Create app.py

app.py

@@ -0,0 +1,30 @@
+import torch
+from PIL import Image
+import torchvision.transforms as transforms
+import numpy as np
+import json
+import requests
+import matplotlib.pyplot as plt
+
+import gradio as gr
+
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+efficientnet = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_efficientnet_b0', pretrained=True)
+utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_convnets_processing_utils')
+
+efficientnet.eval().to(device)
+
+def inference(img):
+  batch = torch.cat(
+    [utils.prepare_input_from_uri(img)]
+  ).to(device)
+  with torch.no_grad():
+      output = torch.nn.functional.softmax(efficientnet(batch), dim=1)
+      
+    
+  results = utils.pick_n_best(predictions=output, n=5)
+  
+  return results
+  
+gr.Interface(inference,gr.inputs.Image(type="file"),"text").launch()