Create app.py

app.py

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+from datasets import load_dataset
+
+dataset = load_dataset('rwcuffney/pick_a_card_test', batch_size=32, shuffle=True)
+
+from transformers import AutoModelForSequenceClassification
+
+model = AutoModelForSequenceClassification.from_pretrained('rwcuffney/autotrain-pick_a_card-3726099224')
+
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained('rwcuffney/autotrain-pick_a_card-3726099224')
+
+def preprocess_text(text):
+    encoded = tokenizer(text, padding='max_length', truncation=True, max_length=128, return_tensors='pt')
+    return encoded
+
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model.to(device)
+model.eval()
+
+for batch in dataset:
+    # Preprocess the text
+    text = batch['text']
+    inputs = preprocess_text(text)
+    inputs = inputs.to(device)
+
+    # Make predictions
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predicted_classes = torch.argmax(outputs.logits, dim=-1)
+
+    # Print the predicted class labels
+    predicted_labels = [dataset.features['label'].names[i] for i in predicted_classes]
+    print(predicted_labels)
+
+
+