Create app.py

app.py

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+from transformers import ViTForImageClassification, ViTImageProcessor
+from datasets import load_dataset
+import gradio as gr
+
+eval = load_dataset("Marxulia/asl_sign_languages_alphabets_v02", split="train")
+eval = eval.rename_column('label', 'labels')
+id2label = {str(i): lab for i, lab in enumerate(eval.features["labels"].names)}
+
+trained_model = ViTForImageClassification.from_pretrained("falba/google-vit-base-ASL")
+processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224-in21k')
+
+
+with gr.Blocks() as demo:
+  gallery = gr.Gallery([i for i in eval['image']])
+  statement = gr.Label()
+
+  def on_select(evt: gr.SelectData):  # SelectData is a subclass of EventData
+      chosen_index = evt.index
+      chosen_image = eval['image'][chosen_index]
+      inputs = processor(images=chosen_image, return_tensors="pt")
+      outputs = trained_model(**inputs)
+      predicted_label_id = outputs.logits.argmax(-1).item()
+      predicted_label = id2label[str(predicted_label_id)]
+      actual_label = eval['labels'][chosen_index]
+      return f"Actual Label: {id2label[str(actual_label)]} | Predicted label: {predicted_label}"
+
+  gallery.select(on_select, None, statement)
+
+demo.launch()