Create app.py

app.py

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+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+import cv2
+import numpy as np
+import gradio as gr
+
+# -------------------------
+# Model definition
+# -------------------------
+def get_model():
+    model = models.vgg16(pretrained=True)
+    for param in model.parameters():
+        param.requires_grad = False
+    model.avgpool = nn.Sequential(
+        nn.Conv2d(512,512,3),
+        nn.MaxPool2d(2),
+        nn.Flatten()
+    )
+    model.classifier = nn.Sequential(
+        nn.Linear(2048,512),
+        nn.ReLU(),
+        nn.Dropout(0.5),
+        nn.Linear(512,136),  # 68 x,y pairs
+        nn.Sigmoid()
+    )
+    return model
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load model
+model = get_model().to(device)
+model.load_state_dict(torch.load("facial_keypoints.pth", map_location=device))
+model.eval()
+
+# -------------------------
+# Image preprocessing
+# -------------------------
+transform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Resize((224,224)),
+    transforms.Normalize(mean=[0.485,0.456,0.406],
+                         std=[0.229,0.224,0.225])
+])
+
+def denormalize_keypoints(pred, img_h=224, img_w=224):
+    pred = pred.detach().cpu().numpy()
+    x = pred[:,:68] * img_w
+    y = pred[:,68:] * img_h
+    return np.stack([x,y], axis=2)
+
+# -------------------------
+# Inference function for Gradio
+# -------------------------
+def predict_keypoints(image):
+    # Convert PIL → CV2 → tensor
+    img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) / 255.0
+    img_resized = cv2.resize(img, (224,224))
+    input_tensor = transform(img_resized).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        pred = model(input_tensor)
+        kps = denormalize_keypoints(pred)[0]  # first batch only
+    
+    # Draw keypoints on image
+    vis_img = cv2.cvtColor((img_resized*255).astype(np.uint8), cv2.COLOR_BGR2RGB)
+    for (x,y) in kps:
+        cv2.circle(vis_img, (int(x), int(y)), 2, (255,0,0), -1)
+    
+    return vis_img
+
+# -------------------------
+# Gradio Interface
+# -------------------------
+demo = gr.Interface(
+    fn=predict_keypoints,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="numpy"),
+    title="Facial Keypoints Detection",
+    description="Upload a face image and the model will predict 68 facial keypoints."
+)
+
+if __name__ == "__main__":
+    demo.launch()