app.py

import os
import re
import markdown
import gradio as gr
from weasyprint import HTML
from markitdown import MarkItDown
from cerebras.cloud.sdk import Cerebras

# Pastikan file style.css tersedia sesuai path (misalnya di folder "resumes" atau di direktori yang sama)

# Dapatkan API key dari environment variables
api_key = os.environ.get("CEREBRAS_API_KEY")

# Inisialisasi MarkItDown dengan semua optional dependencies (pastikan Anda telah menginstal 'markitdown[all]')
md_converter = MarkItDown(enable_plugins=True)

def create_prompt(resume_string: str, jd_string: str) -> str:
    """
    Membuat prompt detail untuk AI agar melakukan optimasi resume
    berdasarkan job description.
    """
    return f"""
You are a professional resume optimization expert specializing in tailoring resumes to specific job descriptions. Your goal is to optimize my resume and provide actionable suggestions for improvement to align with the target role.

### Guidelines:
1. **Relevance**:  
   - Prioritize experiences, skills, and achievements **most relevant to the job description**.  
   - Remove or de-emphasize irrelevant details to ensure a **concise** and **targeted** resume.
   - Limit work experience section to 2-3 most relevant roles
   - Limit bullet points under each role to 2-3 most relevant impacts

2. **Action-Driven Results**:  
   - Use **strong action verbs** and **quantifiable results** (e.g., percentages, revenue, efficiency improvements) to highlight impact.  

3. **Keyword Optimization**:  
   - Integrate **keywords** and phrases from the job description naturally to optimize for ATS (Applicant Tracking Systems).  

4. **Additional Suggestions** *(If Gaps Exist)*:  
   - If the resume does not fully align with the job description, suggest:  
     1. **Additional technical or soft skills** that I could add to make my profile stronger.  
     2. **Certifications or courses** I could pursue to bridge the gap.  
     3. **Project ideas or experiences** that would better align with the role.  

5. **Formatting**:  
   - Output the tailored resume in **clean Markdown format**.  
   - Include an **"Additional Suggestions"** section at the end with actionable improvement recommendations.  

---

### Input:
- **My resume**:  
{resume_string}

- **The job description**:  
{jd_string}

---

### Output:  
1. - A resume in **Markdown format** that emphasizes relevant experience, skills, and achievements.  
   - Incorporates job description **keywords** to optimize for ATS.  
   - Uses strong language and is no longer than **one page**.

2. **Additional Suggestions** *(if applicable)*:  
   - List **skills** that could strengthen alignment with the role.  
   - Recommend **certifications or courses** to pursue.  
   - Suggest **specific projects or experiences** to develop.
"""

def get_resume_response(prompt: str, api_key: str, model: str = "llama-3.3-70b", temperature: float = 0.7) -> str:
    """
    Mengirim prompt ke model Cerebras (LLM) dan mengembalikan hasil streaming response.
    """
    client = Cerebras(api_key=api_key)
    stream = client.chat.completions.create(
        messages=[
            {"role": "system", "content": "Expert resume writer"},
            {"role": "user", "content": prompt}
        ],
        model=model,
        stream=True,
        temperature=temperature,
        max_completion_tokens=1024,
        top_p=1
    )

    response_string = ""
    for chunk in stream:
        response_string += chunk.choices[0].delta.content or ""
    return response_string

def remove_unwanted_headings(markdown_text: str) -> str:
    """
    Menghapus heading apa pun yang mengandung kata 'resume' atau 'optimized'
    (dalam berbagai huruf besar/kecil).
    Contoh heading yang akan dihapus:
    # Resume
    ## optimized
    ### Optimized Resume
    dsb.
    """
    pattern = r'^#+.*\b(?:[Rr]esume|[Oo]ptimized)\b.*$'
    return re.sub(pattern, '', markdown_text, flags=re.MULTILINE)

def process_resume(resume, jd_string):
    """
    Memproses file resume yang di-upload dan job description, lalu
    menghasilkan resume yang telah dioptimasi + saran perbaikan.
    """
    # Gunakan file-like stream dari upload (tanpa cek ekstensi, dukung semua format yang didukung MarkItDown)
    try:
        result = md_converter.convert(resume.file)
        resume_string = result.text_content  # konten Markdown hasil konversi
    except Exception as e:
        return f"Conversion failed: {str(e)}", "", "", "", ""
    
