_resume_filename | |
| ) | |
| except Exception as e: | |
| return (f"Error initializing ResumeCrew: {str(e)}", None, None, None, None, None, None) | |
| # --- Run the resume processing --- | |
| try: | |
| crew_instance.crew().kickoff(inputs={'job_url': job_url, 'company_name': company_name}) | |
| except Exception as e: | |
| return (f"Error during resume processing: {str(e)}", None, None, None, None, None, None) | |
| # --- Retrieve output files --- | |
| try: | |
| job_analysis_path = os.path.join("output", "job_analysis.json") | |
| with open(job_analysis_path, "r") as f: | |
| job_data = json.load(f) | |
| position_name = job_data.get("job_title", "position") | |
| except Exception: | |
| position_name = "position" | |
| optimized_resume_path = os.path.join("output", "optimized_resume.md") | |
| candidate_name = "candidate" | |
| try: | |
| with open(optimized_resume_path, "r") as f: | |
| first_line = f.readline() | |
| if first_line.startswith("#"): | |
| candidate_name = first_line.lstrip("#").strip().replace(" ", "_") | |
| except Exception: | |
| candidate_name = "candidate" | |
| # --- Create the output folder --- | |
| try: | |
| folder_name = f"{company_name}_{position_name}_{candidate_name}_{current_date}" | |
| new_output_dir = os.path.join("output", folder_name) | |
| os.makedirs(new_output_dir, exist_ok=True) | |
| for filename in os.listdir("output"): | |
| file_path = os.path.join("output", filename) | |
| if file_path == new_output_dir: | |
| continue | |
| if filename.endswith(".json") or filename.endswith(".md"): | |
| if os.path.isfile(file_path): | |
| shutil.move(file_path, os.path.join(new_output_dir, filename)) | |
| except Exception as e: | |
| return (f"Error organizing output files: {str(e)}", None, None, None, None, None, None) | |
| # --- Convert Markdown to PDF --- | |
| def md_to_pdf_in_dir(md_filename): | |
| try: | |
| md_path = os.path.join(new_output_dir, md_filename) | |
| if os.path.isfile(md_path): | |
| return convert_md_to_pdf(md_path) | |
| return "" | |
| except Exception as e: | |
| return f"Error converting {md_filename} to PDF: {str(e)}" | |
| pdf_opt = md_to_pdf_in_dir("optimized_resume.md") | |
| pdf_final = md_to_pdf_in_dir("final_report.md") | |
| pdf_int = md_to_pdf_in_dir("interview_questions.md") | |
| message = f"Processing completed using model {model_choice}. Output saved in: {new_output_dir}" | |
| return (message, pdf_opt, pdf_opt, pdf_final, pdf_final, pdf_int, pdf_int) | |
| except Exception as e: | |
| return (f"Unexpected error: {str(e)}", None, None, None, None, None, None) | |
| # --- Define available models --- | |
| model_choices = { | |
| "GPT-4o-mini": "gpt-4o-mini-2024-07-18", | |
| "GPT-4o": "gpt-4o-2024-08-06", | |
| "o3-mini": "o3-mini-2025-01-31", | |
| "o1-mini": "o1-mini-2024-09-12" | |
| } | |
| with gr.Blocks(css=".output-column { width: 700px; }") as demo: | |
| with gr.Row(): | |
| # Left pane: Input fields | |
| with gr.Column(scale=1): | |
| gr.Markdown("## Resume Optimization System") | |
| gr.Markdown( | |
| "Create an optimized resume, job research report, and interview question sheet " | |
| "by simply uploading your resume, entering the company name, and providing the job posting URL. " | |
| "This tool leverages multi-agentic AI and web search to analyze job descriptions, research the company, and " | |
| "tailor your resume for better ATS compatibility and job relevance." | |
| ) | |
| openai_api_key_input = gr.Textbox(label="OpenAI API Key", type="password", placeholder="Enter OpenAI API Key") | |
| serper_api_key_input = gr.Textbox(label="Serper API Key", type="password", placeholder="Enter Serper API Key") | |
| model_dropdown = gr.Dropdown( | |
| choices=list(model_choices.values()), | |
| label="Select Model", | |
| value="gpt-4o-2024-08-06", | |
| interactive=True, | |
| info="Select the model to use for processing." | |
| ) | |
| new_resume_file = gr.File(label="Upload New Resume PDF", file_types=[".pdf"]) | |
| company_name_text = gr.Textbox(label="Company Name", placeholder="Enter company name") | |
| job_url_text = gr.Textbox(label="Job URL", placeholder="Enter job posting URL") | |
| run_button = gr.Button("Run") | |
| # Right pane: Output display | |
| with gr.Column(scale=2, elem_classes="output-column"): # Scale set to an integer to avoid warnings | |
| gr.Markdown("## Processing Status") | |
| status_output = gr.Textbox(label="Status") | |
| with gr.Tabs(): | |
| with gr.Tab("Optimized Resume PDF"): | |
| pdf_opt_download = gr.File(label="Download Optimized Resume") | |
| pdf_opt_viewer = PDF(label="View Optimized Resume") | |
| with gr.Tab("Final Report PDF"): | |
| pdf_final_download = gr.File(label="Download Final Report") | |
| pdf_final_viewer = PDF(label="View Final Report") | |
| with gr.Tab("Interview Questions PDF"): | |
| pdf_int_download = gr.File(label="Download Interview Questions") | |
| pdf_int_viewer = PDF(label="View Interview Questions") | |
| run_button.click( | |
| process_resume, | |
| inputs=[ | |
| openai_api_key_input, | |
| serper_api_key_input, | |
| model_dropdown, | |
| new_resume_file, | |
| company_name_text, | |
| job_url_text | |
| ], | |
| outputs=[ | |
| status_output, | |
| pdf_opt_viewer, pdf_opt_download, | |
| pdf_final_viewer, pdf_final_download, | |
| pdf_int_viewer, pdf_int_download | |
| ] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |