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app.py

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+import gradio as gr
+import pandas as pd
+import re
+import os
+import fitz
+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 extract_text_from_pdf(pdf_file_path):
+    doc = fitz.open(pdf_file_path) 
+    text = ""
+    for page in doc:
+        text+=page.get_text() 
+
+    return text
+
+def generate_question_answer_pairs(pdf_file):
+    if pdf_file is None:
+        return "Please upload a PDF file"
+
+    d = {'Question':[],'Answer':[]}
+    df = pd.DataFrame(data=d)
+
+    pdf_text = extract_text_from_pdf(pdf_file.name)
+
+    sentences = re.split(r'(?<=[.!?])', pdf_text)
+    question_answer_pairs = []
+
+    for sentence in sentences:
+        input_ids = tokenizer.encode(sentence, return_tensors="pt")
+        outputs = model.generate(input_ids, max_length=100, num_return_sequences=1)
+        question_answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        question_answer_pairs.append(question_answer)
+
+    result = ''
+
+    for question_answer in question_answer_pairs:
+        qa_parts = question_answer.split("?")
+        if len(qa_parts) >= 2:
+            question_part = qa_parts[0] + "?"
+            answer_part = qa_parts[1].strip()
+            new_data = {'Question': [question_part], 'Answer': [answer_part]}
+            df = pd.concat([df, pd.DataFrame(new_data)], ignore_index=True)
+            result += f"Question: {question_part}\nAnswer: {answer_part}\n\n"
+            
+    df.to_csv("QAPairs.csv")
+    return result, "QAPairs.csv"
+
+title = "Question-Answer Pairs Generation"
+input_file = gr.File(label="Upload a PDF file")
+output_file = gr.File(label="Download as csv")
+output_text = gr.Textbox()
+
+interface = gr.Interface(
+    fn=generate_question_answer_pairs,
+    inputs=input_file,
+    outputs=[output_text, output_file],
+    title=title,
+)
+interface.launch()

requirements.txt

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+torch
+transformers
+pymupdf