Upload 3 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model_stunting.safetensor filter=lfs diff=lfs merge=lfs -text

app.py

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+import gradio as gr
+import torch
+from torchvision import transforms
+from safetensors.torch import load_file
+from PIL import Image
+import os
+
+# -------------------------------
+# 1. Load model PyTorch dari .safetensors
+# -------------------------------
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load state dict dari safetensors
+state_dict = load_file("model_stunting.safetensors")
+
+# Misalkan model kamu adalah CNN custom
+class Dense121(nn.Module):
+    def __init__(self, num_classes, pretrained=True):
+        super(Dense121, self).__init__()
+        if pretrained:
+            try:
+                weights = models.DenseNet121_Weights.IMAGENET1K_V1
+                self.dense121 = models.densenet121(weights=weights)
+            except:
+                self.dense121 = models.densenet121(pretrained=True)
+        else:
+            self.dense121 = models.densenet121(pretrained=False)
+
+        # classifier DenseNet bukan list, jadi langsung akses in_features
+        in_features = self.dense121.classifier.in_features
+        self.dense121.classifier = nn.Linear(in_features, num_classes)
+
+    def forward(self, x):
+        return self.dense121(x)
+
+
+model = Dense121()
+model.load_state_dict(state_dict)
+model.to(device)
+model.eval()
+
+# -------------------------------
+# 2. Transformasi gambar input
+# -------------------------------
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),     # Sesuaikan ukuran input model
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),  # ImageNet norm
+])
+
+# -------------------------------
+# 3. Fungsi prediksi
+# -------------------------------
+def predict(image: Image.Image):
+    img_tensor = transform(image).unsqueeze(0).to(device)  # (1, 3, 224, 224)
+
+    with torch.no_grad():
+        logits = model(img_tensor)
+        probs = torch.softmax(logits, dim=1)
+        pred_class = torch.argmax(probs, dim=1).item()
+        confidence = probs[0][pred_class].item()
+
+    label_map = {0: "Tidak Stunting", 1: "Stunting"}
+    result_label = label_map[pred_class]
+
+    return {
+        "prediction": result_label,
+        "confidence": round(confidence, 4)
+    }
+
+# -------------------------------
+# 4. Interface Gradio
+# -------------------------------
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload Gambar Anak"),
+    outputs=gr.JSON(label="Hasil Prediksi"),
+    title="Prediksi Stunting dari Foto",
+    description="Upload foto anak untuk mendeteksi risiko stunting menggunakan model CNN.",
+    examples=[["example.jpg"]]  # opsional
+)
+
+# -------------------------------
+# 5. Launch
+# -------------------------------
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7860, share=True)

model_stunting.safetensor

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9d67c85264cfcfe3049b7e4559cf7dae718ff13fa39238331c856a90b7d3273
+size 28248920

requirements.txt

@@ -0,0 +1,5 @@
+gradio
+torch
+torchvision
+safetensors
+Pillow