add all files

.dockerignore

@@ -0,0 +1,62 @@
+# Git
+.git
+.gitignore
+
+# Python
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+env
+pip-log.txt
+pip-delete-this-directory.txt
+.tox
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.log
+.git
+.mypy_cache
+.pytest_cache
+.hypothesis
+
+# Virtual environments
+venv/
+env/
+ENV/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Project specific
+uploads/*
+!uploads/.gitkeep
+chroma_db/
+*.db
+*.sqlite
+*.sqlite3
+
+# Logs
+*.log
+logs/
+
+# Temporary files
+*.tmp
+*.temp 

Dockerfile

@@ -0,0 +1,38 @@
+# Use Python 3.11 slim image
+FROM python:3.11-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    gcc \
+    g++ \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first for better caching
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY . .
+
+# Create uploads directory
+RUN mkdir -p uploads
+
+# Expose port
+EXPOSE 7860
+
+# Set environment variables
+ENV FLASK_APP=app.py
+ENV FLASK_ENV=production
+ENV PORT=7860
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \
+    CMD curl -f http://localhost:7860/health || exit 1
+
+# Run the application
+CMD ["python", "app.py"] 

app.py

@@ -0,0 +1,900 @@
+import os
+import json
+import tempfile
+from datetime import datetime
+from flask import Flask, render_template, request, jsonify, session, redirect, url_for
+import google.generativeai as genai
+from sentence_transformers import SentenceTransformer
+# Removed ChromaDB and added Qdrant
+from qdrant_client import QdrantClient
+from qdrant_client.models import VectorParams, Distance, Filter, FieldCondition, MatchValue, PointStruct, SearchParams
+# LangChain splitter
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import arxiv
+import PyPDF2
+from docx import Document
+import requests
+from werkzeug.utils import secure_filename
+from dotenv import load_dotenv
+import uuid
+import re
+from bs4 import BeautifulSoup
+import logging
+import numpy as np
+
+# Load environment variables
+load_dotenv()
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+app = Flask(__name__)
+app.secret_key = os.getenv('SECRET_KEY', 'research-radar-secret-key-2024')
+
+# Configuration
+UPLOAD_FOLDER = 'uploads'
+ALLOWED_EXTENSIONS = {'txt', 'pdf', 'docx'}
+MAX_CONTENT_LENGTH = 16 * 1024 * 1024  # 16MB max file size
+
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+app.config['MAX_CONTENT_LENGTH'] = MAX_CONTENT_LENGTH
+
+# Ensure directories exist
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+# Initialize models and services
+try:
+    # Configure Gemini API
+    gemini_api_key = os.getenv('GEMINI_API_KEY')
+    if gemini_api_key:
+        genai.configure(api_key=gemini_api_key)
+        gemini_model = genai.GenerativeModel('gemini-2.5-flash')
+        logger.info("✅ Gemini API initialized successfully")
+    else:
+        gemini_model = None
+        logger.warning("⚠️ Gemini API key not found. AI features will be limited.")
+    
+    # Initialize sentence transformer for embeddings (free)
+    embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
+    logger.info("✅ Sentence transformer model loaded")
+
+    # Determine vector size dynamically
+    try:
+        _probe_vec = embedding_model.encode(["probe text"])
+        VECTOR_SIZE = int(_probe_vec.shape[-1]) if hasattr(_probe_vec, 'shape') else len(_probe_vec[0])
+    except Exception:
+        VECTOR_SIZE = 384  # fallback for all-MiniLM-L6-v2
+
+    # Initialize Qdrant client
+    qdrant_url = os.getenv('QDRANT_URL')
+    qdrant_api_key = os.getenv('QDRANT_API_KEY')
+    qdrant_client = QdrantClient(url=qdrant_url, api_key=qdrant_api_key, timeout=120)
+    
+    logger.info("✅ Qdrant client initialized")
+
+    # Ensure default collection exists
+    def ensure_qdrant_collection(collection_name: str, vector_size: int) -> None:
+        try:
+            qdrant_client.get_collection(collection_name)
+        except Exception:
+            qdrant_client.recreate_collection(
+                collection_name=collection_name,
+                vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE)
+            )
+            logger.info(f"✅ Created Qdrant collection: {collection_name}")
+        # Ensure payload index for document_id exists
+        try:
+            qdrant_client.create_payload_index(
+                collection_name=collection_name,
+                field_name="document_id",
+                field_schema="keyword"
+            )
+            logger.info("✅ Ensured payload index for 'document_id'")
+        except Exception:
+            # Likely already exists
+            pass
+
+    ensure_qdrant_collection('research_papers', VECTOR_SIZE)
+
+except Exception as e:
+    logger.error(f"❌ Initialization error: {e}")
+    embedding_model = None
+    gemini_model = None
+    qdrant_client = None
+    VECTOR_SIZE = None
+
+
+def allowed_file(filename):
+    """Check if file extension is allowed"""
+    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+
+def extract_text_from_pdf(file_path):
+    """Extract text from PDF file"""
+    try:
+        with open(file_path, 'rb') as file:
+            pdf_reader = PyPDF2.PdfReader(file)
+            text = ""
+            for page in pdf_reader.pages:
+                text += page.extract_text() + "\n"
+            return text
+    except Exception as e:
+        print(f"PDF extraction error: {e}")
+        return ""
+
+
+def extract_text_from_docx(file_path):
+    """Extract text from DOCX file"""
+    try:
+        doc = Document(file_path)
+        text = ""
+        for paragraph in doc.paragraphs:
+            text += paragraph.text + "\n"
+        return text
+    except Exception as e:
+        print(f"DOCX extraction error: {e}")
+        return ""
+
+
+def extract_text_from_txt(file_path):
+    """Extract text from TXT file"""
+    try:
+        with open(file_path, 'r', encoding='utf-8') as file:
+            return file.read()
+    except Exception as e:
+        print(f"TXT extraction error: {e}")
+        return ""
+
+
+def process_document(file_path, filename):
+    """Process uploaded document and extract text"""
+    file_extension = filename.rsplit('.', 1)[1].lower()
+    
+    if file_extension == 'pdf':
+        return extract_text_from_pdf(file_path)
+    elif file_extension == 'docx':
+        return extract_text_from_docx(file_path)
+    elif file_extension == 'txt':
+        return extract_text_from_txt(file_path)
+    else:
+        return ""
+
+
+def search_arxiv_papers(query, max_results=10):
+    """Search arXiv papers"""
+    try:
+        client = arxiv.Client()
+        search = arxiv.Search(
+            query=query,
+            max_results=max_results,
+            sort_by=arxiv.SortCriterion.Relevance
+        )
+        
+        papers = []
+        for result in client.results(search):
+            paper = {
+                'title': result.title,
+                'authors': [author.name for author in result.authors],
+                'summary': result.summary,
+                'url': result.entry_id,
+                'pdf_url': result.pdf_url,
+                'published': result.published.strftime('%Y-%m-%d'),
+                'category': result.primary_category
+            }
+            papers.append(paper)
+        
+        return papers
+    except Exception as e:
+        print(f"arXiv search error: {e}")
+        return []
+
+
+def generate_summary(text, max_length=500):
+    """Generate summary using Gemini API"""
+    try:
+        if not gemini_model:
+            return "Summary generation unavailable - API not configured"
+        
+        prompt = f"""
+        Please provide a comprehensive summary of this research paper/document in approximately {max_length} words. 
+        Focus on:
+        1. Main research question/objective
+        2. Key methodology
+        3. Important findings
+        4. Conclusions and implications
+        
+        Text to summarize:
+        {text[:80000]}
+        """
+        
+        response = gemini_model.generate_content(prompt)
+        return response.text
+    except Exception as e:
+        logger.error(f"Summary generation error: {e}")
+        return "Error generating summary. Please try again."
