Add app.py

app.py

@@ -0,0 +1,135 @@
+import gradio as gr
+import torch
+import torch.nn as nn
+from torchvision import transforms
+from transformers import DeiTModel
+from PIL import Image
+import os
+
+# -----------------------------------------------------------------------------
+# Model Definition (re-defined to be self-contained)
+# -----------------------------------------------------------------------------
+class FingerprintLivenessModel(nn.Module):
+    """
+    Simplified model for fingerprint liveness detection.
+    DeiT-Tiny backbone (192-dim) -> Fingerprint Expert (192->512) -> Classifier (512->2)
+    """
+    def __init__(self):
+        super(FingerprintLivenessModel, self).__init__()
+        # Load pre-trained DeiT-Tiny model
+        self.base_model = DeiTModel.from_pretrained(
+            "facebook/deit-tiny-distilled-patch16-224"
+        )
+        # Fingerprint-specific expert layer
+        self.fingerprint_expert = nn.Linear(192, 512)
+        # Final classifier (2 classes: spoof=0, live=1)
+        self.classifier = nn.Linear(512, 2)
+    
+    def forward(self, pixel_values):
+        outputs = self.base_model(pixel_values)
+        cls_embeddings = outputs.last_hidden_state[:, 0, :]
+        expert_features = self.fingerprint_expert(cls_embeddings)
+        logits = self.classifier(expert_features)
+        return logits
+
+# -----------------------------------------------------------------------------
+# Global Variables & Initialization
+# -----------------------------------------------------------------------------
+DEVICE = "cpu"  # Force CPU for Hugging Face Spaces free tier stability
+MODEL_PATH = "model.pth"
+
+def load_model():
+    if not os.path.exists(MODEL_PATH):
+        raise FileNotFoundError(f"Model weights not found at: {MODEL_PATH}")
+    
+    print(f"Loading model from {MODEL_PATH}...")
+    model = FingerprintLivenessModel()
+    
+    # Load weights
+    checkpoint = torch.load(MODEL_PATH, map_location=DEVICE)
+    model_state_dict = model.state_dict()
+    
+    # Filter and load weights
+    filtered_checkpoint = {}
+    for key in checkpoint.keys():
+        if key.startswith('base_model') or key.startswith('fingerprint_expert') or key.startswith('classifier'):
+            if key in model_state_dict:
+                filtered_checkpoint[key] = checkpoint[key]
+    
+    model.load_state_dict(filtered_checkpoint, strict=False)
+    model.to(DEVICE)
+    model.eval()
+    return model
+
+# Initialize model once
+try:
+    model = load_model()
+    print("Model loaded successfully!")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    model = None
+
+# -----------------------------------------------------------------------------
+# Preprocessing
+# -----------------------------------------------------------------------------
+def preprocess_image(image):
+    normalize = transforms.Normalize(
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]
+    )
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+        normalize
+    ])
+    return transform(image).unsqueeze(0)
+
+# -----------------------------------------------------------------------------
+# Prediction Function
+# -----------------------------------------------------------------------------
+def predict(image):
+    if model is None:
+        return "Model not loaded"
+    
+    if image is None:
+        return "Please upload an image."
+        
+    try:
+        # Preprocess
+        input_tensor = preprocess_image(image).to(DEVICE)
+        
+        # Inference
+        with torch.no_grad():
+            logits = model(input_tensor)
+            probs = torch.softmax(logits, dim=1)
+            
+            spoof_prob = probs[0, 0].item()
+            live_prob = probs[0, 1].item()
+        
+        return {
+            "Live": live_prob,
+            "Spoof": spoof_prob
+        }
+    except Exception as e:
+        return f"Error during prediction: {str(e)}"
+
+# -----------------------------------------------------------------------------
+# Gradio Interface
+# -----------------------------------------------------------------------------
+title = "Fingerprint Liveness Detection"
+description = """
+Upload a fingerprint image to check if it's **Live** or **Spoof**. 
+This model uses a DeiT-Tiny transformer backbone with a fingerprint-specific expert layer.
+"""
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Fingerprint Image"),
+    outputs=gr.Label(num_top_classes=2, label="Prediction"),
+    title=title,
+    description=description,
+    examples=[]
+)
+
+if __name__ == "__main__":
+    iface.launch()