Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+
+MODEL_ID = "YOUR_USERNAME/brain-tumor-resnet18"
+
+processor = AutoImageProcessor.from_pretrained(MODEL_ID)
+model = AutoModelForImageClassification.from_pretrained(MODEL_ID)
+model.eval()
+
+def classify_mri(image):
+    inputs = processor(images=image, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probabilities = torch.softmax(outputs.logits, dim=-1)[0]
+
+    return {
+        model.config.id2label[i]: float(probabilities[i])
+        for i in range(len(probabilities))
+    }
+
+demo = gr.Interface(
+    fn=classify_mri,
+    inputs=gr.Image(type="pil", label="Upload brain MRI"),
+    outputs=gr.Label(num_top_classes=4),
+    title="Brain MRI Tumour Classification",
+    description="Educational prototype only. Not for medical diagnosis."
+)
+
+demo.launch()