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import gradio as gr |
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import arxiv |
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from transformers import pipeline |
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from sentence_transformers import SentenceTransformer, util |
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qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad") |
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embedder = SentenceTransformer("all-MiniLM-L6-v2") |
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def fetch_arxiv_papers(query, max_results=5): |
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search = arxiv.Search( |
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query=query, |
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max_results=max_results, |
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sort_by=arxiv.SortCriterion.Relevance |
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) |
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return list(search.results()) |
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def generate_answer(question, context): |
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return qa_pipeline(question=question, context=context)["answer"] |
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def evaluate_retrieval(query, context): |
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query_embed = embedder.encode(query, convert_to_tensor=True) |
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context_embed = embedder.encode(context, convert_to_tensor=True) |
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similarity = util.pytorch_cos_sim(query_embed, context_embed) |
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score = float(similarity[0][0]) |
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if score > 0.7: |
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return "High" |
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elif score > 0.4: |
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return "Medium" |
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else: |
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return "Low" |
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def evaluate_summary(context, answer): |
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if answer.lower() in context.lower(): |
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return "Good" |
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elif len(answer.split()) > 5: |
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return "Fair" |
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else: |
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return "Poor" |
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def rag_pipeline(user_query): |
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papers = fetch_arxiv_papers(user_query) |
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if not papers: |
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return "No relevant papers found.", "", "" |
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output_blocks = [] |
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for i, paper in enumerate(papers, 1): |
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title = paper.title |
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abstract = paper.summary |
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link = paper.entry_id |
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answer = generate_answer(user_query, abstract) |
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retrieval_score = evaluate_retrieval(user_query, abstract) |
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summary_score = evaluate_summary(abstract, answer) |
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block = f""" |
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### Paper {i} |
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Title: {title} |
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Abstract: {abstract} |
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Summarized Answer: {answer} |
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Link: {link} |
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Retrieval Accuracy: {retrieval_score} |
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Summary Quality: {summary_score} |
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---""" |
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output_blocks.append(block) |
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final_output = "\n".join(output_blocks) |
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return final_output, "Multiple Papers Shown", "See Above" |
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with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="green")) as demo: |
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gr.Markdown("## Scientific Paper Discovery — RAG-Based QA & Evaluation") |
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gr.Markdown("Enter your research topic below to explore papers from arXiv and get summarized insights.") |
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query = gr.Textbox(label="Enter a research topic") |
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run_btn = gr.Button("Search and Summarize") |
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output = gr.Markdown(label="Output") |
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retrieval_accuracy = gr.Textbox(label="Retrieval Accuracy") |
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summary_quality = gr.Textbox(label="Summary Quality") |
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run_btn.click(fn=rag_pipeline, inputs=query, outputs=[output, retrieval_accuracy, summary_quality]) |
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demo.launch() |
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