update website

example/backend/server.py

@@ -0,0 +1,166 @@
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import uvicorn
+import json
+import numpy as np
+import torch
+import faiss
+import time
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import voyageai
+from typing import List, Dict, Any, Optional
+
+# Initialize FastAPI
+app = FastAPI(title="Research Paper RAG API")
+
+# Configure CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # In production, replace with your Svelte app's domain
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load existing embeddings and papers
+paper_embeddings = np.load("paper_embeddings/embeddings_final.npy")
+
+dimension = paper_embeddings.shape[1]  # Get embedding dimension
+
+# Initialize FAISS index
+index = faiss.IndexFlatIP(dimension)  # Use Inner Product similarity
+index.add(paper_embeddings)
+
+with open("paper_embeddings/paper_final.json", "r") as f:
+    papers = json.load(f)
+
+# Initialize models and tokenizer
+llama_model_name = "OpenScholar/Llama-3.1_OpenScholar-8B"
+
+# Initialize Llama model
+llama_tokenizer = AutoTokenizer.from_pretrained(llama_model_name)
+llama_model = AutoModelForCausalLM.from_pretrained(
+    llama_model_name,
+    torch_dtype=torch.float16,
+    device_map="cuda"
+)
+
+# Initialize VoyageAI client
+voyage_client = voyageai.Client()
+
+class QueryRequest(BaseModel):
+    message: str
+
+class PaperInfo(BaseModel):
+    title: str
+    abstract: str
+    similarity: str
+
+class QueryResponse(BaseModel):
+    response: str
+    papers: List[PaperInfo]
+
+def get_voyage_embedding(text, model="voyage-3-lite"):
+    """Get embeddings for text using VoyageAI API with rate limiting and retry logic."""
+    max_retries = 3
+    retry_delay = 5
+    
+    for attempt in range(max_retries):
+        try:
+            response = voyage_client.embed(
+                model=model,
+                texts=[text]
+            )
+            return np.array(response.embeddings[0])
+            
+        except Exception as e:
+            if attempt == max_retries - 1:  # Last attempt
+                print(f"Failed after {max_retries} attempts: {str(e)}")
+                return None
+            print(f"Attempt {attempt + 1} failed: {str(e)}. Retrying in {retry_delay} seconds...")
+            time.sleep(retry_delay)
+            retry_delay *= 2  # Exponential backoff
+
+def find_relevant_papers(query_embedding, top_k=5):
+    # Perform similarity search using FAISS
+    scores, indices = index.search(query_embedding.reshape(1, -1), top_k)
+    
+    # Get the papers and their similarity scores
+    relevant_papers = [papers[idx] for idx in indices[0]]
+    similarities = scores[0]  # Scores are already cosine similarities
+    
+    return relevant_papers, similarities
+
+def format_papers_context(papers, similarities):
+    relevant_papers = []
+    context = "Based on the following relevant papers:\n\n"
+    for paper, similarity in zip(papers, similarities):
+        original_paper = json.load(open(paper['path']))
+        relevant_papers.append(original_paper)
+        context += f"Title: {original_paper['title']}\n"
+        context += f"Published Time: {original_paper['published_time']}\n"
+        context += f"Abstract: {original_paper['abstract']}\n\n"
+    return context, relevant_papers
+
+@app.post("/api/generate", response_model=QueryResponse)
+async def generate_response(request: QueryRequest):
+    try:
+        # Generate embedding for the query
+        query_embedding = get_voyage_embedding(request.message)
+        
+        if query_embedding is None:
+            raise HTTPException(status_code=500, detail="Failed to generate embedding")
+        
+        # Find relevant papers
+        relevant_papers, similarities = find_relevant_papers(query_embedding)
+
+        # Create context from relevant papers
+        context, paper_objects = format_papers_context(relevant_papers, similarities)
+        
+        # Prepare the prompt with context
+        full_prompt = f"{context}\n\nQuestion: {request.message}\nAnswer:"
+        
+        # Generate response using Llama model
+        inputs = llama_tokenizer(full_prompt, return_tensors="pt").to(llama_model.device)
+        
+        with torch.no_grad():
+            outputs = llama_model.generate(
+                inputs.input_ids,
+                max_new_tokens=500,
+                num_return_sequences=1,
+                temperature=0.7,
+                do_sample=True,
+                pad_token_id=llama_tokenizer.eos_token_id
+            )
+        
+        response = llama_tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
+
+        # Format paper info for response
+        paper_info = []
+        for i in range(min(3, len(paper_objects))):
+            paper_info.append(PaperInfo(
+                title=paper_objects[i]['title'],
+                abstract=paper_objects[i]['abstract'],
+                similarity=f"Similarity Score: {similarities[i]:.3f}"
+            ))
+            
+        # Ensure we have exactly 3 papers in the response
+        while len(paper_info) < 3:
+            paper_info.append(PaperInfo(
+                title="",
+                abstract="",
+                similarity=""
+            ))
+            
+        return QueryResponse(
+            response=response,
+            papers=paper_info
+        )
+    
+    except Exception as e:
+        print(f"Error processing request: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
+
+if __name__ == "__main__":
+    uvicorn.run("server:app", host="0.0.0.0", port=8000, reload=True)

