Classification v1.

app.py

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+import gradio as gr
+import torch
+from torchvision import transforms
+from torchvision import models
+from PIL import Image
+import requests
+
+# Load a pre-trained model
+model = models.resnet50(pretrained=True)
+model.eval()
+
+# Preprocess the input image
+def preprocess(image):
+    transform = 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])
+    ])
+    return transform(image).unsqueeze(0)
+
+
+def predict(image):
+    image = Image.fromarray(image.astype('uint8'), 'RGB')
+    preprocessed_img = preprocess(image)
+    outputs = model(preprocessed_img)
+    _, predicted = torch.max(outputs, 1)
+    return predicted.item()
+
+# Create a Gradio interface
+image = gr.inputs.Image()
+label = gr.outputs.Label(num_top_classes=1)
+
+gr.Interface(predict, image, label, capture_session=True).launch()

models/mobilenetv3_large_100_224.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8b8037bebe37c941a2cc49bb9590a2a5b1cb66a2650752db66c0d430dd3ef8fd
+size 17822949