app basic structure

app.py

@@ -0,0 +1,60 @@
+import re
+
+import gradio as gr
+import requests
+import xmltodict
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+from transformers.pipelines.question_answering import QuestionAnsweringPipeline
+
+QA_MODEL_NAME = "ixa-ehu/SciBERT-SQuAD-QuAC"
+
+
+def clean_text(text: str) -> str:
+    text = re.sub("\n", " ", text)
+    return text
+
+
+def get_paper_summary(arxiv_id: str) -> str:
+    paper_url = f"http://export.arxiv.org/api/query?id_list={arxiv_id}"
+    response = requests.get(paper_url)
+    paper_dict = xmltodict.parse(response.content)["feed"]["entry"]
+    return clean_text(paper_dict["summary"])
+
+
+def get_qa_pipeline(qa_model_name: str = QA_MODEL_NAME) -> QuestionAnsweringPipeline:
+    tokenizer = AutoTokenizer.from_pretrained(qa_model_name)
+    model = AutoModelForQuestionAnswering.from_pretrained(qa_model_name)
+    qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)
+    return qa_pipeline
+
+
+def get_answer(question: str, context: str) -> str:
+    qa_pipeline = get_qa_pipeline()
+    prediction = qa_pipeline(question=question, context=context)
+    return prediction["answer"]
+
+
+demo = gr.Blocks()
+
+
+with demo:
+    gr.Markdown("# Document QA")
+
+    # Retrieve paper
+    arxiv_id = gr.Textbox(
+        label="arXiv Paper ID", placeholder="Insert here the ID of a paper on arXiv"
+    )
+    paper_summary = gr.Textbox(label="Paper summary")
+    fetch_document_button = gr.Button("Get Summary")
+    fetch_document_button.click(
+        fn=get_paper_summary, inputs=arxiv_id, outputs=paper_summary
+    )
+
+    # QA on paper
+    question = gr.Textbox(label="Ask a question about the paper:")
+    answer = gr.Textbox("Answer:")
+    ask_button = gr.Button("Ask me 🤖")
+    ask_button.click(fn=get_answer, inputs=[question, paper_summary], outputs=answer)
+
+
+demo.launch()