example/src/app.css

@@ -0,0 +1,162 @@
+/* Reset and base styles */
+* {
+    margin: 0;
+    padding: 0;
+    box-sizing: border-box;
+  }
+  
+  body {
+    font-family: system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 
+      Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;
+    font-size: 16px;
+    line-height: 1.5;
+    color: #333;
+    background-color: #f9f9f9;
+  }
+  
+  h1, h2, h3, h4, h5, h6 {
+    margin-bottom: 0.5em;
+    font-weight: 600;
+    line-height: 1.2;
+  }
+  
+  h1 {
+    font-size: 2rem;
+  }
+  
+  h2 {
+    font-size: 1.75rem;
+  }
+  
+  h3 {
+    font-size: 1.5rem;
+  }
+  
+  p {
+    margin-bottom: 1rem;
+  }
+  
+  a {
+    color: #3498db;
+    text-decoration: none;
+  }
+  
+  a:hover {
+    text-decoration: underline;
+  }
+  
+  button {
+    cursor: pointer;
+  }
+  
+  /* Utility classes */
+  .container {
+    max-width: 1200px;
+    margin: 0 auto;
+    padding: 0 20px;
+  }
+  
+  .text-center {
+    text-align: center;
+  }
+  
+  .mb-1 {
+    margin-bottom: 0.5rem;
+  }
+  
+  .mb-2 {
+    margin-bottom: 1rem;
+  }
+  
+  .mb-3 {
+    margin-bottom: 1.5rem;
+  }
+  
+  .mb-4 {
+    margin-bottom: 2rem;
+  }
+  
+  /* Form elements */
+  input, textarea, select {
+    width: 100%;
+    padding: 0.75rem;
+    border: 1px solid #ddd;
+    border-radius: 4px;
+    font-size: 1rem;
+    font-family: inherit;
+  }
+  
+  input:focus, textarea:focus, select:focus {
+    outline: none;
+    border-color: #3498db;
+    box-shadow: 0 0 0 2px rgba(52, 152, 219, 0.2);
+  }
+  
+  label {
+    display: block;
+    margin-bottom: 0.5rem;
+    font-weight: 500;
+  }
+  
+  /* Button styles */
+  .btn {
+    display: inline-block;
+    background-color: #3498db;
+    color: white;
+    padding: 0.75rem 1.5rem;
+    border: none;
+    border-radius: 4px;
+    font-size: 1rem;
+    font-weight: 500;
+    text-align: center;
+    transition: background-color 0.2s, transform 0.1s;
+  }
+  
+  .btn:hover {
+    background-color: #2980b9;
+    text-decoration: none;
+  }
+  
+  .btn:active {
+    transform: translateY(1px);
+  }
+  
+  .btn-secondary {
+    background-color: #95a5a6;
+  }
+  
+  .btn-secondary:hover {
+    background-color: #7f8c8d;
+  }
+  
+  /* Card style */
+  .card {
+    background-color: white;
+    border-radius: 8px;
+    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
+    overflow: hidden;
+  }
+  
+  .card-header {
+    padding: 1rem;
+    border-bottom: 1px solid #eee;
+  }
+  
+  .card-body {
+    padding: 1rem;
+  }
+  
+  /* Responsive adjustments */
+  @media (max-width: 768px) {
+    h1 {
+      font-size: 1.75rem;
+    }
+    
+    h2 {
+      font-size: 1.5rem;
+    }
+    
+    h3 {
+      font-size: 1.25rem;
+    }
+  }

