Create app.py

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+import re
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("potsawee/t5-large-generation-squad-QuestionAnswer")
+model = AutoModelForSeq2SeqLM.from_pretrained("potsawee/t5-large-generation-squad-QuestionAnswer")
+
+def inference(input_text):
+    if input_text is None:
+        return "Please upload a text"
+
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+
+    sentences = re.split(r'(?<=[.!?])', input_text)
+    question_answer_pairs = []
+
+    for i, sentence in enumerate(sentences):
+        input_ids_clone = tokenizer.encode(sentence, return_tensors="pt")
+        outputs = model.generate(input_ids_clone, max_length=100, num_return_sequences=1)
+        question_answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        question = question_answer.strip()
+        question_answer_pairs.append((f"Question:", question))
+
+    result = ''
+    
+    for i in range(len(question_answer_pairs)):
+        if question_answer_pairs[i][1] == '':
+            break
+        question_part = question_answer_pairs[i][1].split("?")[0] + "?"
+        answer_part = question_answer_pairs[i][1].split("?")[1].strip()
+        result += f"Question: {question_part}\nAnswer: {answer_part}\n\n"
+
+    return result
+
+title = "Question Answer Pairs Generator"
+
+input_text = gr.Textbox(lines=4, label="Text:")
+
+interface = gr.Interface(
+    fn=inference,
+    inputs=[input_text],
+    outputs= "text",
+    title=title,
+)
+
+interface.launch()