Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+def recognize(img):
+  img1=img.resize((224,224))
+  img_norm = transform_norm(img1)
+  output = model(img_norm[None,])
+  out=output.argmax(dim=-1).item()
+  out1=(df.iloc[out,0])
+  return out1
+demo = gr.Interface(fn=recognize, inputs='pil',outputs='text',examples= [['Banana.jpg']])
+demo.launch()#debug=True)
+import torch
+from PIL import Image
+from torchvision import transforms
+from torchvision.transforms.functional import InterpolationMode
+from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+import hiera
+df=pd.read_csv('Imagenet.txt',usecols=[0],header=None)
+model = hiera.hiera_base_224(pretrained=True, checkpoint="mae_in1k_ft_in1k")
+input_size = 224
+transform_list = [
+transforms.Resize(int((256 / 224) * input_size), interpolation=InterpolationMode.BICUBIC),
+transforms.CenterCrop(input_size)
+]
+#transform_vis = transforms.Compose(transform_list)
+transform_norm = transforms.Compose(transform_list + [
+transforms.ToTensor(),
+transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD),
+])
+def recognize(img):
+  img1=img.resize((224,224))
+  img_norm = transform_norm(img1)
+  output = model(img_norm[None,])
+  out=output.argmax(dim=-1).item()
+  out1=(df.iloc[out,0])
+  return out1
+demo = gr.Interface(fn=recognize, inputs='pil',outputs='text',examples= [['Banana.jpg']])
+demo.launch()