Add app.py and requirements.txt from Colab

app.py

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+
+import torch
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+import gradio as gr
+from PIL import Image
+
+# ✅ Ganti label sesuai dataset kamu
+id2label = {
+    0: "Angry",
+    1: "Disgust",
+    2: "Fear",
+    3: "Happy",
+    4: "Sad",
+    5: "Surprise",
+    6: "Neutral"
+}
+
+# ✅ Ganti checkpoint/model sesuai modelmu
+checkpoint = "google/vit-base-patch16-224-in21k"
+processor = AutoImageProcessor.from_pretrained(checkpoint)
+model = AutoModelForImageClassification.from_pretrained(
+    checkpoint,
+    num_labels=7,
+    id2label=id2label,
+    label2id={v: k for k, v in id2label.items()}
+)
+model.eval()
+
+def predict(img: Image.Image):
+    if img.mode != "RGB":
+        img = img.convert("RGB")
+    inputs = processor(images=img, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probs = torch.nn.functional.softmax(logits, dim=1)[0]
+    return {id2label[i]: float(probs[i]) for i in range(len(probs))}
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Facial Expression Classifier",
+    description="Upload a face image and the model will predict the facial expression."
+)
+
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

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+
+gradio>=3.0
+torch
+torchvision
+transformers
+Pillow
+numpy