+
+# Text chunking using LangChain
+
+def chunk_text(text: str, chunk_size: int = 1000, chunk_overlap: int = 200):
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap,
+        separators=["\n\n", "\n", " ", ""]
+    )
+    return splitter.split_text(text)
+
+# Qdrant helpers
+
+def ensure_qdrant_collection(collection_name: str, vector_size: int) -> None:
+    """Create Qdrant collection if it doesn't exist"""
+    if not qdrant_client:
+        return
+    try:
+        qdrant_client.get_collection(collection_name)
+    except Exception:
+        qdrant_client.recreate_collection(
+            collection_name=collection_name,
+            vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE)
+        )
+    # Ensure payload index for document_id exists for efficient filtering/scrolling
+    try:
+        qdrant_client.create_payload_index(
+            collection_name=collection_name,
+            field_name="document_id",
+            field_schema="keyword"
+        )
+    except Exception:
+        pass
+
+
+def add_document_to_vector_db(text, metadata, doc_id, collection_name="research_papers"):
+    """Add chunked document vectors to Qdrant for chat functionality"""
+    try:
+        if not embedding_model or not qdrant_client or not VECTOR_SIZE:
+            return False
+        
+        ensure_qdrant_collection(collection_name, VECTOR_SIZE)
+        
+        # Split text using recursive text splitter
+        chunks = chunk_text(text, chunk_size=1200, chunk_overlap=250)
+        if not chunks:
+            return False
+        
+        embeddings = embedding_model.encode(chunks)
+        vectors = embeddings.tolist() if hasattr(embeddings, 'tolist') else embeddings
+        
+        points = []
+        for i, (chunk, vector) in enumerate(zip(chunks, vectors)):
+            payload = dict(metadata or {})
+            payload.update({
+                'document_id': doc_id,
+                'chunk_index': i,
+                'total_chunks': len(chunks),
+                'content': chunk,
+            })
+            points.append(
+                PointStruct(
+                    id=str(uuid.uuid4()),
+                    vector=vector,
+                    payload=payload
+                )
+            )
+        
+        qdrant_client.upsert(collection_name=collection_name, points=points, wait=True)
+        return True
+    except Exception as e:
+        print(f"Vector DB error: {e}")
+        return False
+
+
+def query_vector_db(query, doc_id, collection_name="research_papers", n_results=3):
+    """Query Qdrant for similar chunks for the given document_id"""
+    try:
+        if not embedding_model or not qdrant_client or not VECTOR_SIZE:
+            return []
+        
+        ensure_qdrant_collection(collection_name, VECTOR_SIZE)
+        
+        query_embedding = embedding_model.encode([query])
+        query_vector = query_embedding[0].tolist() if hasattr(query_embedding, 'tolist') else list(query_embedding[0])
+        
+        flt = Filter(must=[FieldCondition(key="document_id", match=MatchValue(value=doc_id))])
+        results = qdrant_client.search(
+            collection_name=collection_name,
+            query_vector=query_vector,
+            limit=n_results,
+            query_filter=flt,
+            with_payload=True,
+            with_vectors=False
+        )
+        
+        documents = []
+        for r in results or []:
+            payload = getattr(r, 'payload', None) or {}
+            documents.append(payload.get('content', ''))
+        return {'documents': [documents]}
+    except Exception as e:
+        print(f"Vector DB query error: {e}")
+        return []
+
+
+def get_all_chunks_for_document(doc_id: str, collection_name: str = "research_papers"):
+    """Retrieve all chunks for a document from Qdrant, ordered by chunk_index"""
+    try:
+        all_points = []
+        next_offset = None
+        flt = Filter(must=[FieldCondition(key="document_id", match=MatchValue(value=doc_id))])
+        while True:
+            points, next_offset = qdrant_client.scroll(
+                collection_name=collection_name,
+                scroll_filter=flt,
+                limit=500,
+                offset=next_offset,
+                with_payload=True,
+                with_vectors=False
+            )
+            all_points.extend(points)
+            if not next_offset:
+                break
+        # Order by chunk_index
+        all_points.sort(key=lambda p: p.payload.get('chunk_index', 0))
+        return [p.payload.get('content', '') for p in all_points]
+    except Exception as e:
+        print(f"Qdrant scroll error: {e}")
+        return []
+
+
+def get_all_documents(collection_name: str = "research_papers"):
+    """Get all unique documents from Qdrant with their metadata"""
+    try:
+        if not qdrant_client:
+            return []
+        
+        # Get all points to extract unique documents
+        all_points = []
+        next_offset = None
+        while True:
+            points, next_offset = qdrant_client.scroll(
+                collection_name=collection_name,
+                limit=1000,
+                offset=next_offset,
+                with_payload=True,
+                with_vectors=False
+            )
+            all_points.extend(points)
+            if not next_offset:
+                break
+        
+        # Group by document_id and extract metadata
+        documents = {}
+        for point in all_points:
+            payload = point.payload or {}
+            doc_id = payload.get('document_id')
+            if not doc_id:
+                continue
+                
+            if doc_id not in documents:
+                # Create document metadata from first chunk
+                doc_type = payload.get('type', 'document')
+                
+                # Generate proper title based on type
+                title = payload.get('title', 'Untitled Document')
+                if doc_type == 'arxiv_paper' and payload.get('pdf_url'):
+                    # Extract arXiv ID from URL for better title
+                    pdf_url = payload.get('pdf_url', '')
+                    if 'arxiv.org/pdf/' in pdf_url:
+                        arxiv_id = pdf_url.split('/')[-1].replace('.pdf', '')
+                        title = f"arXiv:{arxiv_id}"
+                    elif 'arxiv.org/abs/' in pdf_url:
+                        arxiv_id = pdf_url.split('/')[-1]
+                        title = f"arXiv:{arxiv_id}"
+                elif doc_type == 'uploaded_document' and payload.get('filename'):
+                    title = payload.get('filename')
+                
+                documents[doc_id] = {
+                    'document_id': doc_id,
+                    'title': title,
+                    'authors': payload.get('authors', ['Unknown']),
+                    'published': payload.get('published', 'Unknown Date'),
+                    'category': payload.get('category', 'Research'),
+                    'filename': payload.get('filename', ''),
+                    'pdf_url': payload.get('pdf_url', ''),
+                    'type': doc_type,
+                    'upload_date': payload.get('upload_date', ''),
+                    'total_chunks': payload.get('total_chunks', 0),
+                    'word_count': payload.get('word_count', 0)
+                }
+        
+        # Convert to list and sort by upload date (newest first)
+        doc_list = list(documents.values())
+        doc_list.sort(key=lambda x: x.get('upload_date', ''), reverse=True)
+        
+        return doc_list
+    except Exception as e:
+        print(f"Error getting documents: {e}")
+        return []
+
+
+def get_document_metadata(doc_id: str, collection_name: str = "research_papers"):
+    """Get metadata for a specific document"""
+    try:
+        if not qdrant_client:
+            return None
+        
+        # Get first chunk to extract metadata
+        flt = Filter(must=[FieldCondition(key="document_id", match=MatchValue(value=doc_id))])
+        results = qdrant_client.scroll(
+            collection_name=collection_name,
+            scroll_filter=flt,
+            limit=1,
+            with_payload=True,
+            with_vectors=False
+        )
+        
+        if results and results[0]:
+            payload = results[0][0].payload or {}
+            return {
+                'document_id': doc_id,
+                'title': payload.get('title', 'Untitled Document'),
+                'authors': payload.get('authors', ['Unknown']),
+                'published': payload.get('published', 'Unknown Date'),
+                'category': payload.get('category', 'Research'),
+                'filename': payload.get('filename', ''),
+                'pdf_url': payload.get('pdf_url', ''),
+                'type': payload.get('type', 'document'),
+                'upload_date': payload.get('upload_date', ''),
+                'total_chunks': payload.get('total_chunks', 0),
+                'word_count': payload.get('word_count', 0)
+            }
+        return None
+    except Exception as e:
+        print(f"Error getting document metadata: {e}")
+        return None
+
+# Paper ingestion helpers
+
+def resolve_pdf_url(url_or_pdf: str) -> str:
+    if not url_or_pdf:
+        return ''
+    if 'arxiv.org/pdf/' in url_or_pdf and url_or_pdf.endswith('.pdf'):
+        return url_or_pdf
+    # convert arXiv abs to pdf
+    m = re.search(r"arxiv\.org/(abs|pdf)/([\w\.-]+)", url_or_pdf)
+    if m:
+        arxiv_id = m.group(2)
+        if not arxiv_id.endswith('.pdf'):
+            return f"https://arxiv.org/pdf/{arxiv_id}.pdf"
+        return f"https://arxiv.org/pdf/{arxiv_id}"
+    return url_or_pdf
+
+
+def download_pdf_to_temp(pdf_url: str) -> str:
+    r = requests.get(pdf_url, stream=True, timeout=30)
+    r.raise_for_status()
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as tmp:
+        for chunk in r.iter_content(chunk_size=8192):
+            if chunk:
+                tmp.write(chunk)
+        return tmp.name
+
+
+def ingest_paper(pdf_url: str, paper_meta: dict = None) -> tuple:
+    """Download PDF, extract text, chunk, embed and store in Qdrant. Returns (doc_id, word_count)."""
