Create app.py

app.py

@@ -0,0 +1,45 @@
+import gradio as gr
+from transformers import (
+      BartForConditionalGeneration,
+      BartTokenizer
+)
+
+model_name = 'unlisboa/bart_qa_assistant'
+tokenizer = BartTokenizer.from_pretrained(model_name)
+device = get_device()
+model = BartForConditionalGeneration.from_pretrained(model_name).to(device)
+model.eval()
+
+def example(question, censor):
+    print(question, censor)
+    return question + str(censor)
+    
+examples = [["What's the meaning of life?", True]]
+
+checkbox = gr.Checkbox(value=True, label="should censor output")
+question_input = gr.Textbox(lines=2, label='Question:')
+
+model_input = tokenizer(question_input, truncation=True, padding=True, return_tensors="pt")
+generated_answers_encoded = model.generate(input_ids=model_input["input_ids"].to(device),
+                                           attention_mask=model_input["attention_mask"].to(device),
+                                           #bad_words_ids=bad_words_ids,
+                                           force_words_ids=None,
+                                           min_length=1,
+                                           max_length=100,
+                                           do_sample=True,
+                                           early_stopping=True,
+                                           num_beams=4,
+                                           temperature=1.0,
+                                           top_k=None,
+                                           top_p=None,
+                                           # eos_token_id=tokenizer.eos_token_id,
+                                           no_repeat_ngram_size=2,
+                                           num_return_sequences=1,
+                                           return_dict_in_generate=True,
+                                           output_scores=True)
+
+response = tokenizer.batch_decode(generated_answers_encoded['sequences'], skip_special_tokens=True,
+                                  clean_up_tokenization_spaces=True)[0]
+
+answer_output = gr.Textbox(lines=2, label='Answer:')
+gr.Interface(fn=example, inputs=[question_input, checkbox], outputs=[answer_output], allow_flagging="never", examples=examples).launch()