Update app.py

app.py

@@ -1,4 +1,16 @@
 import gradio as gr
+import transformers
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# model & tokenizer
+model_name = "t5-base-qa-squad-v1.1-portuguese"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# parameters
+max_target_length=32
+num_beams=1
+early_stopping=True
 
 title = "QA App | T5 base finetuned on SQuAD 1.1 in Portuguese"
 description = "Forneça seu próprio parágrafo e faça perguntas sobre o texto. Quão bem o modelo responde? (este aplicativo usa o modelo https://huggingface.co/pierreguillou/t5-base-qa-squad-v1.1-portuguese)"
@@ -8,12 +20,29 @@ context = "A pandemia de COVID-19, também conhecida como pandemia de coronavír
 
 question = "Quando começou a pandemia de Covid-19 no mundo?"
 
-gr.Interface.load(
-             "pierreguillou/t5-base-qa-squad-v1.1-portuguese",
-             css=".footer {display:none !important}",
-             inputs=[gr.inputs.Textbox(lines=7, default=context, label="Context paragraph"), gr.inputs.Textbox(lines=2, default=question, label="Question")],
-             outputs=[gr.outputs.Textbox(label="Answer"), gr.outputs.Textbox(label="Score")],
-             title=title,
-             description=description,
-             article=article,
-             ).launch()
+def qa(question, context):
+  input_text = "question: " + question + "context: " + context
+  inputs = tokenizer(input_text, return_tensors="pt")
+
+  outputs = model.generate(inputs["input_ids"],
+                             max_length=max_target_length, 
+                             num_beams=num_beams, 
+                             early_stopping=early_stopping
+                            )
+  pred = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
+  return pred
+             
+# interface gradio
+iface = gr.Interface(
+    title=title,
+    description=description,
+    article=article,
+    allow_screenshot=allow_screenshot,
+    allow_flagging=allow_flagging,
+    fn=qa,
+    inputs=[gr.inputs.Textbox(placeholder="Digite uma questao aqui:", lines=5),gr.inputs.Textbox(placeholder="Digite um contexto aqui:", lines=5)],
+    outputs=[gr.outputs.HTML(label="QA com T5 base")],
+    examples=examples
+)
+
+iface.launch()