Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+from transformers import AutoProcessor, AutoModelForImageClassification
+import torch
+from PIL import Image
+
+model_name = 'e1010101/vit-384-tongue-image'
+processor = AutoProcessor.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+
+def classify_image(image):
+ inputs = processor(images=image, return_tensors="pt")
+ with torch.no_grad():
+ outputs = model(**inputs)
+ logits = outputs.logits
+ # Apply sigmoid for multi-label classification
+ probs = torch.sigmoid(logits)[0].numpy()
+ # Get label names
+ labels = model.config.id2label.values()
+ # Create a dictionary of labels and probabilities
+ result = {label: float(prob) for label, prob in zip(labels, probs)}
+ # Sort results by probability
+ result = dict(sorted(result.items(), key=lambda item: item[1], reverse=True))
+ return result
+
+interface = gr.Interface(
+ fn=classify_image,
+ inputs=gr.inputs.Image(type="pil"),
+ outputs=gr.outputs.Label(num_top_classes=None),
+ title="Multi-Label Image Classification",
+ description="Upload an image to get classification results."
+)
+
+
+if __name__ == "__main__":
+ interface.launch()