import time
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

MODEL_ID = "abnetsisaynew/joblink-match-scorer"

# ── Model loading ─────────────────────────────────────────────────────────────
_startup_time = time.time()
print(f"⏳ Loading model: {MODEL_ID} ...")
_tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
_model     = AutoModelForSequenceClassification.from_pretrained(
    MODEL_ID,
    num_labels=1,
    problem_type="regression",
    ignore_mismatched_sizes=True,
)
_model.eval().float()
_load_time = time.time() - _startup_time
print(f"✅ Model loaded in {_load_time:.1f}s!")

# ── Prediction function ───────────────────────────────────────────────────────
def compute_score(text: str):
    if not text or not text.strip():
        return {"score": 0.0}
    text = text.strip()[:4096]
    tokens = _tokenizer(
        text, truncation=True, padding="max_length",
        max_length=512, return_tensors="pt"
    )
    with torch.no_grad():
        score = _model(**tokens).logits.squeeze().item()
    return {"score": round(float(max(0.0, min(1.0, score))), 4)}

custom_css = '''
.gradio-container {
    font-family: 'Inter', sans-serif !important;
}
.header-text {
    text-align: center;
    color: var(--color-accent) !important;
    font-weight: 800;
    margin-bottom: 0.5rem;
    font-size: 2.8rem !important;
    letter-spacing: -0.025em;
}
.sub-text {
    text-align: center;
    color: var(--body-text-color-subdued) !important;
    margin-bottom: 2rem;
    font-size: 1.2rem !important;
}
.score-box {
    border-radius: 12px;
    padding: 1.5rem;
    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
}
'''

with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo", neutral_hue="slate"), css=custom_css, title="JobLink Matcher") as demo:
    gr.Markdown("<h1 class='header-text'>🔗 JobLink AI Match Scorer</h1>")
    gr.Markdown("<p class='sub-text'>State-of-the-art semantic matching powered by fine-tuned DeBERTa-v3.</p>")
    
    with gr.Row():
        with gr.Column(scale=2):
            gr.Markdown("### 📝 Job Description & Candidate CV")
            input_text = gr.Textbox(
                show_label=False,
                placeholder="JOB: Senior Software Engineer... [SEP] CANDIDATE: BSc Computer Science...",
                lines=12,
                container=False
            )
            submit_btn = gr.Button("🚀 Compute Match Score", variant="primary", size="lg")
            
        with gr.Column(scale=1):
            gr.Markdown("### 🎯 Match Score Result")
            with gr.Group(elem_classes="score-box"):
                output_json = gr.JSON(label="Result JSON")
            
            gr.Markdown(
                """
                <br>
                
                ### 📊 Score Guide
                - 🟢 **≥ 0.85** : Excellent Match
                - 🟡 **0.65 - 0.84** : Good Match
                - 🟠 **0.40 - 0.64** : Moderate Match
                - 🔴 **< 0.40** : Poor Match
                """
            )
            
    # api_name="predict" creates the /call/predict endpoints natively!
    submit_btn.click(fn=compute_score, inputs=input_text, outputs=output_json, api_name="predict")
    
    gr.Markdown("---")
    gr.Markdown("### 🧪 Quick Tests")
    gr.Examples(
        examples=[
            # 1. Perfect Match
            ["JOB: Full Stack Developer. Experience: 4+ years. Required Skills: React, Node.js, MongoDB, TypeScript. [SEP] CANDIDATE: Full Stack Engineer. Experience: 5 years. Skills: React, Node.js, MongoDB, TypeScript, AWS."],
            
            # 2. Good Match (Transferable Skills)
            ["JOB: Machine Learning Engineer. Required Skills: Python, PyTorch, SQL, Data Modeling. [SEP] CANDIDATE: Data Scientist. Experience: 3 years. Skills: Python, TensorFlow, SQL, Pandas."],
            
            # 3. Junior applying for Senior (Experience Gap)
            ["JOB: Senior DevOps Engineer. Experience: 7+ years. Required Skills: Kubernetes, Terraform, AWS, CI/CD. [SEP] CANDIDATE: Junior Cloud Developer. Experience: 1 year. Skills: AWS, Docker, Git."],
            
            # 4. Partial Skill Match (Skills Gap)
            ["JOB: UI/UX Designer. Required Skills: Figma, Adobe XD, Prototyping, User Research. [SEP] CANDIDATE: Graphic Designer. Skills: Adobe Illustrator, Photoshop, Branding."],
            
            # 5. Missing Core Requirement
            ["JOB: Bilingual Customer Support (Spanish/English). Required Skills: Fluent Spanish, CRM, Communication. [SEP] CANDIDATE: Customer Service Rep. Skills: English, Zendesk, Communication."],
            
            # 6. Completely Unrelated Match (Hard Knockout)
            ["JOB: Heart Surgeon. Field: Health Sciences. Experience: 10+ years. Required Skills: Surgery, Diagnostics. [SEP] CANDIDATE: Truck Driver. Field: Logistics. Experience: 10 years. Skills: Driving, Navigation."]
        ],
        inputs=input_text
    )

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