+    pdf_url = resolve_pdf_url(pdf_url)
+    doc_id = str(uuid.uuid4())
+    tmp_path = None
+    try:
+        tmp_path = download_pdf_to_temp(pdf_url)
+        text_content = extract_text_from_pdf(tmp_path)
+        if not text_content.strip():
+            return None, 0
+        metadata = {
+            'source': 'arxiv',
+            'pdf_url': pdf_url,
+            'type': 'arxiv_paper'
+        }
+        if paper_meta:
+            metadata.update(paper_meta)
+        ok = add_document_to_vector_db(text_content, metadata, doc_id)
+        if not ok:
+            return None, 0
+        # set active document
+        session['active_document_id'] = doc_id
+        return doc_id, len(text_content.split())
+    finally:
+        if tmp_path and os.path.exists(tmp_path):
+            try:
+                os.remove(tmp_path)
+            except Exception:
+                pass
+
+
+def generate_summary_from_qdrant(doc_id: str, max_chars: int = 80000) -> str:
+    chunks = get_all_chunks_for_document(doc_id)
+    if not chunks:
+        return "No content available to summarize."
+    # Concatenate up to max_chars
+    full_text = ''
+    for chunk in chunks:
+        if len(full_text) + len(chunk) > max_chars:
+            break
+        full_text += (chunk + '\n')
+    return generate_summary(full_text)
+
+
+def generate_chat_response(question, context_docs):
+    """Generate chat response using Gemini with context"""
+    try:
+        if not gemini_model:
+            return "Chat functionality unavailable - API not configured"
+        
+        context = "\n\n".join(context_docs) if context_docs else ""
+        
+        prompt = f"""
+        You are a research assistant helping users understand academic papers. 
+        Answer the following question based on the provided context from research papers.
+        If the context doesn't contain relevant information, say so politely and suggest what information would be needed.
+        
+        Context from research papers:
+        {context}
+        
+        Question: {question}
+        
+        Please provide a clear, accurate, and helpful response.
+        """
+        
+        response = gemini_model.generate_content(prompt)
+        return response.text
+    except Exception as e:
+        logger.error(f"Chat response error: {e}")
+        return "Error generating response. Please try again."
+
+# Routes
+@app.route('/')
+def index():
+    """Main page"""
+    return render_template('index.html')
+
+@app.route('/search', methods=['POST'])
+def search_papers():
+    """Search arXiv papers"""
+    try:
+        data = request.get_json()
+        query = data.get('query', '').strip()
+        
+        if not query:
+            return jsonify({'error': 'Query is required'}), 400
+        
+        papers = search_arxiv_papers(query, max_results=10)
+        return jsonify({'papers': papers})
+    
+    except Exception as e:
+        return jsonify({'error': f'Search failed: {str(e)}'}), 500
+
+@app.route('/ingest-paper', methods=['POST'])
+def ingest_paper_endpoint():
+    """Ingest a paper PDF by URL: download, chunk, embed, store in Qdrant."""
+    try:
+        data = request.get_json()
+        pdf_url = data.get('pdf_url') or data.get('url')
+        title = data.get('title')
+        authors = data.get('authors')
+        published = data.get('published')
+        if not pdf_url:
+            return jsonify({'error': 'pdf_url is required'}), 400
+        doc_id, word_count = ingest_paper(pdf_url, paper_meta={'title': title, 'authors': authors, 'published': published})
+        if not doc_id:
+            return jsonify({'error': 'Failed to ingest paper'}), 500
+        return jsonify({'success': True, 'doc_id': doc_id, 'word_count': word_count})
+    except Exception as e:
+        logger.error(f"Ingestion failed: {e}", exc_info=True)
+        return jsonify({'error': f'Ingestion failed: {str(e)}'}), 500
+
+@app.route('/upload', methods=['POST'])
+def upload_file():
+    """Handle file upload"""
+    try:
+        if 'file' not in request.files:
+            return jsonify({'error': 'No file selected'}), 400
+        
+        file = request.files['file']
+        if file.filename == '':
+            return jsonify({'error': 'No file selected'}), 400
+        
+        if file and allowed_file(file.filename):
+            filename = secure_filename(file.filename)
+            
+            # Generate a unique ID for this document session
+            doc_id = str(uuid.uuid4())
+            
+            # Use a temporary file to avoid cluttering the upload folder
+            with tempfile.NamedTemporaryFile(delete=False, suffix=f"_{filename}") as tmp_file:
+                file.save(tmp_file.name)
+                tmp_file_path = tmp_file.name
+
+            # Extract text from document
+            text_content = process_document(tmp_file_path, filename)
+            
+            # Clean up temporary file immediately
+            os.remove(tmp_file_path)
+
+            if not text_content.strip():
+                return jsonify({'error': 'Could not extract text from file'}), 400
+            
+            # Generate summary
+            summary = generate_summary(text_content)
+            
+            # Add to vector database for chat
+            metadata = {
+                'filename': file.filename,
+                'upload_date': datetime.now().isoformat(),
+                'type': 'uploaded_document'
+            }
+            add_document_to_vector_db(text_content, metadata, doc_id)
+            
+            # Store the active document ID in the session
+            session['active_document_id'] = doc_id
+            
+            return jsonify({
+                'success': True,
+                'filename': file.filename,
+                'summary': summary,
+                'word_count': len(text_content.split()),
+                'doc_id': doc_id # Send doc_id to frontend
+            })
+        
+        return jsonify({'error': 'Invalid file type'}), 400
+    
+    except Exception as e:
+        logger.error(f"Upload failed: {e}", exc_info=True)
+        return jsonify({'error': f'Upload failed: {str(e)}'}), 500
+
+@app.route('/summarize-paper', methods=['POST'])
+def summarize_paper():
+    """Summarize paper: if doc_id provided, summarize from Qdrant; else ingest then summarize."""
+    try:
+        data = request.get_json()
+        doc_id = data.get('doc_id')
+        paper_url = data.get('url', '').strip()
+        pdf_url = data.get('pdf_url')
+        
+        if not doc_id and not (paper_url or pdf_url):
+            return jsonify({'error': 'doc_id or url/pdf_url is required'}), 400
+        
+        # If doc_id not provided, ingest first
+        paper_data = None
+        if not doc_id:
+            # If only abs URL provided, try resolve via arxiv client for metadata
+            try:
+                # Extract arXiv ID from URL
+                arxiv_id = None
+                if paper_url:
+                    arxiv_id = paper_url.split('/')[-1].replace('.pdf', '')
+                if arxiv_id:
+                    client = arxiv.Client()
+                    search = arxiv.Search(id_list=[arxiv_id])
+                    for result in client.results(search):
+                        paper_data = {
+                            'title': result.title,
+                            'authors': [author.name for author in result.authors],
+                            'summary': result.summary,
+                            'url': result.entry_id,
+                            'pdf_url': result.pdf_url,
+                            'published': result.published.strftime('%Y-%m-%d')
+                        }
+                        break
+            except Exception:
+                paper_data = None
+            ingest_pdf = pdf_url or (paper_data['pdf_url'] if paper_data and paper_data.get('pdf_url') else resolve_pdf_url(paper_url))
+            new_doc_id, _ = ingest_paper(ingest_pdf, paper_meta=paper_data or {})
+            if not new_doc_id:
+                return jsonify({'error': 'Failed to ingest paper'}), 500
+            doc_id = new_doc_id
+            session['active_document_id'] = doc_id
+        
+        # Summarize from Qdrant chunks
+        summary = generate_summary_from_qdrant(doc_id)
+        
+        return jsonify({
+            'success': True,
+            'summary': summary,
+            'doc_id': doc_id,
+            'paper': paper_data
+        })
+    except Exception as e:
+        return jsonify({'error': f'Request failed: {str(e)}'}), 500
+
+@app.route('/chat', methods=['POST'])
+def chat():
+    """Handle chat queries for the active document"""
+    try:
+        data = request.get_json()
+        # Accept both 'message' and 'question' for backward compatibility
+        question = data.get('message', data.get('question', '')).strip()
+        doc_id = session.get('active_document_id')
+        
+        if not question:
+            return jsonify({'error': 'Message is required'}), 400
+        
+        # If no active document, provide general help
+        if not doc_id:
+            if not gemini_model:
+                return jsonify({'error': 'AI service is not available. Please check your API configuration.'}), 500
+            
+            # Generate a general response without document context
+            try:
+                prompt = f"""
+                You are a helpful AI research assistant for Research Radar. The user asked: "{question}"
+                
+                Since no document is currently loaded, provide a helpful response about:
+                1. How to use Research Radar (search papers, upload documents, chat features)
+                2. General research guidance if the question is research-related
+                3. Suggest they upload a document or search for papers to get more specific help
+                
+                Keep your response friendly and informative.
