update app.py

README.md

@@ -12,4 +12,6 @@ license: afl-3.0
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
 
-Demo app updated.
+### References
+* https://huggingface.co/docs/hub/spaces#manage-app-with-github-actions
+* https://www.gradio.app/image_classification_in_pytorch/

app.py

@@ -1,30 +1,101 @@
 import torch
-import requests
+from PIL import Image
+from torchvision import transforms
 import gradio as gr
+import os
 
-from torchvision import transforms
 
 """
-Built following https://www.gradio.app/image_classification_in_pytorch/.
+Built following:
+https://huggingface.co/spaces/pytorch/ResNet/tree/main
+https://www.gradio.app/image_classification_in_pytorch/
 """
 
-# Load model
-model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
-
-# Download human-readable labels for ImageNet.
-response = requests.get("https://git.io/JJkYN")
-labels = response.text.split("\n")
-
-def predict(inp):
-  inp = transforms.ToTensor()(inp).unsqueeze(0)
-  with torch.no_grad():
-    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
-    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}    
-  return confidences
-
-gr.Interface(fn=predict, 
-             inputs=gr.inputs.Image(type="pil"),
-             outputs=gr.outputs.Label(num_top_classes=3),
-             examples=["example1.jpg", "example2.jpg"],
-             theme="default",
-             css=".footer{display:none !important}").launch()
+os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt")
+
+model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True)
+model.eval()
+
+# Download an example image from the pytorch website
+torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
+
+def inference(input_image):
+    preprocess = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    input_tensor = preprocess(input_image)
+    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
+
+    # move the input and model to GPU for speed if available
+    if torch.cuda.is_available():
+        input_batch = input_batch.to('cuda')
+        model.to('cuda')
+
+    with torch.no_grad():
+        output = model(input_batch)
+    # The output has unnormalized scores. To get probabilities, you can run a softmax on it.
+    probabilities = torch.nn.functional.softmax(output[0], dim=0)
+
+    # Read the categories
+    with open("imagenet_classes.txt", "r") as f:
+        categories = [s.strip() for s in f.readlines()]
+    # Show top categories per image
+    top5_prob, top5_catid = torch.topk(probabilities, 5)
+    result = {}
+    for i in range(top5_prob.size(0)):
+        result[categories[top5_catid[i]]] = top5_prob[i].item()
+    return result
+
+inputs = gr.inputs.Image(type='pil')
+outputs = gr.outputs.Label(type="confidences",num_top_classes=5)
+
+title = "An Image Classification Demo with ResNet"
+description = "Demo of a ResNet image classifier trained on the ImageNet dataset. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1512.03385' target='_blank'>Deep Residual Learning for Image Recognition</a> | <a href='https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py' target='_blank'>Github Repo</a></p>"
+
+gr.Interface(inference, 
+            inputs, 
+            outputs, 
+            examples=["example1.jpg", "example2.jpg"], 
+            title=title, 
+            description=description, 
+            article=article,
+            analytics_enabled=False).launch()
+
+# import torch
+# import requests
+# import gradio as gr
+
+# from torchvision import transforms
+
+# """
+# Built following https://www.gradio.app/image_classification_in_pytorch/.
+# """
+
+# # Load model
+# model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
+
+# # Download human-readable labels for ImageNet.
+# response = requests.get("https://git.io/JJkYN")
+# labels = response.text.split("\n")
+
+# def predict(inp):
+#   inp = transforms.ToTensor()(inp).unsqueeze(0)
+#   with torch.no_grad():
+#     prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
+#     confidences = {labels[i]: float(prediction[i]) for i in range(1000)}    
+#   return confidences
+
+# title = "Image Classifier"
+
+# article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1512.03385' target='_blank'>Deep Residual Learning for Image Recognition</a> | <a href='https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py' target='_blank'>Github Repo</a></p>"
+
+# gr.Interface(fn=predict, 
+#              inputs=gr.inputs.Image(type="pil"),
+#              outputs=gr.outputs.Label(num_top_classes=3),
+#              examples=["example1.jpg", "example2.jpg"],
+#              theme="default",
+#              css=".footer{display:none !important}").launch()

requirements.txt

@@ -1,2 +1,3 @@
 torch
-torchvision
+torchvision
+Pillow