example/src/routes/+layout.svelte

@@ -0,0 +1,81 @@
+<script>
+  import '../app.css';
+</script>
+
+<div class="app">
+  <header>
+    <div class="header-content">
+      <h1>Research Paper Assistant</h1>
+      <nav>
+        <a href="/">Home</a>
+        <a href="/about">About</a>
+      </nav>
+    </div>
+  </header>
+  
+  <main>
+    <slot />
+  </main>
+  
+  <footer>
+    <p>&copy; {new Date().getFullYear()} Research Paper RAG System - Built with Svelte</p>
+  </footer>
+</div>
+
+<style>
+  .app {
+    display: flex;
+    flex-direction: column;
+    min-height: 100vh;
+  }
+  
+  header {
+    background-color: #2c3e50;
+    color: white;
+    padding: 1rem 0;
+    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+  }
+  
+  .header-content {
+    max-width: 1200px;
+    margin: 0 auto;
+    padding: 0 20px;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+  }
+  
+  header h1 {
+    margin: 0;
+    font-size: 1.5rem;
+  }
+  
+  nav {
+    display: flex;
+    gap: 20px;
+  }
+  
+  nav a {
+    color: white;
+    text-decoration: none;
+    font-weight: 500;
+    padding: 5px;
+  }
+  
+  nav a:hover {
+    text-decoration: underline;
+  }
+  
+  main {
+    flex: 1;
+  }
+  
+  footer {
+    background-color: #f8f9fa;
+    padding: 1rem 0;
+    text-align: center;
+    font-size: 0.875rem;
+    color: #7f8c8d;
+    border-top: 1px solid #ecf0f1;
+  }
+</style>

