app.py

# app.py

import streamlit as st
import arxiv
import networkx as nx
import matplotlib.pyplot as plt
import datetime

from transformers import pipeline

# Initialize Hugging Face pipelines for summarization and text generation
@st.cache_resource(show_spinner=False)
def load_summarizer():
    return pipeline("summarization", model="facebook/bart-large-cnn")

@st.cache_resource(show_spinner=False)
def load_generator():
    return pipeline("text-generation", model="gpt2")

summarizer = load_summarizer()
generator = load_generator()

# -------------------------------
# Helper Functions
# -------------------------------

def retrieve_papers(query, max_results=5):
    """
    Retrieve academic papers from arXiv based on the query.
    """
    search = arxiv.Search(query=query, max_results=max_results)
    papers = []
    for result in search.results():
        paper = {
            "title": result.title,
            "summary": result.summary,
            "url": result.pdf_url,
            "authors": [author.name for author in result.authors],
            "published": result.published
        }
        papers.append(paper)
    return papers

def summarize_text(text):
    """
    Use a generative model to create a concise summary of the input text.
    """
    # The summarizer may need the text to be below a certain token length.
    # If necessary, you could chunk the text.
    summarized = summarizer(text, max_length=130, min_length=30, do_sample=False)
    return summarized[0]['summary_text']

def generate_concept_map(papers):
    """
    Generate a visual concept map by connecting papers with shared authors.
    """
    G = nx.Graph()
    # Add nodes for each paper title
    for paper in papers:
        G.add_node(paper['title'])
    # Create edges between papers that share at least one common author
    for i in range(len(papers)):
        for j in range(i + 1, len(papers)):
            common_authors = set(papers[i]['authors']).intersection(set(papers[j]['authors']))
            if common_authors:
                G.add_edge(papers[i]['title'], papers[j]['title'])
    return G

def generate_citation(paper):
    """
    Format citation information in APA style.
    """
    authors = ", ".join(paper['authors'])
    year = paper['published'].year if isinstance(paper['published'], datetime.datetime) else "n.d."
    title = paper['title']
    url = paper['url']
    citation = f"{authors} ({year}). {title}. Retrieved from {url}"
    return citation

def generate_proposal_suggestions(text):
    """
    Generate research proposal suggestions based on the synthesized literature review.
    """
    prompt = (
        "Based on the following literature review, propose a novel research proposal "
        "including potential research questions and an outline for experimental design.\n\n"
        f"{text}\n\nProposal:"
    )
    generated = generator(prompt, max_new_tokens=50, num_return_sequences=1)
    return generated[0]['generated_text']

# -------------------------------
# Streamlit User Interface
# -------------------------------

st.title("📚PaperPilot – The Intelligent Academic Navigator")
st.markdown("Welcome to **PaperPilot**! Enter a research topic or question below to retrieve academic papers, generate summaries, visualize concept maps, format citations, and get research proposal suggestions.")

# Input section
query = st.text_input("Research Topic or Question:")

if st.button("Search"):

    if query.strip() == "":
        st.warning("Please enter a research topic or question.")
    else:
        # --- Step 1: Retrieve Papers ---
        with st.spinner("Retrieving relevant academic papers..."):
            papers = retrieve_papers(query, max_results=5)
        
        if not papers:
            st.error("No papers found. Please try a different query.")
        else:
            st.success(f"Found {len(papers)} papers.")

            # --- Step 2: Display Retrieved Papers ---
            st.header("Retrieved Papers")
            for idx, paper in enumerate(papers, start=1):
                with st.expander(f"{idx}. {paper['title']}"):
                    st.markdown(f"**Authors:** {', '.join(paper['authors'])}")
                    st.markdown(f"**Published:** {paper['published'].strftime('%Y-%m-%d') if isinstance(paper['published'], datetime.datetime) else 'n.d.'}")
                    st.markdown(f"**Link:** [PDF Link]({paper['url']})")
                    st.markdown("**Abstract:**")
                    st.write(paper['summary'])

            # --- Step 3: Generate Summaries & Literature Review ---
            st.header("Automated Summaries & Literature Review")
            combined_summary = ""
            for paper in papers:
                st.subheader(f"Summary for: {paper['title']}")
                # Use the paper summary as input for further summarization
                summary_text = summarize_text(paper['summary'])
                st.write(summary_text)
                combined_summary += summary_text + " "

            # --- Step 4: Create Visual Concept Map & Gap Analysis ---
            st.header("Visual Concept Map & Gap Analysis")
            G = generate_concept_map(papers)
            if len(G.nodes) > 0:
                fig, ax = plt.subplots(figsize=(8, 6))
                pos = nx.spring_layout(G, seed=42)
                nx.draw_networkx(G, pos, with_labels=True, node_color='skyblue', edge_color='gray', node_size=1500, font_size=8, ax=ax)
                st.pyplot(fig)
            else:
                st.info("Not enough data to generate a concept map.")

            # --- Step 5: Citation & Reference Management ---
            st.header("Formatted Citations (APA Style)")
            for paper in papers:
                citation = generate_citation(paper)
                st.markdown(f"- {citation}")

            # --- Step 6: Research Proposal Assistance ---
            st.header("Research Proposal Suggestions")
            proposal = generate_proposal_suggestions(combined_summary)
            st.write(proposal)
st.caption("Built with ❤️")