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app.py

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+import gradio as gr
+from transformers import AutoModelForImageClassification, AutoImageProcessor
+from PIL import Image
+import torch
+
+# Load model and processor from Hugging Face
+model_name = "Muzmmillcoste/finetuned-dermnet"
+model = AutoModelForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+labels = model.config.id2label
+
+# Define prediction function
+def predict(image):
+    image = image.convert("RGB")  # Ensure correct format
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax(-1).item()
+    predicted_label = labels[str(predicted_class_idx)]
+    return f"Predicted Class: {predicted_label}"
+
+# Create Gradio Interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Hair and Skin Disease Classifier",
+    description="Upload a scalp or skin image. The model will classify the condition using a fine-tuned Vision Transformer."
+)
+
+# Launch app
+interface.launch()