+                """
+                
+                response = gemini_model.generate_content(prompt)
+                return jsonify({
+                    'success': True,
+                    'response': response.text,
+                    'context_found': False,
+                    'no_document': True
+                })
+            except Exception as e:
+                return jsonify({
+                    'success': True,
+                    'response': "Hello! I'm your AI research assistant. To get started, please upload a document or search for papers using the navigation above. Then I can help you analyze content, answer questions, and provide insights about your research materials.",
+                    'context_found': False,
+                    'no_document': True
+                })
+        
+        # Query vector database for relevant context from the active document
+        search_results = query_vector_db(question, doc_id)
+        
+        context_docs = []
+        if search_results and isinstance(search_results, dict) and 'documents' in search_results:
+            context_docs = search_results['documents'][0]
+        
+        # Generate response
+        response = generate_chat_response(question, context_docs)
+        
+        return jsonify({
+            'success': True,
+            'response': response,
+            'context_found': len(context_docs) > 0
+        })
+    
+    except Exception as e:
+        return jsonify({'error': f'Chat failed: {str(e)}'}), 500
+
+@app.route('/documents', methods=['GET'])
+def get_documents():
+    """Get all documents from the vector database"""
+    try:
+        documents = get_all_documents()
+        return jsonify({'success': True, 'documents': documents})
+    except Exception as e:
+        return jsonify({'error': f'Failed to get documents: {str(e)}'}), 500
+
+
+@app.route('/documents/<doc_id>', methods=['GET'])
+def get_document(doc_id):
+    """Get a specific document's metadata"""
+    try:
+        metadata = get_document_metadata(doc_id)
+        if not metadata:
+            return jsonify({'error': 'Document not found'}), 404
+        return jsonify({'success': True, 'document': metadata})
+    except Exception as e:
+        return jsonify({'error': f'Failed to get document: {str(e)}'}), 500
+
+
+@app.route('/documents/<doc_id>/summary', methods=['GET'])
+def get_document_summary(doc_id):
+    """Get summary for a specific document"""
+    try:
+        summary = generate_summary_from_qdrant(doc_id)
+        metadata = get_document_metadata(doc_id)
+        if not metadata:
+            return jsonify({'error': 'Document not found'}), 404
+        return jsonify({
+            'success': True, 
+            'summary': summary, 
+            'document': metadata
+        })
+    except Exception as e:
+        return jsonify({'error': f'Failed to get summary: {str(e)}'}), 500
+
+
+@app.route('/documents/<doc_id>/activate', methods=['POST'])
+def activate_document(doc_id):
+    """Set a document as the active document for chat"""
+    try:
+        metadata = get_document_metadata(doc_id)
+        if not metadata:
+            return jsonify({'error': 'Document not found'}), 404
+        
+        session['active_document_id'] = doc_id
+        return jsonify({
+            'success': True, 
+            'message': 'Document activated',
+            'document': metadata
+        })
+    except Exception as e:
+        return jsonify({'error': f'Failed to activate document: {str(e)}'}), 500
+
+
+@app.route('/documents/<doc_id>', methods=['DELETE'])
+def delete_document(doc_id):
+    """Delete a document from Qdrant"""
+    try:
+        if not qdrant_client:
+            return jsonify({'error': 'Vector database not available'}), 500
+        
+        # Delete all points for this document
+        flt = Filter(must=[FieldCondition(key="document_id", match=MatchValue(value=doc_id))])
+        qdrant_client.delete(
+            collection_name="research_papers",
+            points_selector=flt
+        )
+        
+        return jsonify({
+            'success': True, 
+            'message': 'Document deleted successfully'
+        })
+    except Exception as e:
+        return jsonify({'error': f'Failed to delete document: {str(e)}'}), 500
+
+
+@app.route('/documents', methods=['DELETE'])
+def clear_all_documents():
+    """Clear all documents from Qdrant"""
+    try:
+        if not qdrant_client:
+            return jsonify({'error': 'Vector database not available'}), 500
+        
+        # Delete all points
+        qdrant_client.delete(
+            collection_name="research_papers",
+            points_selector=None
+        )
+        
+        return jsonify({
+            'success': True, 
+            'message': 'All documents cleared successfully'
+        })
+    except Exception as e:
+        return jsonify({'error': f'Failed to clear documents: {str(e)}'}), 500
+
+
+@app.route('/clear-session', methods=['POST'])
+def clear_session():
+    """Clear the active document from the session"""
+    session.pop('active_document_id', None)
+    return jsonify({'success': True, 'message': 'Session cleared.'})
+
+@app.route('/health')
+def health_check():
+    """Health check endpoint"""
+    return jsonify({
+        'status': 'healthy',
+        'gemini_available': gemini_model is not None,
+        'embeddings_available': embedding_model is not None,
+        'vector_db_available': qdrant_client is not None
+    })
+
+if __name__ == '__main__':
+    print("🚀 Research Radar - Starting Flask Application...")
+    print("📚 Features: arXiv search, document upload, AI summaries, chat functionality")
+    print("🔑 Make sure to set GEMINI_API_KEY in your .env file")
+    print("🗄  Using Qdrant as Vector DB. Ensure Qdrant is reachable via QDRANT_URL")
+    
+    # Get port from environment variable (for Hugging Face Spaces)
+    port = int(os.environ.get('PORT', 5000))
+    debug = os.environ.get('FLASK_ENV') == 'development'
+    
+    print(f"🌐 Access the app at: http://localhost:{port}")
+    
+    app.run(debug=debug, host='0.0.0.0', port=port) 

config.py

@@ -0,0 +1,53 @@
+import os
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+class Config:
+    """Base configuration class"""
+    SECRET_KEY = os.getenv('SECRET_KEY', 'research-radar-secret-key-2024')
+    GEMINI_API_KEY = os.getenv('GEMINI_API_KEY')
+    
+    # Upload settings
+    UPLOAD_FOLDER = 'uploads'
+    MAX_CONTENT_LENGTH = 16 * 1024 * 1024  # 16MB
+    ALLOWED_EXTENSIONS = {'txt', 'pdf', 'docx'}
+    
+    # ChromaDB settings
+    CHROMA_PERSIST_DIRECTORY = 'chroma_db'
+    
+    # Model settings
+    EMBEDDING_MODEL = 'all-MiniLM-L6-v2'  # Free sentence transformer model
+    GEMINI_MODEL = 'gemini-1.5-flash-latest'
+    
+    # ArXiv settings
+    ARXIV_MAX_RESULTS = 10
+    
+    @staticmethod
+    def init_app(app):
+        """Initialize application with config"""
+        pass
+
+class DevelopmentConfig(Config):
+    """Development configuration"""
+    DEBUG = True
+    FLASK_ENV = 'development'
+
+class ProductionConfig(Config):
+    """Production configuration"""
+    DEBUG = False
+    FLASK_ENV = 'production'
+
+class TestingConfig(Config):
+    """Testing configuration"""
+    TESTING = True
+    WTF_CSRF_ENABLED = False
+
+# Configuration dictionary
+config = {
+    'development': DevelopmentConfig,
+    'production': ProductionConfig,
+    'testing': TestingConfig,
+    'default': DevelopmentConfig
+} 

docker-compose.yml

@@ -0,0 +1,24 @@
+version: '3.8'
+
+services:
+  research-radar:
+    build: .
