app.py

import torch
from torchvision.models.detection import fasterrcnn_resnet50_fpn
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.transforms import functional as F
from PIL import Image, ImageDraw
import gradio as gr

# Label names
COCO_CLASSES = {
    0: "Background",
    1: "Without Mask",
    2: "With Mask",
    3: "Incorrect Mask"
}

# Load model
def get_model(num_classes=4):
    model = fasterrcnn_resnet50_fpn(weights=None)
    in_features = model.roi_heads.box_predictor.cls_score.in_features
    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
    return model

# Setup
device = torch.device("cpu")
model = get_model()
model.load_state_dict(torch.load("fasterrcnn_resnet50_epoch_4.pth", map_location=device))
model.to(device)
model.eval()

# Inference function
def predict(image):
    image_tensor = F.to_tensor(image).unsqueeze(0).to(device)

    with torch.no_grad():
        prediction = model(image_tensor)

    boxes = prediction[0]["boxes"]
    labels = prediction[0]["labels"]
    scores = prediction[0]["scores"]

    draw = ImageDraw.Draw(image)
    threshold = 0.5

    for box, label, score in zip(boxes, labels, scores):
        if score > threshold:
            x1, y1, x2, y2 = box
            class_name = COCO_CLASSES.get(label.item(), "Unknown")
            draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
            draw.text((x1, y1), f"{class_name} ({score:.2f})", fill="red")

    return image

# Gradio Interface
gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", label="Upload a Face Image"),
    outputs=gr.Image(type="pil", label="Detection Result"),
    title="Face Mask Detection - Faster R-CNN",
    description="Detects faces with mask, without mask, or incorrectly worn mask."
).launch()