Create app.py

app.py

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+from transformers import ViTImageProcessor, ViTForImageClassification
+import torch
+import gradio as gr
+
+feature_extractor = ViTImageProcessor.from_pretrained("car_scene_model")
+model = ViTForImageClassification.from_pretrained("car_scene_model")
+
+labels = ['Exterior', 'Interior', 'Unknown']
+
+def classify(im):
+  features = feature_extractor(im, return_tensors='pt')
+  logits = model(features["pixel_values"])[-1]
+  probability = torch.nn.functional.softmax(logits, dim=-1)
+  probs = probability[0].detach().numpy()
+  confidences = {label: float(probs[i]) for i, label in enumerate(labels)} 
+  return confidences
+
+
+description = """
+Car scene recognition demo
+        """
+interface = gr.Interface(fn=classify,
+                         inputs="image",
+                         outputs="label",
+                         title="Car scene recognition)",
+                         description=description ) 
+
+interface.launch()