Update app.py

app.py

@@ -8,6 +8,10 @@ from transformers import RobertaTokenizerFast, RobertaForSequenceClassification,
 model = RobertaForSequenceClassification.from_pretrained('Prakhar618/Gptdetect')
 tokenizer = RobertaTokenizerFast.from_pretrained('Prakhar618/Gptdetect', max_length = 256)
 
+def tokenize_function(examples):
+    return tokenizer(examples['text'], padding=True, truncation=True,
+        max_length=256)
+    
 def predict(text):
     # Convert test dataframe to Hugging Face dataset
     test_dataset = Dataset.from_pandas(pd.DataFrame(text,columns=['text']))
@@ -18,10 +22,10 @@ def predict(text):
     y_pred = np.argmax(predictions, axis=1)
     return y_pred
     
+# Create Gradio interface
+text_input = gr.Textbox(lines=7, label="Input Text", placeholder="Enter your text here...")
+output_text = gr.Textbox(label="Predicted Sentiment")
 
-def tokenize_function(examples):
-    return tokenizer(examples['text'], padding=True, truncation=True,
-        max_length=256)
 
 test_args = TrainingArguments(
     do_train=False,
@@ -34,5 +38,5 @@ trainer = Trainer(
     args=test_args,
 )
 
-iface = gr.Interface(fn=predict, inputs="text", outputs="text")
-iface.launch()
+iface = gr.Interface(fn=predict, inputs=text_input, outputs=output_text)
+iface.launch(share=True)