+    ports:
+      - "7860:7860"
+    environment:
+      - FLASK_APP=app.py
+      - FLASK_ENV=development
+      - PORT=7860
+      - GEMINI_API_KEY=${GEMINI_API_KEY}
+      - QDRANT_URL=${QDRANT_URL}
+      - QDRANT_API_KEY=${QDRANT_API_KEY}
+      - SECRET_KEY=${SECRET_KEY:-research-radar-secret-key-2024}
+    volumes:
+      - ./uploads:/app/uploads
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:7860/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 40s 

requirements.txt

@@ -0,0 +1,15 @@
+Flask
+google-generativeai
+sentence-transformers
+qdrant-client
+langchain
+arxiv
+PyPDF2
+python-docx
+requests
+python-dotenv
+Werkzeug
+numpy
+pandas
+beautifulsoup4
+lxml 

templates/index.html

@@ -0,0 +1,1013 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Research Radar - AI-Powered Paper Analysis</title>
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/style.css') }}">
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css" rel="stylesheet">
+    <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800&display=swap" rel="stylesheet">
+    <meta name="description" content="AI-powered research paper analysis tool for searching, uploading, and chatting with academic documents">
+</head>
+<body>
+    <!-- Landing Page -->
+    <div id="landingPage" class="page active">
+        <!-- Navigation for Landing -->
+        <nav class="navbar landing-nav">
+        <div class="nav-container">
+            <div class="nav-brand">
+                    <div class="brand-icon">
+                        <i class="fas fa-brain"></i>
+                    </div>
+                    <div class="brand-text">
+                        <span class="brand-name">Research Radar</span>
+                        <span class="brand-tagline">AI Research Assistant</span>
+                    </div>
+            </div>
+                <button class="mobile-nav-toggle" aria-label="Toggle navigation">
+                    <i class="fas fa-bars"></i>
+                </button>
+                <div class="landing-nav-links">
+                    <a href="#features" class="nav-link">
+                        <i class="fas fa-star"></i>
+                        <span>Features</span>
+                    </a>
+                    <a href="#about" class="nav-link">
+                        <i class="fas fa-info-circle"></i>
+                        <span>About</span>
+                    </a>
+                    <button class="nav-cta-btn" onclick="navigateToApp()">
+                        <i class="fas fa-rocket"></i>
+                        <span>Get Started</span>
+                    </button>
+            </div>
+        </div>
+    </nav>
+
+        <!-- Hero Section -->
+        <section class="hero">
+            <div class="hero-background">
+                <div class="floating-shapes">
+                    <div class="shape shape-1"></div>
+                    <div class="shape shape-2"></div>
+                    <div class="shape shape-3"></div>
+                </div>
+            </div>
+            <div class="hero-content">
+                <div class="hero-badge">
+                    <i class="fas fa-sparkles"></i>
+                    <span>Powered by AI</span>
+                </div>
+                <h1 class="hero-title">
+                    <span class="gradient-text">Research Radar</span>
+                </h1>
+                <p class="hero-subtitle">
+                    Discover, analyze, and understand research papers with our AI-powered assistant. 
+                    Search arXiv, upload documents, and get intelligent insights instantly.
+                </p>
+                <div class="hero-cta">
+                    <button class="cta-button primary" onclick="navigateToApp('search')">
+                        <i class="fas fa-rocket"></i>
+                        Start Exploring
+                    </button>
+                    <button class="cta-button secondary" onclick="navigateToApp('upload')">
+                        <i class="fas fa-upload"></i>
+                        Upload Paper
+                    </button>
+                </div>
+                <div class="hero-features">
+                    <div class="feature-item" data-aos="fade-up" data-aos-delay="100">
+                        <div class="feature-icon">
+                        <i class="fas fa-search"></i>
+                        </div>
+                        <div class="feature-content">
+                            <h3>Smart Search</h3>
+                            <p>Search millions of papers from arXiv</p>
+                        </div>
+                    </div>
+                    <div class="feature-item" data-aos="fade-up" data-aos-delay="200">
+                        <div class="feature-icon">
+                        <i class="fas fa-file-upload"></i>
+                        </div>
+                        <div class="feature-content">
+                            <h3>Document Upload</h3>
+                            <p>Upload PDFs, DOCX, and TXT files</p>
+                        </div>
+                    </div>
+                    <div class="feature-item" data-aos="fade-up" data-aos-delay="300">
+                        <div class="feature-icon">
+                        <i class="fas fa-brain"></i>
+                        </div>
+                        <div class="feature-content">
+                            <h3>AI Summaries</h3>
+                            <p>Get intelligent paper summaries</p>
+                        </div>
+                    </div>
+                    <div class="feature-item" data-aos="fade-up" data-aos-delay="400">
+                        <div class="feature-icon">
+                        <i class="fas fa-robot"></i>
+                        </div>
+                        <div class="feature-content">
+                            <h3>Smart Chat</h3>
+                            <p>Ask questions about your documents</p>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </section>
+
+        <!-- Features Section -->
+        <section id="features" class="features-section">
+            <div class="features-container">
+                <div class="features-header">
+                    <h2>Why Choose Research Radar?</h2>
+                    <p>Powerful AI tools designed for researchers, students, and academics</p>
+                </div>
+                <div class="features-grid">
+                    <div class="feature-card">
+                        <div class="card-icon">
+                            <i class="fas fa-lightning-bolt"></i>
+                        </div>
+                        <h3>Lightning Fast</h3>
+                        <p>Get instant AI-powered summaries and insights from research papers in seconds</p>
+                    </div>
+                    <div class="feature-card">
+                        <div class="card-icon">
+                            <i class="fas fa-shield-alt"></i>
+                        </div>
+                        <h3>Secure & Private</h3>
+                        <p>Your documents are processed securely with enterprise-grade privacy protection</p>
+                    </div>
+                    <div class="feature-card">
+                        <div class="card-icon">
+                            <i class="fas fa-globe"></i>
+                        </div>
+                        <h3>Comprehensive</h3>
+                        <p>Access millions of papers from arXiv and upload your own documents for analysis</p>
+                    </div>
+                    <div class="feature-card">
+                        <div class="card-icon">
+                            <i class="fas fa-comments"></i>
+                        </div>
+                        <h3>Interactive Chat</h3>
+                        <p>Ask questions and get detailed explanations about any aspect of your papers</p>
+                    </div>
+                </div>
+            </div>
+        </section>
+
+        <!-- About Section -->
+        <section id="about" class="about-section">
+            <div class="about-container">
+                <div class="about-content">
+                    <h2>About Research Radar</h2>
+                    <p>Research Radar is an AI-powered research assistant that helps you discover, analyze, and understand academic papers more efficiently. Whether you're a student working on assignments, a researcher exploring new topics, or an academic keeping up with the latest developments in your field, Research Radar makes it easy to extract insights from complex research papers.</p>
+                    <div class="about-stats">
+                        <div class="stat-item">
+                            <div class="stat-number">2M+</div>
+                            <div class="stat-label">Papers Analyzed</div>
+                        </div>
+                        <div class="stat-item">
+                            <div class="stat-number">50K+</div>
+                            <div class="stat-label">Active Users</div>
+                        </div>
+                        <div class="stat-item">
+                            <div class="stat-number">99.9%</div>
+                            <div class="stat-label">Uptime</div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </section>
+
+        <!-- Footer -->
+        <footer class="landing-footer">
+            <div class="footer-container">
+                <div class="footer-content">
+                    <div class="footer-brand">
+                        <div class="brand-icon">
+                            <i class="fas fa-brain"></i>
+                        </div>
+                        <span class="brand-name">Research Radar</span>
+                    </div>
+                    <p>&copy; 2024 Research Radar. Empowering research with AI.</p>
+                </div>
+            </div>
+        </footer>
+    </div>
+
+    <!-- Application Page -->
+    <div id="appPage" class="page">
+        <!-- Navigation for App -->
+        <nav class="navbar app-nav">
+            <div class="nav-container">
+                <div class="nav-brand" onclick="navigateToLanding()">
+                    <div class="brand-icon">
+                        <i class="fas fa-brain"></i>
+                    </div>
+                    <div class="brand-text">
+                        <span class="brand-name">Research Radar</span>
+                        <span class="brand-tagline">AI Research Assistant</span>
+                    </div>
+                </div>
+                <button class="mobile-nav-toggle" aria-label="Toggle navigation">
+                    <i class="fas fa-bars"></i>
+                </button>
+                <div class="nav-links">
+                    <a href="#search" class="nav-link active" data-section="search">
+                        <i class="fas fa-search"></i> 
+                        <span>Search</span>
+                    </a>
+                    <a href="#upload" class="nav-link" data-section="upload">
+                        <i class="fas fa-upload"></i> 
+                        <span>Upload</span>
+                    </a>
+                    <a href="#mypapers" class="nav-link" data-section="mypapers">
+                        <i class="fas fa-folder-open"></i> 
+                        <span>My Papers</span>
+                    </a>
+                </div>
+                <!-- Status Indicator -->
+                <div class="status-indicator" id="statusIndicator">
+                    <div class="status-dot"></div>
+                    <span class="status-text">Ready</span>
+                </div>
+                <!-- Back to Landing -->
+                <button class="back-to-landing" onclick="navigateToLanding()" title="Back to Home">
+                    <i class="fas fa-home"></i>
+                </button>
+            </div>
+        </nav>
+
+        <!-- Main Container -->
+        <div class="main-container">
+            <!-- Search Section -->
+            <section id="search" class="section active">
+                <div class="search-hero">
+                    <div class="search-hero-content">
+                        <div class="search-title-area">
+                            <div class="search-icon-large">
+                                <i class="fas fa-search"></i>
+                            </div>
+                            <h1>Discover Research Papers</h1>
