Upload app.py

app.py

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