Add the files

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Carregar o modelo diretamente do Hugging Face Hub
+classifier = pipeline("image-classification", model="mestrevh/computer-vision-cifar-10")
+
+# Função de classificação
+def predict_image(image):
+    return classifier(image)
+
+# Interface Gradio
+interface = gr.Interface(fn=predict_image, 
+                         inputs=gr.inputs.Image(type="pil"), 
+                         outputs="label", 
+                         live=True)
+
+interface.launch()

requirements.txt

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+transformers
+gradio

train.py

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+from transformers import Trainer, TrainingArguments
+from datasets import load_dataset
+from transformers import ViTForImageClassification, ViTFeatureExtractor
+
+# Carregar o dataset (exemplo com o dataset CIFAR-10)
+dataset = load_dataset("cifar10")
+
+# Carregar o modelo pré-treinado e o feature extractor
+model = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224-in21k")
+feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
+
+# Preprocessamento
+def preprocess_function(examples):
+    return feature_extractor(examples["image"], return_tensors="pt")
+
+# Aplicando o preprocessamento ao dataset
+dataset = dataset.map(preprocess_function, batched=True)
+
+# Definir os parâmetros de treinamento
+training_args = TrainingArguments(
+    output_dir="./results",
+    evaluation_strategy="epoch",
+    learning_rate=2e-5,
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=64,
+    num_train_epochs=3,
+    weight_decay=0.01,
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=dataset["train"],
+    eval_dataset=dataset["test"],
+)
+
+# Treinar o modelo
+trainer.train()
+
+model.save_pretrained("./computer-vision-cifar-10")
+feature_extractor.save_pretrained("./computer-vision-cifar-10")