Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Carregar o modelo e o tokenizer
+model_name = "vic35get/nhtsa_complaints_classifier"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Função para inferência
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    return torch.argmax(outputs.logits, dim=1).item()
+
+# Interface Gradio
+iface = gr.Interface(fn=predict, inputs="text", outputs="text")
+
+# Rodar a interface
+iface.launch()