initial commit

app.py

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+import gradio as gr
+from PIL import Image
+from transformers import ViTImageProcessor, ViTForImageClassification
+
+
+
+processor = ViTImageProcessor.from_pretrained('Rageshhf/fine-tuned-model')
+
+id2label = {0: 'Mild_Demented', 1: 'Moderate_Demented', 2: 'Non_Demented', 3: 'Very_Mild_Demented'}
+label2id = {'Mild_Demented': 0, 'Moderate_Demented': 1, 'Non_Demented': 2, 'Very_Mild_Demented': 3}
+
+
+model = ViTForImageClassification.from_pretrained(
+    'Rageshhf/fine-tuned-model',
+    num_labels=4,
+    id2label=id2label,
+    label2id=label2id,
+    ignore_mismatched_sizes=True)
+
+title = "Medi- classifier"
+description = """Trained to classify disease based on image data."""
+
+
+
+def predict(image):
+
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+    # model predicts one of the 1000 ImageNet classes
+    predicted_class_idx = logits.argmax(-1).item()
+    return(model.config.id2label[predicted_class_idx])
+
+demo = gr.Interface(fn=predict, inputs="image", outputs="text", title=title,
+    description=description,).launch()
+
+# demo.launch(debug=True)