Update app.py

app.py

@@ -3,13 +3,12 @@ from transformers import AutoModelForImageClassification, AutoImageProcessor
 from PIL import Image
 import torch
 
-# Load the pretrained model and processor from Hugging Face
-model_name = "HuggingFaceM4/swin-tiny-patch4-window7-224-finetuned-isic"
+# Load public model
+model_name = "nateraw/vit-base-patch16-224-in21k-finetuned-dermnet"
 model = AutoModelForImageClassification.from_pretrained(model_name)
 processor = AutoImageProcessor.from_pretrained(model_name)
 labels = model.config.id2label
 
-# Prediction function
 def predict(image):
     image = image.convert("RGB")
     inputs = processor(images=image, return_tensors="pt")
@@ -20,14 +19,11 @@ def predict(image):
     label = labels[str(predicted_idx)]
     return f"Prediction: {label}"
 
-# Gradio Interface
-interface = gr.Interface(
+gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil"),
     outputs="text",
-    title="Skin Lesion Classifier (ISIC Pretrained)",
-    description="Upload a skin or scalp image. The model predicts the skin condition using a fine-tuned Swin Transformer."
-)
-
-interface.launch()
+    title="Skin Condition Classifier (DermNet)",
+    description="Upload a skin or scalp image. This model classifies dermatological conditions using a ViT model trained on DermNet."
+).launch()