app.py

import gradio as gr
import arxiv
from transformers import pipeline

# Load summarization model
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")

# Search and summarize papers
def search_and_summarize(topic, sort_by_option):
    try:
        num_papers = 3  # fixed value
        sort_mapping = {
            "Relevance": arxiv.SortCriterion.Relevance,
            "Most Recent": arxiv.SortCriterion.SubmittedDate
        }

        search = arxiv.Search(
            query=topic,
            max_results=num_papers,
            sort_by=sort_mapping.get(sort_by_option, arxiv.SortCriterion.Relevance)
        )

        results = []
        for result in search.results():
            summary = summarizer(result.summary[:1000], max_length=120, min_length=30, do_sample=False)[0]['summary_text']
            authors = ", ".join([author.name for author in result.authors])
            published_date = result.published.date().strftime("%Y-%m-%d")
            result_block = (
                f"📘 *{result.title}*\n\n"
                f"👩‍🔬 Authors: {authors}\n"
                f"📅 Published: {published_date}\n\n"
                f"📝 Summary: {summary}\n\n"
                f"🔗 [Read More]({result.pdf_url})"
            )
            results.append(result_block)

        return "\n\n---\n\n".join(results) if results else "No results found."

    except Exception as e:
        return f"⚠️ An error occurred: {e}"

# Gradio UI
with gr.Blocks(theme=gr.themes.Base()) as demo:
    gr.Markdown("# 🤖 AI Research Assistant\nSummarize academic research papers using Hugging Face models + Arxiv!")

    with gr.Row():
        topic = gr.Textbox(label="🔍 Enter your research topic", placeholder="e.g. diffusion models in AI")
        sort_by = gr.Dropdown(choices=["Relevance", "Most Recent"], value="Relevance", label="Sort by")

    search_btn = gr.Button("Search 🔎")
    output = gr.Markdown()

    # Show loading message
    def show_loading():
        return "⏳ Loading, please wait..."

    search_btn.click(fn=show_loading, inputs=[], outputs=output, queue=False)
    search_btn.click(fn=search_and_summarize, inputs=[topic, sort_by], outputs=output)

demo.launch()