Create app.py

app.py

@@ -0,0 +1,42 @@
+import gradio as gr
+import torch 
+from torchvision import models,transforms
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+backbone=models.densenet121(pretrained=True)
+for param in backbone.parameters():
+    #print(param)
+    param.requires_grad=False
+for param in backbone.features.denseblock4.parameters():
+    param.requires_grad=True
+backbone.classifier=torch.nn.Sequential(
+torch.nn.Dropout(0.2,inplace=True),
+torch.nn.Linear(1024,4),
+   
+)
+model=backbone.to(device)
+model.load_state_dict(torch.load('Alzahimer_weights.pt',map_location=torch.device('cpu')))
+label_2id={'MildDemented':0,'ModerateDemented':1,'NonDemented':2,'VeryMildDemented':3}
+id2_label={i:v for v,i in label_2id.items()}
+preprocess=transforms.Compose([ transforms.Resize((224,224)),
+                                transforms.ToTensor(),
+                                      transforms.Normalize([0.485, 0.456, 0.406],
+                                                           [0.229, 0.224, 0.225])])
+def detect_symptom(image):
+  img=preprocess(image)
+  with torch.no_grad():
+    model.eval()
+    pred=model(img.unsqueeze(0))
+    pred_max=torch.argmax(pred,dim=1)
+    label=id2_label[pred_max.detach().cpu().numpy()[0]]
+    confidence=torch.nn.functional.softmax(pred.float(),dim=-1).detach().cpu().topk(3)
+  #print(confidence.indices[0][0].item())
+    predictions={id2_label[confidence.indices[0][i].item()]:confidence.values[0][i].item() for i in range(3)}
+    return predictions
+
+  
+demo=gr.Interface(fn=detect_symptom,inputs=gr.Image(type='pil'),
+                  outputs=gr.Label(num_top_classes=3),
+                  examples=['0020ed3a-2b5f-4e46-9b96-97484c10a88c.jpg','003304b2-5091-4342-93d8-1720afca671e.jpg',
+                           'mild_dimer.jpg'],
+                  themes=gr.themes.Soft(primary_hue=gr.themes.colors.amber,secondary_hue=gr.themes.colors.blue))
+demo.launch(debug=False)