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| import streamlit as st | |
| import requests | |
| import os | |
| API_BASE = os.getenv("API_URL", "http://127.0.0.1:8000") | |
| st.set_page_config(page_title="PaperLens", page_icon="🔍", layout="wide") | |
| # ── INIT SESSION STATE — MUST BE FIRST ─────────── | |
| if "summaries" not in st.session_state: | |
| st.session_state.summaries = {} | |
| if "search_results" not in st.session_state: | |
| st.session_state.search_results = None | |
| if "last_query" not in st.session_state: | |
| st.session_state.last_query = "" | |
| # ── HELPERS ────────────────────────────────────── | |
| def search_papers(query: str, top_k: int = 10): | |
| try: | |
| resp = requests.get(f"{API_BASE}/search", params={"query": query, "top_k": top_k}, timeout=30) | |
| return resp.json() if resp.status_code == 200 else None | |
| except Exception as e: | |
| st.error(f"Search failed: {e}") | |
| return None | |
| def get_summary(paper_id: str): | |
| try: | |
| resp = requests.get(f"{API_BASE}/summarize/{paper_id}", timeout=30) | |
| return resp.json() if resp.status_code == 200 else None | |
| except Exception as e: | |
| st.error(f"Summary failed: {e}") | |
| return None | |
| def check_api(): | |
| try: | |
| resp = requests.get(f"{API_BASE}/health", timeout=5) | |
| if resp.status_code == 200: | |
| return True, resp.json() | |
| return False, {} | |
| except Exception: | |
| return False, {} | |
| # ── HEADER ─────────────────────────────────────── | |
| st.markdown("# 🔍 PaperLens") | |
| st.markdown("**Semantic search engine for research papers — find papers by meaning, not keywords**") | |
| st.divider() | |
| # ── API STATUS ─────────────────────────────────── | |
| is_up, health = check_api() | |
| if is_up: | |
| st.markdown( | |
| f" **Online** | " | |
| f"**{health.get('paper_indexed', 0)} papers** | " | |
| f"**all-MiniLM-L6-v2** | " | |
| f"**FAISS IVFFlat** | " | |
| f"**Groq LLaMA-3.3-70b**", | |
| unsafe_allow_html=True | |
| ) | |
| else: | |
| st.error("❌ API offline — run: uvicorn src.api.main:app --reload") | |
| st.stop() | |
| st.divider() | |
| # ── SEARCH BAR ─────────────────────────────────── | |
| col1, col2 = st.columns([4, 1]) | |
| with col1: | |
| query = st.text_input("Search", placeholder="e.g. transformer attention mechanism...", label_visibility="collapsed") | |
| with col2: | |
| top_k = st.selectbox("Results", [5, 10, 20], index=1, label_visibility="collapsed") | |
| search_clicked = st.button("🔍 Search", type="primary", use_container_width=True) | |
| # ── SAVE RESULTS ON SEARCH ─────────────────────── | |
| if search_clicked and query.strip(): | |
| with st.spinner("Searching..."): | |
| data = search_papers(query, top_k) | |
| st.session_state.search_results = data | |
| st.session_state.last_query = query | |
| # ── DISPLAY RESULTS FROM SESSION STATE ─────────── | |
| data = st.session_state.search_results | |
| query = st.session_state.last_query | |
| if data and data.get("results"): | |
| st.markdown(f"**{data['total']} results** for *\"{query}\"* — `{data['latency_ms']}ms`") | |
| st.divider() | |
| for i, paper in enumerate(data["results"]): | |
| col1, col2 = st.columns([5, 1]) | |
| with col1: | |
| st.markdown(f"### {i+1}. {paper['title']}") | |
| with col2: | |
| st.markdown(f"**Score:** `{paper['score']:.3f}`") | |
| st.markdown( | |
| f"{paper['authors'][:80]}{'...' if len(paper['authors']) > 80 else ''} " | |
| f"| {paper.get('year', 'N/A')} | `{paper.get('field', '')}`" | |
| ) | |
| with st.expander("Abstract"): | |
| st.write(paper["abstract"]) | |
| if st.button("🤖 AI Summary", key=f"sum_{paper['id']}_{i}"): | |
| with st.spinner("Groq LLaMA-3 reading the paper..."): | |
| summary = get_summary(paper["id"]) | |
| if summary: | |
| st.session_state.summaries[paper["id"]] = summary | |
| if paper["id"] in st.session_state.summaries: | |
| s = st.session_state.summaries[paper["id"]] | |
| st.success(f"Generated in {s['latency_ms']}ms | Tokens: {s['tokens_used']}") | |
| st.markdown(s["summary"]) | |
| st.markdown(f"[View on arXiv]({paper['url']})") | |
| st.divider() | |
| # ── SIDEBAR ────────────────────────────────────── | |
| with st.sidebar: | |
| st.markdown("## About PaperLens") | |
| st.markdown(""" | |
| **Stack:** | |
| - sentence-transformers | |
| - FAISS IVFFlat | |
| - FastAPI + LRU cache | |
| - Groq LLaMA-3.3-70b | |
| - Streamlit | |
| **Benchmarks:** | |
| - 956 papers indexed | |
| - p50 latency: 0.02ms | |
| - QPS: 76,540 | |
| - Cache: 1738x speedup | |
| """) | |
| st.divider() | |
| st.markdown("**Try these:**") | |
| st.code("transformer attention") | |
| st.code("reinforcement learning") | |
| st.code("computer vision CNN") | |
| st.code("large language models") | |
| st.divider() | |
| st.markdown("Built by [Venkatesh P](https://linkedin.com/in/venkatesh-ml)") |