Update app.py

app.py

@@ -1,9 +1,11 @@
 import torch
 from transformers import RobertaTokenizer, RobertaForSequenceClassification, Trainer, TrainingArguments
 from datasets import load_dataset, load_metric
+import gradio as gr
 
 # Carregar o dataset IMDB
 dataset = load_dataset('imdb')
+metric = load_metric('accuracy')
 
 # Carregar o tokenizer e o modelo RoBERTa
 tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
@@ -57,15 +59,21 @@ print(results)
 model.save_pretrained('./model')
 tokenizer.save_pretrained('./model')
 
-from huggingface_hub import HfApi, HfFolder
+# Função de inferência
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    outputs = model(**inputs)
+    predictions = torch.argmax(outputs.logits, dim=-1)
+    return "Positive" if predictions.item() == 1 else "Negative"
 
-api = HfApi()
-token = HfFolder.get_token()
-
-api.upload_folder(
-    folder_path='./model',
-    repo_id='eldavid/roberta-imdb',
-    repo_type='model',
-    commit_message="Initial upload of RoBERTa IMDB model"
+# Interface Gradio
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter a movie review..."),
+    outputs="text",
+    title="IMDB Review Sentiment Analysis",
+    description="A simple Gradio interface to predict sentiment of IMDB movie reviews using a RoBERTa model."
 )
 
+if __name__ == "__main__":
+    iface.launch()