updated python deps

reticulated-image-classifier/app.R

@@ -86,7 +86,7 @@ server <- function(input, output, session) {
         } else {
             return(list(src = new_path, style = htmltools::css(width = "100%")))
         }
-    })
+    }, deleteFile = FALSE)
 
     # default images
     observeEvent(input$oil_platform, image_path("./img/oil_platform.jpg"))

reticulated-image-classifier/image-classifier.py

@@ -6,27 +6,27 @@
 from torch.autograd import Variable
 
 # Set PyTorch model directory
-os.environ["TORCH_MODEL_ZOO"] = "./model"
+os.environ["TORCH_HOME"] = "./model"
 
 squeeze = models.squeezenet1_1(pretrained=True)
 squeeze.eval()
 
-normalize = transforms.Normalize(
-   mean=[0.485, 0.456, 0.406],
-   std=[0.229, 0.224, 0.225]
-)
+normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
 
-preprocess = transforms.Compose([
-   transforms.Resize(256),
-   transforms.CenterCrop(224),
-   transforms.ToTensor(),
-   normalize
-])
+preprocess = transforms.Compose(
+    [
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        normalize,
+    ]
+)
 
-with open('labels.json') as f:
+with open("labels.json") as f:
     labels_data = json.load(f)
 
-labels = {int(key):value for (key, value) in labels_data.items()}
+labels = {int(key): value for (key, value) in labels_data.items()}
+
 
 def classify_image_pytorch(image_path):
 
@@ -41,7 +41,6 @@ def classify_image_pytorch(image_path):
     for prediction in top_k:
         description = labels[prediction]
         score = fc_out.data.numpy()[0][prediction]
-        results.append(('%s (score = %.5f)' % (description, score)))
-
-    return(results)
+        results.append(("%s (score = %.5f)" % (description, score)))
 
+    return results