    # Buat prompt untuk AI
    prompt = create_prompt(resume_string, jd_string)

    # Dapatkan response dari AI
    response_string = get_resume_response(prompt, api_key)

    # Pisahkan response menjadi "optimized resume" dan "additional suggestions"
    response_list = response_string.split("## Additional Suggestions")
    new_resume = response_list[0].strip()
    # Ganti tanda asterisk di awal baris menjadi tanda "-" untuk bullet list standar
    new_resume = re.sub(r'^\* ', '- ', new_resume, flags=re.MULTILINE)
    suggestions = "## Additional Suggestions\n\n" + response_list[1].strip() if len(response_list) > 1 else ""

    # ===== Hapus heading yang mengandung kata resume/optimized =====
    new_resume = new_resume.replace("# Optimized Resume", "")
    new_resume = new_resume.replace("## Optimized Resume", "")
    new_resume = new_resume.replace("Optimized Resume", "")
    new_resume = new_resume.replace("# Resume", "")
    new_resume = new_resume.replace("## Resume", "")
    new_resume = re.sub(r'^#+\s*Resume\s*', '', new_resume, flags=re.MULTILINE)
    new_resume = remove_unwanted_headings(new_resume)
    # ===============================================================

    # Simpan resume asli (Markdown) jika diperlukan
    original_resume_path = "resumes/original_resume.md"
    with open(original_resume_path, "w", encoding='utf-8') as f:
        f.write(resume_string)
    
    # Simpan resume hasil optimasi (Markdown)
    optimized_resume_path = "resumes/optimized_resume.md"
    with open(optimized_resume_path, "w", encoding='utf-8') as f:
        f.write(new_resume)

    # Kembalikan output untuk di-render di Gradio
    return resume_string, new_resume, original_resume_path, optimized_resume_path, suggestions

def export_resume(new_resume):
    """
    Meng-export resume hasil optimasi (Markdown) menjadi PDF
    menggunakan WeasyPrint.
    """
    try:
        # Konversi Markdown ke HTML dengan ekstensi tambahan agar format terjaga
        html_content = markdown.markdown(new_resume, extensions=['extra', 'nl2br'])

        # Path output PDF
        output_pdf_file = "resumes/optimized_resume.pdf"

        # Gunakan stylesheet (pastikan path style.css benar)
        HTML(string=html_content).write_pdf(
            output_pdf_file,
            stylesheets=["resumes/style.css"]
        )

        return output_pdf_file
    except Exception as e:
        return f"Failed to export resume: {str(e)} 💔"

# Bangun aplikasi Gradio
with gr.Blocks() as app:
    gr.Markdown("# Resume Optimizer 📄")
    gr.Markdown("Upload your resume, paste the job description, and get actionable insights!")

    with gr.Row():
        resume_input = gr.File(label="Upload Your Resume")    
        jd_input = gr.Textbox(
            label="Paste the Job Description Here",
            lines=9,
            interactive=True,
            placeholder="Paste job description..."
        )

    run_button = gr.Button("Optimize Resume 🤖")

    with gr.Row():
        before_md = gr.Markdown(label="Original Resume (Before)")
        after_md = gr.Markdown(label="Optimized Resume (After)")
        output_suggestions = gr.Markdown(label="Suggestions")

    with gr.Row():
        download_before = gr.File(label="Download Original Resume")
        download_after = gr.File(label="Download Optimized Resume")
        
    export_button = gr.Button("Export Optimized Resume as PDF 🚀")
    export_result = gr.File(label="Download PDF")

    # Saat tombol Optimize Resume diklik
    run_button.click(
        process_resume,
        inputs=[resume_input, jd_input],
        outputs=[before_md, after_md, download_before, download_after, output_suggestions]
    )

    # Saat tombol Export PDF diklik
    export_button.click(
        export_resume,
        inputs=[after_md],
        outputs=[export_result]
    )

app.launch()