Create app.py

app.py

@@ -0,0 +1,29 @@
+import gradio as gr
+from transformers import pipeline
+import torch
+from torchvision import transforms as T
+
+def calc_result_confidence (model_output):
+    probs = torch.nn.functional.softmax(model_output, dim=1)
+    conf, classes = torch.max(probs, 1)
+
+    return conf.item(), classes.item()
+
+def downsyndrome_gradio_inference(img_file):
+    classes = ['Down Syndrome', 'Healty']
+    infer_transform = T.Compose([
+        T.Resize((255, 255)),
+        T.ToTensor(),
+    ])
+    transform_image = infer_transform(img_file.convert('RGB')).float().unsqueeze(0)
+    model = pipeline(task='image-classification', model='gitfreder/down-syndrome-detection')
+    conf, cls = calc_result_confidence(model(transform_image))
+    
+    return {
+        'Predicted': classes[cls], 
+        'Confidence Score': conf
+        }
+
+
+iface = gr.Interface(fn=downsyndrome_gradio_inference, inputs=gr.Image(type='pil'), outputs=gr.JSON(), title="Down Syndrome Detection", description="A model interfaces that detect downsyndrom children from the photo")
+iface.launch()