Create app.py

app.py

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+ImageClassifier
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+app.py
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+Update app.py
+681c2fe
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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_wordnet_lemmas.txt"
+LABELS = requests.get(IMAGENET_1k_URL).text.strip().split('\n')
+
+model = create_model('resnet50', pretrained=True)
+
+transform = create_transform(
+    **resolve_data_config({}, model=model)
+)
+model.eval()
+
+def predict_fn(img):
+    img = img.convert('RGB')
+    img = transform(img).unsqueeze(0)
+
+    with torch.no_grad():
+        out = model(img)
+    
+    probabilites = torch.nn.functional.softmax(out[0], dim=0)
+
+    values, indices = torch.topk(probabilites, k=5)
+
+    return {LABELS[i]: v.item() for i, v in zip(indices, values)}
+    
+
+title = "Image Classifier"
+description = "Gradio Demo for Image Classifier built with pretrained model resnet50"
+examples = ['cat.jpg', 'dog.jpg']
+
+gr.Interface(predict_fn, gr.inputs.Image(type='pil'), outputs='label', title=title, description=description, examples=examples).launch()
+
+