Update app.py

app.py

@@ -1,73 +1,14 @@
-import os
-import torch
 import gradio as gr
+from transformers import pipeline
 
-from PIL import Image
-from torchvision import transforms
+def predict(image):
+    model_id = "google/vit-base-patch16-224"
+    classifier = pipeline("image-classification", model=model_id)
+    return classifier(image)
 
+title = "Image Classifier"
+description = "A demo that recognizes and classifies images using the model from Hugging Face's 'google/vit-base-patch16-224'."
+input_component = gr.inputs.Image(type="pil", label="Upload an image here")
+output_component = gr.outputs.Label(num_top_classes=3)
 
-"""
-Built following:
-https://huggingface.co/spaces/pytorch/ResNet/tree/main
-https://www.gradio.app/image_classification_in_pytorch/
-"""
-
-# Get classes list
-os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt")
-
-# Load PyTorch model
-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")
-
-# Inference!
-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
-
-# Define ins outs placeholders
-inputs = gr.inputs.Image(type='pil')
-outputs = gr.outputs.Label(type="confidences",num_top_classes=5)
-
-# Define style
-title = "Image Recognition Demo"
-description = "This is a prototype application which demonstrates how artifical intelligence based systems can recognize what object(s) is present in an image. This fundamental task in computer vision known as `Image Classification` has applications stretching from autonomous vehicles to medical imaging. To use it, simply upload your image, or click one of the examples images to load them, which I took at <a href='https://espacepourlavie.ca/en/biodome' target='_blank'>Montréal Biodôme</a>! 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>"
-
-# Run inference
-gr.Interface(inference, 
-            inputs, 
-            outputs, 
-            examples=["example1.jpg", "example2.jpg"], 
-            title=title, 
-            description=description, 
-            article=article,
-            analytics_enabled=False).launch()
+gr.Interface(fn=predict, inputs=input_component, outputs=output_component, title=title, description=description).launch()