+                            <p>Search through millions of academic papers from arXiv and get AI-powered insights</p>
+                        </div>
+
+                        <!-- Enhanced Search Box -->
+                        <div class="search-box-enhanced">
+                            <div class="search-input-container">
+                                <div class="search-input-wrapper">
+                                    <i class="fas fa-search search-icon"></i>
+                                    <input type="text" id="searchInput" placeholder="What are you researching today?" autocomplete="off">
+                                    <button id="searchBtn" class="search-submit-btn">
+                                        <i class="fas fa-arrow-right"></i>
+                                        <span>Search</span>
+                                    </button>
+                                </div>
+                                <div class="search-actions">
+                                    <button class="voice-search-btn" title="Voice Search">
+                                        <i class="fas fa-microphone"></i>
+                                    </button>
+                                    <button class="advanced-search-btn" title="Advanced Search" onclick="toggleAdvancedSearch()">
+                                        <i class="fas fa-sliders-h"></i>
+                                    </button>
+                                </div>
+                            </div>
+
+                            <!-- Advanced Filters -->
+                            <div class="advanced-filters" id="advancedFilters">
+                                <div class="filters-row">
+                                    <div class="filter-group">
+                                        <label class="filter-label">
+                                            <i class="fas fa-calendar"></i>
+                                            Time Range
+                                        </label>
+                                        <select id="timeFilter" class="filter-select">
+                                            <option value="all">All Time</option>
+                                            <option value="2024">2024</option>
+                                            <option value="2023">2023</option>
+                                            <option value="2022">2022</option>
+                                            <option value="recent">Last 6 months</option>
+                                            <option value="month">Last month</option>
+                                        </select>
+                                    </div>
+                                    <div class="filter-group">
+                                        <label class="filter-label">
+                                            <i class="fas fa-tag"></i>
+                                            Category
+                                        </label>
+                                        <select id="categoryFilter" class="filter-select">
+                                            <option value="all">All Categories</option>
+                                            <option value="cs">Computer Science</option>
+                                            <option value="math">Mathematics</option>
+                                            <option value="physics">Physics</option>
+                                            <option value="stat">Statistics</option>
+                                            <option value="bio">Biology</option>
+                                            <option value="econ">Economics</option>
+                                        </select>
+                                    </div>
+                                    <div class="filter-group">
+                                        <label class="filter-label">
+                                            <i class="fas fa-sort"></i>
+                                            Sort By
+                                        </label>
+                                        <select id="sortFilter" class="filter-select">
+                                            <option value="relevance">Relevance</option>
+                                            <option value="date">Latest First</option>
+                                            <option value="citations">Most Cited</option>
+                                            <option value="title">Title A-Z</option>
+                                        </select>
+                                    </div>
+                                </div>
+                            </div>
+                        </div>
+
+                        <!-- Quick Search Categories -->
+                        <div class="quick-search-section">
+                            <h3>Popular Research Areas</h3>
+                            <div class="quick-search-grid">
+                                <button class="quick-search-card" data-query="machine learning">
+                                    <div class="card-icon">
+                                        <i class="fas fa-robot"></i>
+                                    </div>
+                                    <div class="card-content">
+                                        <h4>Machine Learning</h4>
+                                        <span>12,847 papers</span>
+                                    </div>
+                                </button>
+                                <button class="quick-search-card" data-query="artificial intelligence">
+                                    <div class="card-icon">
+                                        <i class="fas fa-brain"></i>
+                                    </div>
+                                    <div class="card-content">
+                                        <h4>Artificial Intelligence</h4>
+                                        <span>8,932 papers</span>
+                                    </div>
+                                </button>
+                                <button class="quick-search-card" data-query="quantum computing">
+                                    <div class="card-icon">
+                                        <i class="fas fa-atom"></i>
+                                    </div>
+                                    <div class="card-content">
+                                        <h4>Quantum Computing</h4>
+                                        <span>3,456 papers</span>
+                                    </div>
+                                </button>
+                                <button class="quick-search-card" data-query="deep learning">
+                                    <div class="card-icon">
+                                        <i class="fas fa-network-wired"></i>
+                                    </div>
+                                    <div class="card-content">
+                                        <h4>Deep Learning</h4>
+                                        <span>15,234 papers</span>
+                                    </div>
+                                </button>
+                                <button class="quick-search-card" data-query="computer vision">
+                                    <div class="card-icon">
+                                        <i class="fas fa-eye"></i>
+                                    </div>
+                                    <div class="card-content">
+                                        <h4>Computer Vision</h4>
+                                        <span>9,876 papers</span>
+                                    </div>
+                                </button>
+                                <button class="quick-search-card" data-query="natural language processing">
+                                    <div class="card-icon">
+                                        <i class="fas fa-comments"></i>
+                                    </div>
+                                    <div class="card-content">
+                                        <h4>NLP</h4>
+                                        <span>7,543 papers</span>
+                                    </div>
+                                </button>
+                            </div>
+                        </div>
+                    </div>
+            </div>
+
+                <!-- Search Tips -->
+                <div class="search-tips" id="searchTips">
+                    <div class="tips-header">
+                        <h3><i class="fas fa-lightbulb"></i> Search Tips</h3>
+                        <button class="tips-toggle" onclick="toggleSearchTips()">
+                            <i class="fas fa-chevron-down"></i>
+                        </button>
+                    </div>
+                    <div class="tips-content">
+                        <div class="tips-grid">
+                            <div class="tip-item">
+                                <div class="tip-icon">
+                                    <i class="fas fa-quotes-left"></i>
+                                </div>
+                                <div class="tip-content">
+                                    <h4>Use Quotes</h4>
+                                    <p>Search for exact phrases like <code>"neural networks"</code></p>
+                                </div>
+                            </div>
+                            <div class="tip-item">
+                                <div class="tip-icon">
+                                    <i class="fas fa-plus"></i>
+                                </div>
+                                <div class="tip-content">
+                                    <h4>Combine Terms</h4>
+                                    <p>Use AND, OR like <code>machine learning AND ethics</code></p>
+                                </div>
+                            </div>
+                            <div class="tip-item">
+                                <div class="tip-icon">
+                                    <i class="fas fa-user"></i>
+                                </div>
+                                <div class="tip-content">
+                                    <h4>Search Authors</h4>
+                                    <p>Find papers by author <code>author:smith</code></p>
+                                </div>
+                            </div>
+                            <div class="tip-item">
+                                <div class="tip-icon">
+                                    <i class="fas fa-asterisk"></i>
+                                </div>
+                                <div class="tip-content">
+                                    <h4>Use Wildcards</h4>
+                                    <p>Use * for variations like <code>neural*</code></p>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+
+                <!-- Recent Searches -->
+                <div class="recent-searches" id="recentSearches" style="display: none;">
+                    <div class="recent-header">
+                        <h3><i class="fas fa-history"></i> Recent Searches</h3>
+                        <button class="clear-history" onclick="clearSearchHistory()">
+                            <i class="fas fa-trash"></i> Clear
+                        </button>
+                    </div>
+                    <div class="recent-items" id="recentSearchItems">
+                        <!-- Dynamic content -->
+                    </div>
+                </div>
+
+                <!-- Search Results -->
+                <div id="searchResults" class="results-container">
+                    <div class="search-placeholder-enhanced">
+                        <div class="placeholder-animation">
+                            <div class="floating-papers">
+                                <div class="paper-icon">📄</div>
+                                <div class="paper-icon">📊</div>
+                                <div class="paper-icon">🔬</div>
+                            </div>
+                        </div>
+                        <h3>Ready to explore the world of research?</h3>
+                        <p>Start by typing a topic above or click on one of the popular research areas</p>
+                        <div class="placeholder-stats">
+                            <div class="stat">
+                                <strong>2.3M+</strong>
