app.py

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).float().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()