app.py

"""
AI Job Finder — app.py
------------------------------------
Real logic (from the original notebook):
  1. Extract text from the uploaded resume PDF with PyMuPDF (fitz)
  2. Pull live remote job listings from the Remotive API
  3. Embed the resume + job descriptions with SentenceTransformer (all-MiniLM-L6-v2)
  4. Rank jobs by cosine similarity to the resume
  5. Return the top 10 matches

UI: custom gradient theme, glassmorphic cards, gradient button,
    results rendered as styled "job cards" instead of a plain dataframe.

Run with:
    pip install -r requirements.txt
    python app.py
"""

import os
import fitz  # PyMuPDF
import docx  # python-docx
import requests
import gradio as gr

from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

# Hugging Face ZeroGPU Spaces require at least one function decorated with
# @spaces.GPU. If this is running locally or on CPU-only hardware, the
# "spaces" package won't be installed / needed, so we fall back to a
# no-op decorator in that case.
try:
    import spaces
    GPU_DECORATOR = spaces.GPU
except ImportError:
    def GPU_DECORATOR(fn):
        return fn

# ----------------------------------------------------------------------
# 1. LOAD MODEL ONCE AT STARTUP (GPU if available, else CPU)
# ----------------------------------------------------------------------
import torch

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Loading model on device: {DEVICE} ...")
model = SentenceTransformer("all-MiniLM-L6-v2", device=DEVICE)
print("Model loaded successfully.")

REMOTIVE_API_URL = "https://remotive.com/api/remote-jobs"


# ----------------------------------------------------------------------
# 2. CORE LOGIC
# ----------------------------------------------------------------------
def extract_pdf_text(pdf_path: str) -> str:
    doc = fitz.open(pdf_path)
    text = ""
    for page in doc:
        text += page.get_text()
    doc.close()
    return text


def extract_docx_text(docx_path: str) -> str:
    document = docx.Document(docx_path)
    parts = [p.text for p in document.paragraphs]

    # Also pull text out of any tables (resumes sometimes use table layouts)
    for table in document.tables:
        for row in table.rows:
            for cell in row.cells:
                if cell.text:
                    parts.append(cell.text)

    return "\n".join(parts)


def extract_resume_text(file_path: str) -> str:
    ext = os.path.splitext(file_path)[1].lower()

    if ext == ".pdf":
        return extract_pdf_text(file_path)
    elif ext in (".docx",):
        return extract_docx_text(file_path)
    elif ext == ".doc":
        raise gr.Error(
            "Legacy .doc files aren't supported — please save your resume as .docx or .pdf and try again."
        )
    else:
        raise gr.Error("Unsupported file type. Please upload a .pdf or .docx resume.")


def fetch_remote_jobs(limit: int = 100):
    response = requests.get(REMOTIVE_API_URL, timeout=15)
    response.raise_for_status()
    return response.json()["jobs"][:limit]


@GPU_DECORATOR
def find_jobs(resume_file):
    if resume_file is None:
        raise gr.Error("Please upload your resume (PDF or DOCX) before submitting.")

    # --- 1. Extract resume text ---
    resume_text = extract_resume_text(resume_file)
    if not resume_text.strip():
        raise gr.Error("Couldn't extract any text from that file. Try a different resume.")

    # --- 2. Fetch live job listings ---
    try:
        jobs = fetch_remote_jobs(limit=100)
    except Exception:
        raise gr.Error("Couldn't fetch live job listings right now. Please try again shortly.")

    descriptions = [job["description"] for job in jobs]

    # --- 3. Embeddings + similarity (runs on GPU if available) ---
    with torch.no_grad():
        resume_embedding = model.encode(
            resume_text,
            device=DEVICE,
            convert_to_numpy=True,
        )
        job_embeddings = model.encode(
            descriptions,
            device=DEVICE,
            batch_size=32,
            convert_to_numpy=True,
            show_progress_bar=False,
        )

    scores = cosine_similarity([resume_embedding], job_embeddings)[0]

    # --- 4. Rank + build results ---
    results = []
    for i, job in enumerate(jobs):
        results.append({
            "title": job["title"],
            "company": job["company_name"],
            "location": job["candidate_required_location"],
            "score": round(float(scores[i]) * 100, 2),
            "url": job["url"],
        })

    results = sorted(results, key=lambda x: x["score"], reverse=True)[:10]

    # --- 5. Render as HTML job cards ---
    if not results:
        return "<p style='color:#9d9bc7;'>No matching jobs found. Try again later.</p>"

    cards_html = ""
    for job in results:
        cards_html += f"""
        <a href="{job['url']}" target="_blank" style="text-decoration:none;">
            <div class="job-card">
                <div class="job-card-header">
                    <span class="job-title">{job['title']}</span>
                    <span class="job-match">{job['score']}% match</span>
                </div>
                <div class="job-company">{job['company']}</div>
                <div class="job-location">📍 {job['location']}</div>
            </div>
        </a>
        """

