Upload app.py

app.py

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+import spaces
+import gradio as gr
+import torch
+from transformers import CLIPModel, CLIPProcessor
+from PIL import Image
+
+# ─── Model Loading (module-level for ZeroGPU) ───────────────────────────
+MODEL_ID = "kwanY/styleid"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = CLIPModel.from_pretrained(MODEL_ID).to(device)
+processor = CLIPProcessor.from_pretrained(MODEL_ID)
+model.eval()
+
+# ─── Inference ───────────────────────────────────────────────────────────
+
+@spaces.GPU(duration=30)
+def compare_faces(img1: Image.Image, img2: Image.Image):
+    """Compare face identity between two images using StyleID."""
+    if img1 is None or img2 is None:
+        return "⚠️ Please upload both images.", ""
+
+    def get_embedding(img):
+        inputs = processor(images=img.convert("RGB"), return_tensors="pt").to(device)
+        with torch.no_grad():
+            emb = model.get_image_features(**inputs)
+            emb = emb / emb.norm(dim=-1, keepdim=True)
+        return emb
+
+    e1 = get_embedding(img1)
+    e2 = get_embedding(img2)
+    score = (e1 * e2).sum(dim=-1).item()
+
+    # Interpretation based on paper thresholds
+    if score >= 0.55:
+        verdict = "✅ Confidently Same Person"
+        color = "#22c55e"
+    elif score >= 0.45:
+        verdict = "✅ Very Likely Same Person"
+        color = "#4ade80"
+    elif score >= 0.35:
+        verdict = "🟡 Probably Same Person"
+        color = "#facc15"
+    elif score >= 0.25:
+        verdict = "🟠 Uncertain — Borderline"
+        color = "#fb923c"
+    else:
+        verdict = "❌ Likely Different People"
+        color = "#ef4444"
+
+    # Create a visual gauge
+    score_pct = max(0, min(100, int(score * 100)))
+    gauge_html = f"""
+    <div style="text-align:center; padding: 20px;">
+        <div style="font-size: 48px; font-weight: bold; color: {color};">{score:.3f}</div>
+        <div style="font-size: 20px; margin-top: 8px; color: {color}; font-weight: 600;">{verdict}</div>
+        <div style="margin-top: 16px; background: #1f2937; border-radius: 12px; height: 24px; overflow: hidden; position: relative;">
+            <div style="background: linear-gradient(90deg, #ef4444, #fb923c, #facc15, #4ade80, #22c55e); height: 100%; width: {score_pct}%; border-radius: 12px; transition: width 0.5s;"></div>
+        </div>
+        <div style="display: flex; justify-content: space-between; font-size: 11px; color: #9ca3af; margin-top: 4px;">
+            <span>Different</span>
+            <span>Uncertain</span>
+            <span>Same</span>
+        </div>
+    </div>
+    """
+
+    details = f"""### How to Read the Score
+| Range | Meaning |
+|-------|---------|
+| **> 0.55** | ✅ Confidently same person |
+| **0.45 – 0.55** | ✅ Very likely same person |
+| **0.35 – 0.45** | 🟡 Probably same person |
+| **0.25 – 0.35** | 🟠 Uncertain / borderline |
+| **< 0.25** | ❌ Likely different people |
+
+**Your Score: {score:.4f}**
+
+> **Note:** StyleID is optimized for comparing a real photo against its stylized version (anime, cartoon, painting, sketch, etc.). Works best with a single prominent face per image.
+"""
+
+    return gauge_html, details
+
+# ─── UI ──────────────────────────────────────────────────────────────────
+
+DESCRIPTION = """
+# 🎭 StyleID — Does AI Preserve Your Face?
+
+Upload a **real photo** and a **stylized portrait** (anime, cartoon, painting, sketch, etc.)  
+→ StyleID checks if the identity is preserved across art styles.
+
+**Powered by [StyleID](https://huggingface.co/kwanY/styleid)** — a perception-aware face identity model trained to match human judgments across stylizations.
+
+### 🔬 How it works
+StyleID uses a fine-tuned CLIP ViT-L/14 encoder calibrated against human psychometric experiments.
+Unlike standard face recognition (ArcFace, AdaFace), StyleID is specifically designed for **cross-style** identity matching — 
+real photo ↔ anime, cartoon, painting, caricature, 3D render, etc.
+
+### ⚡ Use Cases
+- Check if your AI-stylized portrait still looks like you
+- Evaluate face-swap / face-transfer quality
+- Compare identity preservation across different AI art tools
+- Research: benchmark stylization models for ID preservation
+"""
+
+TIPS = """
+### 💡 Tips for Best Results
+1. **One face per image** — StyleID doesn't do face detection
+2. **Face should be prominent** — at least 25% of the image
+3. **Frontal or 3/4 view works best** — extreme profiles may reduce scores
+4. **Works across styles** — anime, cartoon, oil painting, watercolor, pencil sketch, 3D render, caricature
+5. **NOT a security tool** — for research/evaluation only
+"""
+
+with gr.Blocks(
+    title="StyleID — Face Identity Across Art Styles",
+    theme=gr.themes.Soft(
+        primary_hue="violet",
+        secondary_hue="blue",
+    ),
+    css="footer { display: none !important; }"
+) as demo:
+    gr.Markdown(DESCRIPTION)
+
+    with gr.Row():
+        with gr.Column():
+            img1 = gr.Image(type="pil", label="📸 Original Photo (Real Face)", height=400)
+        with gr.Column():
+            img2 = gr.Image(type="pil", label="🎨 Stylized Portrait (Anime, Cartoon, etc.)", height=400)
+
+    btn = gr.Button("🔍 Compare Identity", variant="primary", size="lg")
+
+    with gr.Row():
+        with gr.Column():
+            gauge = gr.HTML(label="Identity Score")
+        with gr.Column():
+            details = gr.Markdown(label="Details")
+
+    btn.click(
+        fn=compare_faces,
+        inputs=[img1, img2],
+        outputs=[gauge, details],
+    )
+
+    with gr.Accordion("💡 Tips & Info", open=False):
+        gr.Markdown(TIPS)
+
+    gr.Markdown("""
+---
+**Model:** [kwanY/styleid](https://huggingface.co/kwanY/styleid) | **Paper:** [StyleID (arXiv 2604.21689)](https://arxiv.org/abs/2604.21689) | **License:** Research use only — not for biometric authentication.
+""")
+
+demo.launch()