init imagesense app

app.py

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+import requests
+import gradio as gr
+import torch
+
+from timm import create_model
+from timm.data import resolve_data_config
+from timm.data.transforms_factory import create_transform
+
+
+IMAGENET_1K_URL = 'https://storage.googleapis.com/bit_models/ilsvrc2012_1k_wordnet_lemmas.txt'
+IMAGENET_1K_LABELS = requests.get(IMAGENET_1K_URL).text.strip().split('\n')
+
+model = create_model('restnet50', pretrained=True)
+
+transform = create_transform(
+    **resolve_data_config({}, model=model)['test_time_augmentation'][0])
+
+
+model.eval()
+
+
+def predict(image):
+    img = image.convert('RGB')
+    transformed_image = transform(img).unsqueeze(0)
+    with torch.no_grad():
+        out = model(transformed_image)
+    probabilities = torch.nn.functional.softmax(out[0], dim=0)
+    values, indices = torch.topk(probabilities, k=5)
+    return {IMAGENET_1K_LABELS[i]: v.item() for i, v in zip(indices, values)}
+
+
+gr.Interface(predict, gr.inputs.Image(type='pil'),
+             output='label').launch()

requirements.txt

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+timm
+gradio