import os
import streamlit as st
import arxiv
import random
import datetime
import requests
from scholarly import scholarly

# -------------------------------
# Helper Functions
# -------------------------------
def get_paper_metadata(arxiv_id):
    """Fetch metadata like citations and connected papers for scoring."""
    metadata = {
        "citations": 0,
        "institution": "Unknown",
        "authors": [],
        "connected_papers": 0
    }
    
    # Fetch citation count from scite.ai
    scite_url = f"https://api.scite.ai/v1/papers/arxiv:{arxiv_id}"
    response = requests.get(scite_url)
    if response.status_code == 200:
        data = response.json()
        metadata["citations"] = data.get("citation_count", 0)
    
    # Fetch connected paper count from Connected Papers
    connected_papers_url = f"https://www.connectedpapers.com/api/graph/{arxiv_id}"
    response = requests.get(connected_papers_url)
    if response.status_code == 200:
        data = response.json()
        metadata["connected_papers"] = len(data.get("nodes", []))
    
    return metadata

def calculate_trust_score(metadata):
    """Compute trust score based on citations and author credibility."""
    trust_score = 50  # Base score
    
    # Citations factor (max boost 30 points)
    if metadata["citations"] > 100:
        trust_score += 30
    elif metadata["citations"] > 50:
        trust_score += 20
    elif metadata["citations"] > 10:
        trust_score += 10
    
    # Connected Papers factor (max boost 20 points)
    if metadata["connected_papers"] > 20:
        trust_score += 20
    elif metadata["connected_papers"] > 10:
        trust_score += 10
    
    return min(trust_score, 100)

def calculate_relevance_score(paper, query):
    """Compute relevance score based on keyword match and recency."""
    relevance_score = 50  # Base score
    
    # Keyword match factor
    query_terms = query.lower().split()
    title_terms = paper['title'].lower().split()
    match_count = len(set(query_terms) & set(title_terms))
    relevance_score += match_count * 5
    
    # Publication date factor
    if isinstance(paper['published'], datetime.datetime):
        years_old = datetime.datetime.now().year - paper['published'].year
        if years_old < 1:
            relevance_score += 15
        elif years_old < 3:
            relevance_score += 10
        elif years_old < 5:
            relevance_score += 5
    
    return min(relevance_score, 100)

def retrieve_papers(query, max_results=5):
    """Retrieve academic papers from arXiv."""
    search = arxiv.Search(query=query, max_results=max_results)
    papers = []
    for result in search.results():
        arxiv_id = result.entry_id.split("/")[-1]
        metadata = get_paper_metadata(arxiv_id)
        trust_score = calculate_trust_score(metadata)
        relevance_score = calculate_relevance_score({"title": result.title, "published": result.published}, query)
        
        paper = {
            "title": result.title,
            "summary": result.summary,
            "url": result.pdf_url,
            "doi": f"https://doi.org/10.48550/arXiv.{arxiv_id}",
            "bib_explorer": f"https://arxiv.org/abs/{arxiv_id}",
            "connected_papers": f"https://www.connectedpapers.com/api/graph/{arxiv_id}",
            "litmaps": f"https://app.litmaps.com/preview/{arxiv_id}",
            "scite": f"https://scite.ai/reports/arxiv:{arxiv_id}",
            "authors": [author.name for author in result.authors],
            "published": result.published,
            "trust_score": trust_score,
            "relevance_score": relevance_score
        }
        papers.append(paper)
    return papers

def random_paper_search():
    """Retrieve random papers without user input."""
    random_queries = ["artificial intelligence", "quantum computing", "neuroscience", "climate change", "robotics"]
    query = random.choice(random_queries)
    return retrieve_papers(query, max_results=random.randint(5, 15))

# -------------------------------
# Streamlit UI
# -------------------------------
st.title("📚 PaperPilot – Intelligent Academic Navigator")

with st.sidebar:
    st.header("🔍 Search Parameters")
    query = st.text_input("Research topic or question:")
    
    col1, col2 = st.columns([3, 1])
    with col1:
        search_button = st.button("🚀 Find Articles")
    with col2:
        random_button = st.button("🎲 Random Papers")
    
    if search_button and query.strip():
        with st.spinner("Searching arXiv..."):
            papers = retrieve_papers(query)
            if papers:
                st.session_state.papers = papers
            else:
                st.error("No papers found. Try different keywords.")
    elif random_button:
        with st.spinner("Fetching random papers..."):
            papers = random_paper_search()
            st.session_state.papers = papers

if 'papers' in st.session_state:
    for idx, paper in enumerate(st.session_state.papers, 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 'Unknown'}")
            st.markdown(f"**Trust Score:** {paper['trust_score']} / 100")
            st.markdown(f"**Relevance Score:** {paper['relevance_score']} / 100")
            st.markdown(f"**DOI:** [Link]({paper['doi']})")
            st.markdown(f"**Bibliographic Explorer:** [Link]({paper['bib_explorer']})")
            st.markdown(f"**Connected Papers:** [Link]({paper['connected_papers']})")
            st.markdown(f"**Litmaps:** [Link]({paper['litmaps']})")
            st.markdown(f"**Scite:** [Link]({paper['scite']})")
            st.markdown("**Abstract:**")
            st.write(paper['summary'])