debugged file

app.py

@@ -1,49 +1,52 @@
 import gradio as gr
+from huggingface_hub import hf_hub_url
+from PIL import Image
+import requests
 import torch
+from torchvision.io import read_image
 from torchvision import transforms as T
 import torchvision.models as models
-import requests
-from huggingface_hub import hf_hub_url
+
+
+# we import the model
+resnet34 = models.resnet34(pretrained=True)
+# evaluation mode
+resnet34.eval()
+
+## labeling
+# Load the file containing the 1,000 labels for the ImageNet dataset classes
+url = hf_hub_url(repo_id="Selma/pytorch-resnet34", filename="imagenet_classes.txt")
+response = requests.get(url)
+# write to a label file
+open("labels.txt", "wb").write(response.content)
+# extract the labels from the file
+with open('labels.txt', "r") as f:
+    labels = [line.strip() for line in f.readlines()]
 
 
 def classify(image):
-  
-  ## preprocessing
-  # we need a transform step to normalise the pictures
-  transform = T.Compose([T.Resize(256), T.CenterCrop(224), T.ToTensor(), T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
-  
-  # we import the model
-  resnet34 = models.resnet34(pretrained=True)
-  
-  # normalise the image
-  image_transformed = transform(image)
-  # reshape
-  batch_image_transformed = torch.unsqueeze(image_transformed, 0)
-  # evaluation mode
-  resnet34.eval()
-  # get the predictions
-  output = resnet34(batch_image_transformed)
-  
-  ## labeling
-  # Load the file containing the 1,000 labels for the ImageNet dataset classes
-  url = hf_hub_url(repo_id="Selma/pytorch-resnet34", filename="imagenet_classes.txt")
-  response = requests.get(url)
-  # write to a label file
-  open("labels.txt", "wb").write(response.content)
-  #extract the labels from the file
-  with open('labels.txt', "r") as f:
-      labels = [line.strip() for line in f.readlines()]
-  
-  ## predict the class
-  # Find the index (tensor) corresponding to the maximum score in the out tensor.
-  # Torch.max function can be used to find the information
-  _, index = torch.max(output, 1)
-  
-  # Find the score in terms of percentage by using torch.nn.functional.softmax function
-  # which normalizes the output to range [0,1] and multiplying by 100
-  percentage = torch.nn.functional.softmax(output, dim=1)[0] * 100
-  
-  return "The image depicts: " + labels[index[0]] + "with a score of " + percentage[index[0]].item() + "%"
+    ## preprocessing
+    # we need a transform step to normalise the pictures
+    transform = T.Compose([T.Resize(256), T.CenterCrop(224), T.ToTensor(),
+                           T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
+    # normalise the image
+    image_transformed = transform(image)
+    # reshape
+    batch_image_transformed = torch.unsqueeze(image_transformed, 0)
+
+    # get the predictions
+    output = resnet34(batch_image_transformed)
+
+    ## predict the class
+    # Find the index (tensor) corresponding to the maximum score in the out tensor.
+    # Torch.max function can be used to find the information
+    _, index = torch.max(output, 1)
+
+    # Find the score in terms of percentage by using torch.nn.functional.softmax function
+    # which normalizes the output to range [0,1] and multiplying by 100
+    percentage = torch.nn.functional.softmax(output, dim=1)[0] * 100
+
+    return "The image depicts: " + labels[index[0]] + " with a score of " + str(round(percentage[index[0]].item())) + "%"
 
 iface = gr.Interface(fn=classify, inputs="image", outputs="text")
 iface.launch()