add app.py

app.py

@@ -0,0 +1,32 @@
+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).unsqueze(0)
+
+    with torch._nograd():
+        out = model(img)
+
+    probabilities = torch.nn.functional.softmax(out[0], dim=0)
+
+    values, indices = torch.topk(probabilities,k=5)
+
+    return {LABELS[i]: v.item() for i,v in zip(indices,values)}
+
+gr.Interface(predict_fn,gr.inputs.Image(type='pil'), outputs='label').launch()

requirements.txt

@@ -0,0 +1,2 @@
+timm
+gradio