    return cards_html


# ----------------------------------------------------------------------
# 3. CUSTOM THEME
# ----------------------------------------------------------------------
theme = gr.themes.Soft(
    primary_hue=gr.themes.colors.violet,
    secondary_hue=gr.themes.colors.indigo,
    neutral_hue=gr.themes.colors.slate,
    font=[gr.themes.GoogleFont("Poppins"), "ui-sans-serif", "sans-serif"],
    font_mono=[gr.themes.GoogleFont("JetBrains Mono"), "monospace"],
).set(
    body_background_fill="linear-gradient(135deg, #0f0c29 0%, #302b63 50%, #24243e 100%)",
    body_background_fill_dark="linear-gradient(135deg, #0f0c29 0%, #302b63 50%, #24243e 100%)",
    block_background_fill="rgba(255,255,255,0.06)",
    block_background_fill_dark="rgba(255,255,255,0.06)",
    block_border_width="1px",
    block_border_color="rgba(255,255,255,0.12)",
    block_radius="20px",
    block_shadow="0 8px 32px rgba(0,0,0,0.35)",
    button_primary_background_fill="linear-gradient(90deg, #7f5af0 0%, #ff6ac1 100%)",
    button_primary_background_fill_hover="linear-gradient(90deg, #6b46e5 0%, #f0499f 100%)",
    button_primary_text_color="#ffffff",
    button_secondary_background_fill="rgba(255,255,255,0.08)",
    button_secondary_background_fill_hover="rgba(255,255,255,0.16)",
    button_secondary_text_color="#e5e5f5",
    input_background_fill="rgba(255,255,255,0.05)",
    body_text_color="#f1f0fb",
    body_text_color_subdued="#b8b6d6",
)


# ----------------------------------------------------------------------
# 4. CUSTOM CSS
# ----------------------------------------------------------------------
custom_css = """
@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@400;500;600;700&display=swap');

* { font-family: 'Poppins', sans-serif !important; }

#app-title {
    text-align: center;
    font-size: 2.4rem;
    font-weight: 700;
    background: linear-gradient(90deg, #a78bfa, #f472b6, #fb923c);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    margin-bottom: 0.2rem;
    letter-spacing: -0.5px;
}

#app-subtitle {
    text-align: center;
    color: #c9c7e8;
    font-size: 1.05rem;
    margin-bottom: 1.8rem;
    font-weight: 400;
}

.upload-card, .output-card {
    border-radius: 22px !important;
    backdrop-filter: blur(14px);
    padding: 6px;
}

#submit-btn {
    font-weight: 600 !important;
    font-size: 1.05rem !important;
    padding: 12px 0 !important;
    border-radius: 14px !important;
    box-shadow: 0 6px 20px rgba(127, 90, 240, 0.45);
    transition: transform 0.15s ease, box-shadow 0.15s ease;
}
#submit-btn:hover {
    transform: translateY(-2px);
    box-shadow: 0 10px 26px rgba(244, 114, 182, 0.5);
}

#clear-btn {
    border-radius: 14px !important;
    font-weight: 500 !important;
    transition: transform 0.15s ease;
}
#clear-btn:hover { transform: translateY(-2px); }

.job-card {
    background: rgba(255,255,255,0.06);
    border: 1px solid rgba(255,255,255,0.12);
    border-radius: 16px;
    padding: 16px 20px;
    margin-bottom: 14px;
    box-shadow: 0 4px 14px rgba(0,0,0,0.25);
    transition: transform 0.15s ease, border-color 0.15s ease;
    cursor: pointer;
}
.job-card:hover {
    transform: translateY(-3px);
    border-color: rgba(167,139,250,0.6);
}
.job-card-header {
    display: flex;
    justify-content: space-between;
    align-items: center;
    margin-bottom: 6px;
    gap: 10px;
}
.job-title {
    font-size: 1.1rem;
    font-weight: 600;
    color: #ffffff;
}
.job-match {
    background: linear-gradient(90deg, #34d399, #22c55e);
    color: #062b1c;
    font-size: 0.78rem;
    font-weight: 700;
    padding: 3px 10px;
    border-radius: 999px;
    white-space: nowrap;
}
.job-company {
    color: #d8b4fe;
    font-weight: 500;
    font-size: 0.95rem;
    margin-bottom: 2px;
}
.job-location {
    color: #b8b6d6;
    font-size: 0.85rem;
}

footer { visibility: hidden; }
"""


# ----------------------------------------------------------------------
# 5. LAYOUT
# ----------------------------------------------------------------------
with gr.Blocks(theme=theme, css=custom_css, title="AI Job Finder") as demo:

    gr.HTML("<div id='app-title'>🤖 AI Job Finder</div>")
    gr.HTML(
        "<div id='app-subtitle'>Upload your resume and let AI recommend the best matching jobs for you</div>"
    )

    with gr.Row(equal_height=True):
        with gr.Column(scale=1, elem_classes="upload-card"):
            resume_input = gr.File(
                label="📄 Upload Resume (PDF or DOCX)",
                file_types=[".pdf", ".docx"],
                type="filepath",
            )
            with gr.Row():
                clear_btn = gr.Button("Clear", elem_id="clear-btn", variant="secondary")
                submit_btn = gr.Button("✨ Find My Jobs", elem_id="submit-btn", variant="primary")

        with gr.Column(scale=1, elem_classes="output-card"):
            gr.Markdown("### 🎯 Recommended Jobs")
            output_html = gr.HTML(
                "<p style='color:#9d9bc7;'>Your matched jobs will appear here after you submit your resume.</p>"
            )

    submit_btn.click(fn=find_jobs, inputs=resume_input, outputs=output_html)
    clear_btn.click(
        fn=lambda: (None, "<p style='color:#9d9bc7;'>Your matched jobs will appear here after you submit your resume.</p>"),
        inputs=None,
        outputs=[resume_input, output_html],
    )


if __name__ == "__main__":
    demo.launch()