app.py

# Hugging Face Spaces-ready version of your JobPrep.AI classic resume generator
# Upload resume PDF + paste job description → get AI-rewritten resume PDF

import gradio as gr
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
import pdfplumber
from fpdf import FPDF
import re
import os

MODEL_NAME = "MBZUAI/LaMini-Flan-T5-783M"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
generator = pipeline("text2text-generation", model=model, tokenizer=tokenizer)

# Extract raw resume text from uploaded PDF
def extract_text(pdf_file):
    with pdfplumber.open(pdf_file.name) as pdf:
        return "\n".join(page.extract_text() or "" for page in pdf.pages)

# Extract name, email, phone, links from resume
def extract_personal_info(text):
    name = text.strip().split("\n")[0].strip()
    email = re.search(r"[\w\.-]+@[\w\.-]+", text)
    phone = re.search(r"(\+?\d[\d\s\-\(\)]{7,})", text)
    links = re.findall(r"(https?://[^\s]+|www\.[^\s]+)", text)
    location = re.search(r"\b(Dublin|Ireland|[A-Z][a-z]+,?\s?[A-Z][a-z]+?)\b", text)
    return {
        "name": name.upper(),
        "contact": " | ".join(filter(None, [
            email.group(0) if email else "",
            phone.group(0).strip() if phone else "",
            " | ".join(links),
            location.group(0) if location else ""
        ]))
    }

# Rewrite section using LLM
def rewrite_section(title, content, job):
    if not content.strip(): return ""
    prompt = f"""
Rewrite the following section of a resume titled '{title}' to align with the job description below.
Make it concise, professional, and rich in job-relevant keywords.

Section:
{content}

Job Description:
{job}
"""
    result = generator(prompt, max_length=512, do_sample=True, top_p=0.9, temperature=0.7)[0]['generated_text']
    return result.strip()

# Generate and save the final resume PDF
def generate_resume(pdf_file, job_description):
    raw_text = extract_text(pdf_file)
    personal = extract_personal_info(raw_text)

    sections = {
        "Professional Summary": "",
        "Skills": "",
        "Work Experience": "",
        "Projects": "",
        "Education": "",
        "Certifications": "",
        "Leadership": ""
    }

    for sec in sections.keys():
        match = re.search(rf"{sec.upper()}\n(.+?)(?=\n[A-Z ]{{4,}}|\Z)", raw_text, re.DOTALL)
        if match:
            sections[sec] = match.group(1).strip()

    rewritten = {title: rewrite_section(title, content, job_description) for title, content in sections.items()}

    out_path = "final_resume.pdf"
    pdf = FPDF()
    pdf.add_page()
    pdf.set_auto_page_break(auto=True, margin=15)
    pdf.set_font("Arial", size=11)

    pdf.set_font("Arial", style="B", size=14)
    pdf.cell(200, 10, txt=personal['name'], ln=True, align="C")
    pdf.set_font("Arial", size=11)
    pdf.multi_cell(0, 10, personal['contact'], align="C")
    pdf.ln(5)

    for title, body in rewritten.items():
        if not body.strip(): continue
        pdf.set_font("Arial", style="B", size=12)
        pdf.cell(200, 10, txt=title.upper(), ln=True)
        pdf.set_font("Arial", size=11)
        bullets = re.split(r"\s*-\s+", body.strip())
        for bullet in bullets:
            bullet = bullet.strip()
            if bullet:
                pdf.multi_cell(0, 10, f"- {bullet}")
        pdf.ln(4)

    pdf.output(out_path)
    return out_path

# Gradio Interface
demo = gr.Interface(
    fn=generate_resume,
    inputs=[
        gr.File(label="Upload Your Resume PDF", type="filepath"),
        gr.Textbox(label="Paste Job Description", lines=7, placeholder="Enter the job description here")
    ],
    outputs=gr.File(label="Download Final Resume PDF"),
    title="JobPrep.AI | Resume Optimizer",
    description="Upload your resume and tailor it instantly to any job description."
)

demo.launch()