{{ summary }}
{{ experience }}
{{ education }}
``` This template will be used to generate a styled HTML résumé before converting it into a PDF. --- ## Step 2: Build the FastAPI Endpoint We’ll create a FastAPI application that enhances the résumé text with OpenAI, renders the HTML template, and generates a PDF with WeasyPrint. ```python import os import openai from dotenv import load_dotenv from fastapi import FastAPI, HTTPException, Response from fastapi.responses import StreamingResponse from pydantic import BaseModel from jinja2 import Environment, FileSystemLoader from weasyprint import HTML import io # Load environment variables load_dotenv() openai.api_key = os.getenv("OPENAI_API_KEY") app = FastAPI(title="ResumeBurger PDF Generator") # Define request model class ResumeData(BaseModel): name: str summary: str experience: str education: str job_description: str # This could be used to tailor the résumé further def enhance_resume_section(text: str, job_description: str) -> str: prompt = ( f"Enhance the following résumé section to better align with the job description provided.\n\n" f"Section text:\n{text}\n\nJob description:\n{job_description}\n\n" "Provide a refined, professional version." ) try: response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=[{"role": "user", "content": prompt}], max_tokens=150, temperature=0.7, ) return response.choices[0].message.content.strip() except Exception as e: raise Exception(f"OpenAI API error: {e}") @app.post("/generate-resume-pdf") async def generate_resume_pdf(data: ResumeData): try: # Enhance résumé sections using OpenAI enhanced_summary = enhance_resume_section(data.summary, data.job_description) enhanced_experience = enhance_resume_section(data.experience, data.job_description) enhanced_education = enhance_resume_section(data.education, data.job_description) except Exception as error: raise HTTPException(status_code=500, detail=str(error)) # Set up Jinja2 environment to load HTML template env = Environment(loader=FileSystemLoader('.')) template = env.get_template("resume_template.html") html_content = template.render( name=data.name, summary=enhanced_summary, experience=enhanced_experience, education=enhanced_education ) # Generate PDF using WeasyPrint pdf_file = HTML(string=html_content).write_pdf() pdf_stream = io.BytesIO(pdf_file) pdf_stream.seek(0) headers = { "Content-Disposition": f"attachment; filename={data.name.replace(' ', '_')}_Resume.pdf" } return StreamingResponse(pdf_stream, media_type="application/pdf", headers=headers) if __name__ == "__main__": import uvicorn uvicorn.run("your_script_name:app", host="0.0.0.0", port=8000, reload=True) ``` Replace `"your_script_name"` with your file name (without the `.py` extension). ### How It Works - **Enhancing Text:** The `enhance_resume_section` function sends each résumé section (summary, experience, education) along with the job description to OpenAI. It refines the text for clarity and impact. - **Template Rendering:** The enhanced sections are injected into the `resume_template.html` file using Jinja2, producing a well-formatted HTML document. - **PDF Generation:** WeasyPrint converts the HTML into a PDF, which is then returned as a downloadable file via FastAPI. --- ## Step 3: Testing and Deployment - **Local Testing:** Run the application with: ```bash uvicorn your_script_name:app --reload ``` Send a POST request to `http://localhost:8000/generate-resume-pdf` with a JSON body: ```json { "name": "Jane Doe", "summary": "Experienced software developer with a background in AI.", "experience": "5 years of experience at tech companies.", "education": "Bachelor's degree in Computer Science.", "job_description": "Looking for a candidate with strong AI, machine learning, and cloud computing skills." } ``` The API will return a PDF file that you can download and review. - **Docker Deployment (Optional):** Containerize your application using a Dockerfile for scalable deployment. --- ## Conclusion With this tutorial, you’ve built a powerful tool that automates the entire résumé PDF generation process—from AI-enhanced text refinement to PDF conversion. By leveraging FastAPI, OpenAI, Jinja2, and WeasyPrint, you can provide job seekers with professional, recruiter-approved résumés in minutes. Customize the templates and enhancement prompts to best suit your audience, integrate with ResumeBurger’s platform, and empower users to stand out in today’s competitive job market. Happy coding, and here’s to landing your dream job with a standout résumé!