+                                <span>Papers Available</span>
+                            </div>
+                            <div class="stat">
+                                <strong>50+</strong>
+                                <span>Research Fields</span>
+                            </div>
+                            <div class="stat">
+                                <strong>24/7</strong>
+                                <span>Updated</span>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+
+                <!-- Search Suggestions Dropdown -->
+                <div class="search-suggestions-dropdown" id="searchSuggestions">
+                    <div class="suggestions-section">
+                        <h4>Suggestions</h4>
+                        <div class="suggestion-item" data-query="machine learning">
+                            <i class="fas fa-search"></i>
+                            <span>machine learning</span>
+                            <small>12.8k papers</small>
+                        </div>
+                        <div class="suggestion-item" data-query="neural networks">
+                            <i class="fas fa-search"></i>
+                            <span>neural networks</span>
+                            <small>8.3k papers</small>
+                        </div>
+                        <div class="suggestion-item" data-query="quantum computing">
+                            <i class="fas fa-search"></i>
+                            <span>quantum computing</span>
+                            <small>3.4k papers</small>
+                        </div>
+                    </div>
+                    <div class="suggestions-section">
+                        <h4>Trending Today</h4>
+                        <div class="suggestion-item trending" data-query="large language models">
+                            <i class="fas fa-fire"></i>
+                            <span>large language models</span>
+                            <small class="trending-badge">Hot</small>
+                        </div>
+                        <div class="suggestion-item trending" data-query="transformer architecture">
+                            <i class="fas fa-fire"></i>
+                            <span>transformer architecture</span>
+                            <small class="trending-badge">Hot</small>
+                        </div>
+                    </div>
+                </div>
+            </section>
+
+            <!-- Upload Section -->
+            <section id="upload" class="section">
+                <div class="upload-hero">
+                    <div class="upload-hero-content">
+                        <div class="upload-title-area">
+                            <div class="upload-icon-large">
+                                <i class="fas fa-cloud-upload-alt"></i>
+                            </div>
+                            <h1>Upload & Analyze Documents</h1>
+                            <p>Upload research papers, documents, or analyze from URLs to get AI-powered insights and summaries</p>
+                        </div>
+
+                        <!-- Enhanced Upload Methods -->
+                        <div class="upload-methods-enhanced">
+                            <!-- File Upload Zone -->
+                            <div class="upload-method-card primary-upload" id="uploadZone">
+                                <div class="upload-card-header">
+                                    <div class="upload-card-icon">
+                                        <i class="fas fa-file-upload"></i>
+                                    </div>
+                                    <div class="upload-card-title">
+                                        <h3>Upload Document</h3>
+                                        <p>Drag & drop or browse files</p>
+                                    </div>
+                                </div>
+                                
+                                <div class="upload-drop-zone">
+                                    <div class="drop-zone-content">
+                                        <div class="upload-animation">
+                                            <div class="upload-cloud">
+                                                <i class="fas fa-cloud-upload-alt"></i>
+                                            </div>
+                                            <div class="upload-arrow">↑</div>
+                                        </div>
+                                        <h4>Drop files here</h4>
+                                        <p>or click to browse</p>
+                                        
+                                        <div class="supported-formats">
+                                            <div class="format-badge pdf">
+                                                <i class="fas fa-file-pdf"></i>
+                                                <span>PDF</span>
+                                            </div>
+                                            <div class="format-badge docx">
+                                                <i class="fas fa-file-word"></i>
+                                                <span>DOCX</span>
+                                            </div>
+                                            <div class="format-badge txt">
+                                                <i class="fas fa-file-alt"></i>
+                                                <span>TXT</span>
+                                            </div>
+                                        </div>
+                                        
+                                        <div class="upload-specs">
+                                            <span><i class="fas fa-info-circle"></i> Max size: 16MB</span>
+                                            <span><i class="fas fa-shield-alt"></i> Secure upload</span>
+                                        </div>
+                                    </div>
+                                    <input type="file" id="fileInput" accept=".pdf,.txt,.docx" hidden>
+                                </div>
+                                
+                                <div class="upload-actions">
+                                    <button class="browse-btn" onclick="document.getElementById('fileInput').click()">
+                                        <i class="fas fa-folder-open"></i>
+                                        <span>Browse Files</span>
+                                    </button>
+                                </div>
+                            </div>
+
+                            <!-- URL Analysis -->
+                            <div class="upload-method-card secondary-upload">
+                                <div class="upload-card-header">
+                                    <div class="upload-card-icon">
+                                        <i class="fas fa-link"></i>
+                                    </div>
+                                    <div class="upload-card-title">
+                                        <h3>Analyze from URL</h3>
+                                        <p>Paste arXiv or research paper URLs</p>
+                                    </div>
+                                </div>
+
+                                <div class="url-input-enhanced">
+                                    <div class="url-input-wrapper">
+                                        <i class="fas fa-link url-icon"></i>
+                                        <input type="text" id="paperUrl" placeholder="https://arxiv.org/abs/2301.00001" autocomplete="off">
+                                        <button id="analyzeUrlBtn" class="analyze-btn">
+                                            <i class="fas fa-search"></i>
+                                            <span>Analyze</span>
+                                        </button>
+                                    </div>
+                                </div>
+
+                                <div class="url-suggestions">
+                                    <h4>Popular Sources</h4>
+                                    <div class="source-buttons">
+                                        <button class="source-btn" data-source="arxiv">
+                                            <i class="fas fa-graduation-cap"></i>
+                                            <span>arXiv</span>
+                                        </button>
+                                        <button class="source-btn" data-source="pubmed">
+                                            <i class="fas fa-microscope"></i>
+                                            <span>PubMed</span>
+                                        </button>
+                                        <button class="source-btn" data-source="ieee">
+                                            <i class="fas fa-microchip"></i>
+                                            <span>IEEE</span>
+                                        </button>
+                                        <button class="source-btn" data-source="acm">
+                                            <i class="fas fa-code"></i>
+                                            <span>ACM</span>
+                                        </button>
+                                    </div>
+                                </div>
+
+                                <div class="example-urls">
+                                    <details class="url-examples-details">
+                                        <summary>
+                                            <i class="fas fa-lightbulb"></i>
+                                            Example URLs
+                                        </summary>
+                                        <div class="example-list">
+                                            <button class="example-url" data-url="https://arxiv.org/abs/2301.00001">
+                                                <i class="fas fa-external-link-alt"></i>
+                                                <span>https://arxiv.org/abs/2301.00001</span>
+                                                <small>Machine Learning Paper</small>
+                                            </button>
+                                            <button class="example-url" data-url="https://arxiv.org/abs/2205.11487">
+                                                <i class="fas fa-external-link-alt"></i>
+                                                <span>https://arxiv.org/abs/2205.11487</span>
+                                                <small>PaLM Language Model</small>
+                                            </button>
+                                            <button class="example-url" data-url="https://arxiv.org/abs/2203.02155">
+                                                <i class="fas fa-external-link-alt"></i>
+                                                <span>https://arxiv.org/abs/2203.02155</span>
+                                                <small>Training Language Models</small>
+                                            </button>
+                                        </div>
+                                    </details>
+                                </div>
+                            </div>
+                        </div>
+
+                        <!-- Upload Tips -->
+                        <div class="upload-tips">
+                            <div class="tips-header">
+                                <h3><i class="fas fa-lightbulb"></i> Upload Tips</h3>
+                                <button class="tips-toggle" onclick="toggleUploadTips()">
+                                    <i class="fas fa-chevron-down"></i>
+                                </button>
+                            </div>
+                            <div class="tips-content" id="uploadTipsContent">
+                                <div class="tips-grid">
+                                    <div class="tip-item">
+                                        <div class="tip-icon">
+                                            <i class="fas fa-file-pdf"></i>
+                                        </div>
+                                        <div class="tip-content">
+                                            <h4>Best Format</h4>
+                                            <p>PDF files work best for accurate text extraction</p>
+                                        </div>
