initial commit

app.py

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+from _typeshed import OpenBinaryModeUpdating
+from types import resolve_bases
+import requests
+import gradio as gr
+import torch
+from timm import create_model
+from tim.data import reslove_data_config
+from timm.data.transformer 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('restnet50',pretrained=True)
+
+transofrm = create_transform(**resolve_data_config{}, model=model)
+
+model.eval()
+
+def predict_fn(img):
+    img = img.convert('RGB')
+    img = transofrm(img).unsqueez(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

demo1.py

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+import gradio as gr
+from gradio import inputs
+
+
+def greet(name):
+    return "Hello " + name + "!!"
+
+
+ui = gr.Interface(greet, inputs="text", outputs="text")
+ui.launch()

requirements.txt

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+gradio
+timm