example/src/routes/+page.svelte

@@ -1,2 +1,246 @@
-<h1>Welcome to SvelteKit</h1>
-<p>Visit <a href="https://svelte.dev/docs/kit">svelte.dev/docs/kit</a> to read the documentation</p>
+<script>
+  import { onMount } from 'svelte';
+  
+  // State variables
+  let question = '';
+  let isLoading = false;
+  let response = '';
+  let papers = [
+    { title: '', abstract: '', similarity: '' },
+    { title: '', abstract: '', similarity: '' },
+    { title: '', abstract: '', similarity: '' }
+  ];
+  
+  // Example questions
+  const examples = [
+    "Can you recommend some papers about perovskite which are published in last 5 years?",
+    "Explain the concept of attention mechanisms in deep learning.",
+    "What are the main challenges in few-shot learning?"
+  ];
+  
+  // Function to handle form submission
+  async function handleSubmit() {
+    if (!question.trim()) return;
+    
+    isLoading = true;
+    response = '';
+    
+    try {
+      const res = await fetch('/api/query', {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json'
+        },
+        body: JSON.stringify({ message: question })
+      });
+      
+      if (!res.ok) throw new Error('Failed to get response');
+      
+      const data = await res.json();
+      
+      // Update state with response data
+      response = data.response;
+      papers = [
+        { 
+          title: data.papers[0].title, 
+          abstract: data.papers[0].abstract, 
+          similarity: data.papers[0].similarity 
+        },
+        { 
+          title: data.papers[1].title, 
+          abstract: data.papers[1].abstract, 
+          similarity: data.papers[1].similarity 
+        },
+        { 
+          title: data.papers[2].title, 
+          abstract: data.papers[2].abstract, 
+          similarity: data.papers[2].similarity 
+        }
+      ];
+    } catch (error) {
+      console.error('Error:', error);
+      response = `An error occurred: ${error.message}`;
+    } finally {
+      isLoading = false;
+    }
+  }
+  
+  // Function to use an example question
+  function useExample(example) {
+    question = example;
+  }
+</script>
+
+<svelte:head>
+  <title>RAG-Enhanced Llama-3.1 Chat Interface</title>
+</svelte:head>
+
+<main class="container">
+  <h1>RAG-Enhanced Llama-3.1 Chat Interface</h1>
+  <p>Ask questions about papers in the database. The system will find relevant papers and use them to generate informed responses.</p>
+  
+  <div class="query-section">
+    <textarea 
+      bind:value={question} 
+      rows="3" 
+      placeholder="Enter your question..."
+      aria-label="Question"
+    ></textarea>
+    
+    <button on:click={handleSubmit} disabled={isLoading}>
+      {isLoading ? 'Processing...' : 'Submit'}
+    </button>
+  </div>
+  
+  <div class="examples-section">
+    <h3>Examples:</h3>
+    <div class="examples-list">
+      {#each examples as example}
+        <button class="example-button" on:click={() => useExample(example)}>
+          {example}
+        </button>
+      {/each}
+    </div>
+  </div>
+  
+  {#if response}
+    <div class="response-section">
+      <h2>Generated Response</h2>
+      <div class="response-box">
+        {response}
+      </div>
+    </div>
+  {/if}
+  
+  {#if papers[0].title}
+    <div class="papers-section">
+      <h2>Relevant Papers</h2>
+      <div class="papers-grid">
+        {#each papers as paper, i}
+          <div class="paper-card">
+            <h3>Paper {i+1}</h3>
+            <h4>{paper.title}</h4>
+            <p class="similarity">{paper.similarity}</p>
+            <div class="abstract">
+              <h5>Abstract</h5>
+              <p>{paper.abstract}</p>
+            </div>
+          </div>
+        {/each}
+      </div>
+    </div>
+  {/if}
+</main>
+
+<style>
+  .container {
+    max-width: 1200px;
+    margin: 0 auto;
+    padding: 20px;
+    font-family: system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;
+  }
+  
+  h1 {
+    color: #2c3e50;
+    margin-bottom: 10px;
+  }
+  
+  .query-section {
+    margin: 20px 0;
+  }
+  
+  textarea {
+    width: 100%;
+    padding: 12px;
+    border: 1px solid #ddd;
+    border-radius: 4px;
+    font-size: 16px;
+    margin-bottom: 10px;
+    font-family: inherit;
+  }
+  
+  button {
+    background-color: #3498db;
+    color: white;
+    border: none;
+    padding: 10px 20px;
+    border-radius: 4px;
+    cursor: pointer;
+    font-size: 16px;
+    transition: background-color 0.3s;
+  }
+  
+  button:hover:not(:disabled) {
+    background-color: #2980b9;
+  }
+  
+  button:disabled {
+    background-color: #95a5a6;
+    cursor: not-allowed;
+  }
+  
+  .examples-section {
+    margin: 20px 0;
+  }
+  
+  .examples-list {
+    display: flex;
+    flex-direction: column;
+    gap: 10px;
+  }
+  
+  .example-button {
+    background-color: #ecf0f1;
+    color: #2c3e50;
+    text-align: left;
+    padding: 10px;
+  }
+  
+  .response-section, .papers-section {
+    margin-top: 30px;
+  }
+  
+  .response-box {
+    background-color: #f8f9fa;
+    padding: 15px;
+    border-radius: 4px;
+    border-left: 4px solid #3498db;
+    white-space: pre-wrap;
+  }
+  
+  .papers-grid {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(300px, 1fr));
+    gap: 20px;
+    margin-top: 20px;
+  }
+  
+  .paper-card {
+    border: 1px solid #ddd;
+    border-radius: 4px;
+    padding: 15px;
+    background-color: #fff;
+    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+  }
+  
+  .similarity {
+    font-size: 14px;
+    color: #7f8c8d;
+    margin: 5px 0;
+  }
+  
+  .abstract {
+    margin-top: 10px;
+  }
+  
+  .abstract h5 {
+    margin-bottom: 5px;
+  }
+  
+  .abstract p {
+    font-size: 14px;
+    line-height: 1.5;
+    max-height: 200px;
+    overflow-y: auto;
+  }
+</style>