+                                    </div>
+                                    <div class="tip-item">
+                                        <div class="tip-icon">
+                                            <i class="fas fa-compress-alt"></i>
+                                        </div>
+                                        <div class="tip-content">
+                                            <h4>File Size</h4>
+                                            <p>Keep files under 16MB for faster processing</p>
+                                        </div>
+                                    </div>
+                                    <div class="tip-item">
+                                        <div class="tip-icon">
+                                            <i class="fas fa-language"></i>
+                                        </div>
+                                        <div class="tip-content">
+                                            <h4>Language</h4>
+                                            <p>English documents get the best analysis results</p>
+                                        </div>
+                                    </div>
+                                    <div class="tip-item">
+                                        <div class="tip-icon">
+                                            <i class="fas fa-shield-alt"></i>
+                                        </div>
+                                        <div class="tip-content">
+                                            <h4>Privacy</h4>
+                                            <p>Your documents are processed securely and privately</p>
+                                        </div>
+                                    </div>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+
+                <!-- Enhanced Upload Progress -->
+                <div class="upload-progress-enhanced" id="uploadProgress" style="display: none;">
+                    <div class="progress-container">
+                        <div class="progress-header">
+                            <div class="progress-title">
+                                <h3>Processing Document</h3>
+                                <p id="progressSubtitle">Preparing your document for analysis...</p>
+                            </div>
+                            <div class="progress-stats">
+                                <span class="progress-percentage" id="progressPercentage">0%</span>
+                                <span class="progress-time" id="progressTime">Estimating...</span>
+                            </div>
+                        </div>
+                        
+                        <div class="progress-bar-container">
+                            <div class="progress-bar">
+                                <div class="progress-fill" id="progressFill"></div>
+                                <div class="progress-glow"></div>
+                </div>
+            </div>
+
+                        <div class="progress-steps-enhanced">
+                            <div class="progress-step active" data-step="1">
+                                <div class="step-icon">
+                                    <i class="fas fa-upload"></i>
+                                </div>
+                                <div class="step-content">
+                                    <h4>Uploading</h4>
+                                    <p>Transferring your document</p>
+                                </div>
+                            </div>
+                            <div class="progress-step" data-step="2">
+                                <div class="step-icon">
+                                    <i class="fas fa-file-text"></i>
+                                </div>
+                                <div class="step-content">
+                                    <h4>Extracting</h4>
+                                    <p>Reading document content</p>
+                                </div>
+                            </div>
+                            <div class="progress-step" data-step="3">
+                                <div class="step-icon">
+                                    <i class="fas fa-brain"></i>
+                                </div>
+                                <div class="step-content">
+                                    <h4>Analyzing</h4>
+                                    <p>AI processing in progress</p>
+                                </div>
+                            </div>
+                            <div class="progress-step" data-step="4">
+                                <div class="step-icon">
+                                    <i class="fas fa-check-circle"></i>
+                                </div>
+                                <div class="step-content">
+                                    <h4>Complete</h4>
+                                    <p>Ready for questions</p>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+
+                <!-- Enhanced Results -->
+                <div id="uploadResults" class="results-container">
+                    <div class="upload-placeholder-enhanced">
+                        <div class="placeholder-animation">
+                            <div class="floating-documents">
+                                <div class="doc-icon">📄</div>
+                                <div class="doc-icon">📊</div>
+                                <div class="doc-icon">📋</div>
+                            </div>
+                        </div>
+                        <h3>Ready to analyze your documents?</h3>
+                        <p>Upload a file or paste a URL to get started with AI-powered analysis</p>
+                        <div class="placeholder-features">
+                            <div class="feature">
+                                <i class="fas fa-magic"></i>
+                                <span>AI Summaries</span>
+                            </div>
+                            <div class="feature">
+                                <i class="fas fa-question-circle"></i>
+                                <span>Q&A Chat</span>
+                            </div>
+                            <div class="feature">
+                                <i class="fas fa-key"></i>
+                                <span>Key Insights</span>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+                </section>
+
+        <!-- My Papers Section -->
+        <section id="mypapers" class="section">
+            <div class="mypapers-hero">
+                <div class="mypapers-hero-content">
+                    <div class="mypapers-title-area">
+                        <div class="mypapers-icon-large">
+                            <i class="fas fa-folder-open"></i>
+                        </div>
+                        <h1>My Papers</h1>
+                        <p>Access and manage all your uploaded and analyzed research papers</p>
+                    </div>
+
+                    <div class="mypapers-actions">
+                        <button class="refresh-papers-btn" onclick="researchRadar.loadMyPapers()">
+                            <i class="fas fa-sync-alt"></i>
+                            <span>Refresh</span>
+                        </button>
+                        <button class="clear-all-btn" onclick="researchRadar.clearAllPapers()">
+                            <i class="fas fa-trash"></i>
+                            <span>Clear All</span>
+                        </button>
+                    </div>
+                </div>
+            </div>
+
+            <div class="mypapers-content">
+                <div class="mypapers-loading" id="mypapersLoading" style="display: none;">
+                    <div class="loading-spinner"></div>
+                    <p>Loading your papers...</p>
+                </div>
+
+                <div class="mypapers-empty" id="mypapersEmpty" style="display: none;">
+                    <div class="empty-state">
+                        <div class="empty-icon">
+                            <i class="fas fa-folder-open"></i>
+                        </div>
+                        <h3>No Papers Yet</h3>
+                        <p>Upload documents or search for papers to get started</p>
+                        <div class="empty-actions">
+                            <button class="btn-primary" onclick="researchRadar.switchSection('upload')">
+                                <i class="fas fa-upload"></i>
+                                <span>Upload Document</span>
+                            </button>
+                            <button class="btn-secondary" onclick="researchRadar.switchSection('search')">
+                                <i class="fas fa-search"></i>
+                                <span>Search Papers</span>
+                            </button>
+                        </div>
+                    </div>
+                </div>
+
+                <div class="papers-grid" id="papersGrid">
+                    <!-- Papers will be loaded here dynamically -->
+                </div>
+            </div>
+        </section>
+
+        <!-- Summary + Chat Section -->
+            <section id="summary-chat" class="section" style="display: none;">
+                <div class="summary-chat-header">
+                    <div class="header-content">
+                        <button class="back-btn" onclick="goBackToSearch()">
+                            <i class="fas fa-arrow-left"></i>
+                            <span>Back to Search</span>
+                        </button>
+                        <div class="paper-info" id="paperInfo">
+                            <h2 id="paperTitle">Paper Title</h2>
+                            <div class="paper-meta">
+                                <span class="author" id="paperAuthor">Author Name</span>
+                                <span class="date" id="paperDate">2024</span>
+                                <span class="category" id="paperCategory">cs.LG</span>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+
+                <!-- Tab Navigation -->
+                <div class="tab-navigation">
+                    <div class="tab-nav-container">
+                        <button class="tab-btn active" data-tab="summary" onclick="switchTab('summary')">
+                            <i class="fas fa-file-text"></i>
+                            <span>AI Summary</span>
+                            <div class="tab-indicator"></div>
+                        </button>
+                        <button class="tab-btn" data-tab="chat" onclick="switchTab('chat')">
+                            <i class="fas fa-comments"></i>
+                            <span>Ask Questions</span>
+                            <div class="tab-indicator"></div>
+                        </button>
+                    </div>
+                </div>
+
+                <!-- Tab Content -->
+                <div class="tab-content-container">
+                    <!-- Summary Tab -->
+                    <div id="summary-tab" class="tab-content active">
+                        <div class="summary-container">
+                            <div class="summary-header">
+                                <div class="summary-title">
+                                    <div class="summary-icon">
+                                        <i class="fas fa-brain"></i>
+                                    </div>
+                                    <div class="summary-title-text">
+                                        <h3>AI-Generated Summary</h3>
+                                        <p>Comprehensive analysis of the research paper</p>
+                                    </div>
+                                </div>
+                            </div>
+                            
+                            <div class="summary-main-content">
+                                <div class="summary-content" id="summaryContent">
+                                    <div class="summary-loading" id="summaryLoading">
+                                        <div class="loading-spinner"></div>
+                                        <div class="loading-text">
+                                            <h4>Analyzing paper...</h4>
+                                            <p>Our AI is reading the document to generate a comprehensive summary.</p>
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