Create app.py

app.py

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+import torch
+import torchvision
+from torchvision.models.detection import FasterRCNN
+from torchvision.models.detection.fasterrcnn_resnet50_fpn import FastRCNNPredictor
+from torchvision.transforms import functional as F
+from PIL import Image, ImageDraw, ImageFont
+import gradio as gr
+
+# Force CPU
+device = torch.device('cpu')
+
+# COCO-style class map
+COCO_CLASSES = {
+    0: "Background", 
+    1: "Stand", 
+    2: "Sit", 
+    3: "Ruku",
+    4: "Sijdah"
+}
+
+# Load model
+def get_model(num_classes):
+    model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=False)
+    in_features = model.roi_heads.box_predictor.cls_score.in_features
+    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
+    return model
+
+model = get_model(num_classes=5)
+model.load_state_dict(torch.load("Salatfasterrcnn_resnet50_epoch_3.pth", map_location=device))
+model.to(device)
+model.eval()
+
+# Prediction function
+def predict(image):
+    image = image.convert("RGB")
+    image_tensor = F.to_tensor(image).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        prediction = model(image_tensor)
+
+    draw = ImageDraw.Draw(image)
+    boxes = prediction[0]["boxes"].cpu().numpy()
+    labels = prediction[0]["labels"].cpu().numpy()
+    scores = prediction[0]["scores"].cpu().numpy()
+
+    for box, label, score in zip(boxes, labels, scores):
+        if score > 0.5:
+            x_min, y_min, x_max, y_max = box
+            class_name = COCO_CLASSES.get(label, "Unknown")
+            draw.rectangle([x_min, y_min, x_max, y_max], outline="red", width=3)
+            draw.text((x_min, y_min), f"{class_name} ({score:.2f})", fill="red")
+
+    return image
+
+# Gradio interface
+gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil"),
+    title="Salat Posture Detection",
+    description="Upload an image to detect salat postures (stand, sit, ruku, sijdah)."
+).launch()