example/src/routes/about/+page.svelte

@@ -0,0 +1,118 @@
+<script>
+  // Any specific about page logic can go here
+</script>
+
+<svelte:head>
+  <title>About - Research Paper Assistant</title>
+</svelte:head>
+
+<div class="container">
+  <h1>About This Project</h1>
+  
+  <div class="card mb-4">
+    <div class="card-body">
+      <h2>RAG-Enhanced Research Paper Assistant</h2>
+      <p>
+        This application uses a Retrieval-Augmented Generation (RAG) system to provide intelligent responses 
+        to research queries. The system searches through a database of scientific papers to find relevant 
+        information and uses the Llama-3.1 language model to generate responses.
+      </p>
+    </div>
+  </div>
+  
+  <div class="mb-4">
+    <h2>How It Works</h2>
+    <p>Our system follows these steps to answer your research questions:</p>
+    
+    <ol>
+      <li>Your question is converted into a vector embedding using VoyageAI's embedding model.</li>
+      <li>This embedding is compared against our database of paper embeddings to find the most relevant papers.</li>
+      <li>The top papers are retrieved and provided as context to the Llama-3.1 language model.</li>
+      <li>The model generates a response based on the relevant papers and your question.</li>
+      <li>Both the response and the relevant papers are displayed to you.</li>
+    </ol>
+  </div>
+  
+  <div class="mb-4">
+    <h2>Technology Stack</h2>
+    <div class="tech-grid">
+      <div class="tech-item">
+        <h3>Frontend</h3>
+        <ul>
+          <li>Svelte & SvelteKit</li>
+          <li>Modern CSS</li>
+        </ul>
+      </div>
+      
+      <div class="tech-item">
+        <h3>Backend</h3>
+        <ul>
+          <li>Python</li>
+          <li>OpenScholar/Llama-3.1_OpenScholar-8B</li>
+          <li>VoyageAI Embeddings</li>
+          <li>FAISS Vector Database</li>
+        </ul>
+      </div>
+      
+      <div class="tech-item">
+        <h3>Data</h3>
+        <ul>
+          <li>Scientific paper database</li>
+          <li>Pre-computed vector embeddings</li>
+        </ul>
+      </div>
+    </div>
+  </div>
+  
+  <div>
+    <h2>Using The System</h2>
+    <p>
+      To use the system, simply enter your research question in the input field on the homepage and click "Submit". 
+      Try to be specific with your questions to get the most relevant results. You can also use one of the example 
+      questions to get started.
+    </p>
+    <p>
+      The system will display both the AI-generated response and the top relevant papers that were used to inform 
+      that response. You can explore these papers further by reading their abstracts.
+    </p>
+  </div>
+</div>
+
+<style>
+  .tech-grid {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(250px, 1fr));
+    gap: 20px;
+    margin-top: 20px;
+  }
+  
+  .tech-item {
+    background-color: #f8f9fa;
+    border-radius: 8px;
+    padding: 20px;
+    border-left: 4px solid #3498db;
+  }
+  
+  .tech-item h3 {
+    margin-bottom: 10px;
+    color: #2c3e50;
+  }
+  
+  .tech-item ul {
+    list-style-type: disc;
+    padding-left: 20px;
+  }
+  
+  .tech-item li {
+    margin-bottom: 5px;
+  }
+  
+  ol {
+    padding-left: 20px;
+    margin: 15px 0;
+  }
+  
+  ol li {
+    margin-bottom: 10px;
+  }
+</style>

example/src/routes/api/query/+server.js

@@ -0,0 +1,68 @@
+import { json } from '@sveltejs/kit';
+
+// Replace this with your actual backend URL when deployed
+const PYTHON_BACKEND_URL = 'http://localhost:8000/api/generate';
+
+/**
+ * @type {import('@sveltejs/kit').RequestHandler}
+ */
+export async function POST({ request }) {
+  try {
+    const { message } = await request.json();
+    
+    if (!message?.trim()) {
+      return json({
+        error: 'No message provided'
+      }, { status: 400 });
+    }
+    
+    // Option 1: Forward to Python backend (if running separately)
+    try {
+      const response = await fetch(PYTHON_BACKEND_URL, {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+        },
+        body: JSON.stringify({ message }),
+      });
+      
+      if (!response.ok) {
+        throw new Error(`Backend responded with status: ${response.status}`);
+      }
+      
+      const data = await response.json();
+      return json(data);
+      
+    } catch (error) {
+      console.error('Error communicating with Python backend:', error);
+      
+      // Fallback to dummy data if backend is not available (for development purposes)
+      // Remove this in production and handle errors appropriately
+      return json({
+        response: "This is a fallback response. The Python backend appears to be offline. Please ensure your backend server is running.",
+        papers: [
+          {
+            title: "Sample Paper 1",
+            abstract: "This is a placeholder abstract for development purposes only.",
+            similarity: "Similarity Score: 0.000"
+          },
+          {
+            title: "Sample Paper 2",
+            abstract: "This is a placeholder abstract for development purposes only.",
+            similarity: "Similarity Score: 0.000"
+          },
+          {
+            title: "Sample Paper 3",
+            abstract: "This is a placeholder abstract for development purposes only.",
+            similarity: "Similarity Score: 0.000"
+          }
+        ]
+      });
+    }
+  } catch (error) {
+    console.error('Error processing request:', error);
+    return json({
+      error: 'Internal server error'
+    }, { status: 500 });
+  }
+}

