Create app.py

app.py

@@ -0,0 +1,42 @@
+import gradio as gr
+import random
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from question_generation import question_generation_sampling
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+g1_tokenizer = AutoTokenizer.from_pretrained("potsawee/t5-large-generation-squad-QuestionAnswer")
+g1_model = AutoModelForSeq2SeqLM.from_pretrained("potsawee/t5-large-generation-squad-QuestionAnswer")
+g2_tokenizer = AutoTokenizer.from_pretrained("potsawee/t5-large-generation-race-Distractor")
+g2_model = AutoModelForSeq2SeqLM.from_pretrained("potsawee/t5-large-generation-race-Distractor")
+g1_model.eval()
+g2_model.eval()
+g1_model.to(device)
+g2_model.to(device)
+
+
+def generate_multiple_choice_question(
+    context
+):
+    num_questions = 1
+    question_item = question_generation_sampling(
+        g1_model, g1_tokenizer,
+        g2_model, g2_tokenizer,
+        context, num_questions, device
+    )[0]
+    question = question_item['question']
+    options = question_item['options']
+    options[0] = f"{options[0]} [ANSWER]"
+    random.shuffle(options)
+    output_string = f"Question: {question}\n[A] {options[0]}\n[B] {options[1]}\n[C] {options[2]}\n[D] {options[3]}"
+    return output_string
+
+demo = gr.Interface(
+    fn=generate_multiple_choice_question,
+    inputs=gr.Textbox(lines=8, placeholder="Context Here..."),
+    outputs=gr.Textbox(lines=5, placeholder="Question: \n[A] \n[B] \n[C] \n[D] "),
+    title="Multiple-choice Question Generator",
+    description="Provide some context (e.g. news article or any passage) in the context box and click **Submit**. The models currently support English only. This demo is a part of MQAG - https://github.com/potsawee/mqag0.",
+    allow_flagging='never'
+)
+demo.launch()