app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from torchvision.transforms import Compose, Resize, ToTensor, Normalize
+from PIL import Image
+
+# Load model and processor
+model_name = "riyadifirman/klasifikasiburung"
+processor = AutoImageProcessor.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+
+# Define image transformations
+normalize = Normalize(mean=processor.image_mean, std=processor.image_std)
+transform = Compose([
+    Resize((224, 224)),
+    ToTensor(),
+    normalize,
+])
+
+def predict(image):
+    image = Image.fromarray(image)
+    inputs = transform(image).unsqueeze(0)
+    outputs = model(inputs)
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax(-1).item()
+    return processor.decode(predicted_class_idx)
+
+# Create Gradio interface
+# In newer versions of Gradio, 'inputs' and 'outputs' are directly
+# specified within the gr.Interface constructor.
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy"), # Changed from gr.inputs.Image to gr.Image
+    outputs="text",
+    title="Bird Classification",
+    description="Upload an image of a bird to classify it."
+)
+
+if __name__ == "__main__":
+    interface.launch()