example/src/routes/settings/+layout.svelte

@@ -1,73 +1,81 @@
-<script lang="ts">
-	/**
-	 * Developed by: Christopher S. Dunham
-	 * Contact: csdunham@ucsb.edu, csdunham@protonmail.com
-	 *
-	 * Copyright © Reagents of the University of California,
-	 * BioPACIFIC MIP, and their affiliates.
-	 *
-	 * This source code is licensed according to the LICENSE file
-	 * found in the root directory of this source tree and on the
-	 * project page on Github.
-	 *
-	 */
-	interface Props {
-		children?: import('svelte').Snippet;
-	}
-
-	let { children }: Props = $props();
+<script>
+  import '../app.css';
 </script>
 
-<section class="settings">
-	<section class="nav">
-		<a class="nav-item" href="/settings">Settings Home</a>
-		<a class="nav-item" href="/settings/ownership">Ownership</a>
-		<a class="nav-item" href="/settings/publishing">Publishing</a>
-	</section>
-
-	<section class="display">
-		{@render children?.()}
-	</section>
-</section>
-
-<style lang="scss">
-	.settings {
-		display: flex;
-		// gap: 10px;
-		// margin-left: 20px;
-		margin-top: 5px;
-	}
-
-	.nav {
-		margin-top: 10px;
-		height: 100%;
-		display: flex;
-		flex-direction: column;
-		width: 250px;
-		align-items: center;
-		border-right: 1px solid black;
-	}
-
-	.nav-item {
-		display: flex;
-		align-items: center;
-		justify-content: center;
-		height: 60px;
-		width: 100%;
-		padding: 20px;
-		text-decoration: none;
-		color: #003660;
-	}
-
-	.nav-item:hover {
-		background-color: #003660;
-		color: white;
-		font-weight: bold;
-	}
+<div class="app">
+  <header>
+    <div class="header-content">
+      <h1>Research Paper Assistant</h1>
+      <nav>
+        <a href="/">Home</a>
+        <a href="/about">About</a>
+      </nav>
+    </div>
+  </header>
+  
+  <main>
+    <slot />
+  </main>
+  
+  <footer>
+    <p>&copy; {new Date().getFullYear()} Research Paper RAG System - Built with Svelte</p>
+  </footer>
+</div>
 
-	.display {
-		display: flex;
-		justify-content: center;
-		width: 70%;
-	}
-</style>
+<style>
+  .app {
+    display: flex;
+    flex-direction: column;
+    min-height: 100vh;
+  }
+  
+  header {
+    background-color: #2c3e50;
+    color: white;
+    padding: 1rem 0;
+    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+  }
+  
+  .header-content {
+    max-width: 1200px;
+    margin: 0 auto;
+    padding: 0 20px;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+  }
+  
+  header h1 {
+    margin: 0;
+    font-size: 1.5rem;
+  }
+  
+  nav {
+    display: flex;
+    gap: 20px;
+  }
+  
+  nav a {
+    color: white;
+    text-decoration: none;
+    font-weight: 500;
+    padding: 5px;
+  }
+  
+  nav a:hover {
+    text-decoration: underline;
+  }
+  
+  main {
+    flex: 1;
+  }
+  
+  footer {
+    background-color: #f8f9fa;
+    padding: 1rem 0;
+    text-align: center;
+    font-size: 0.875rem;
+    color: #7f8c8d;
+    border-top: 1px solid #ecf0f1;
+  }
+</style>