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import gradio as gr |
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import pdfplumber |
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import spacy |
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import os |
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try: |
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nlp = spacy.load("en_core_web_sm", disable=["ner", "lemmatizer"]) |
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except Exception as e: |
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raise RuntimeError(f"Failed to load spaCy model: {str(e)}") |
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def generate_portfolio(resume_file): |
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if resume_file is None: |
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return "No file uploaded. Please upload a PDF resume." |
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try: |
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with pdfplumber.open(resume_file) as pdf: |
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text = "".join(page.extract_text() or "" for page in pdf.pages) |
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except Exception as e: |
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return f"Error processing PDF: {str(e)}" |
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doc = nlp(text) |
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data = { |
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"name": "", |
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"summary": "", |
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"experience": [], |
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"skills": [], |
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"contact": "" |
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} |
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for ent in doc.ents: |
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if ent.label_ == "PERSON" and not data["name"]: |
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data["name"] = ent.text |
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elif ent.label_ == "ORG": |
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data["experience"].append({"company": ent.text, "role": ""}) |
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elif ent.label_ == "EMAIL": |
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data["contact"] = ent.text |
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skill_keywords = ["python", "javascript", "sql", "communication", "leadership"] |
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data["skills"] = [token.text for token in doc if token.text.lower() in skill_keywords] |
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data["summary"] = text[:200] + "..." |
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os.remove(resume_file) |
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portfolio_html = f""" |
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<!DOCTYPE html> |
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<html lang="en"> |
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<head> |
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<meta charset="UTF-8"> |
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<meta name="viewport" content="width=device-width, initial-scale=1.0"> |
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<title>{data['name']}'s Portfolio</title> |
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<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/css/bootstrap.min.css" rel="stylesheet"> |
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</head> |
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<body> |
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<header class="bg-primary text-white text-center py-5"> |
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<h1>{data['name']}</h1> |
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<p>Professional Portfolio</p> |
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</header> |
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<div class="container my-5"> |
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<section> |
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<h2>About Me</h2> |
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<p>{data['summary']}</p> |
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</section> |
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<section> |
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<h2>Experience</h2> |
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{''.join([f'<div class="card mb-3"><div class="card-body"><h5 class="card-title">{exp["company"]}</h5><p class="card-text">{exp["role"]}</p></div></div>' for exp in data['experience']])} |
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</section> |
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<section> |
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<h2>Skills</h2> |
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<ul class="list-group"> |
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{''.join([f'<li class="list-group-item">{skill}</li>' for skill in data['skills']])} |
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</ul> |
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</section> |
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<section> |
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<h2>Contact</h2> |
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<p>Email: {data['contact']}</p> |
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</section> |
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</div> |
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<footer class="bg-dark text-white text-center py-3"> |
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<p>© 2025 {data['name']}</p> |
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<p>Generated by Resume-to-Portfolio AI. Your data was processed securely and not retained.</p> |
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</footer> |
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<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/js/bootstrap.bundle.min.js"></script> |
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</body> |
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</html> |
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""" |
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return portfolio_html |
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interface = gr.Interface( |
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fn=generate_portfolio, |
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inputs=gr.inputs.File(label="Upload Resume (PDF)"), |
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outputs=gr.outputs.HTML(label="Your Portfolio"), |
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title="Resume to Portfolio Generator", |
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description="Upload your resume to generate a portfolio landing page. Your data is processed securely and not stored.", |
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allow_flagging="never" |
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) |
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interface.launch() |
