Upload 5 files

README.md

@@ -1,11 +1,51 @@
 ---
 title: Simple Search Engine
-emoji: 🌍
-colorFrom: yellow
-colorTo: gray
+emoji: 🔍
+colorFrom: blue
+colorTo: purple
 sdk: docker
+app_port: 7860
 pinned: false
-short_description: Simple Search Engine
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Simple Search Engine 🔍
+
+An intelligent document search engine powered by Sentence Transformers (SBERT) and FastAPI.
+
+## Features
+
+- **Semantic Search**: Uses the `all-MiniLM-L6-v2` model for understanding query context
+- **Fast & Efficient**: Built with FastAPI for high performance
+- **Beautiful UI**: Clean, modern interface with gradient design
+- **Real-time Results**: Instant search results with similarity scores
+
+## How It Works
+
+1. Documents are chunked into smaller segments (3 sentences each)
+2. Each chunk is encoded using SBERT into vector embeddings
+3. User queries are encoded and compared using cosine similarity
+4. Top 5 most relevant chunks are returned with similarity scores
+
+## Technology Stack
+
+- **Backend**: FastAPI
+- **ML Model**: Sentence Transformers (all-MiniLM-L6-v2)
+- **NLP**: NLTK for sentence tokenization
+- **Similarity**: Scikit-learn for cosine similarity computation
+
+## Usage
+
+Simply enter your search query in the search box and press Enter or click the Search button. The engine will return the top 5 most relevant document chunks with their similarity scores.
+
+## API Endpoints
+
+- `GET /` - Web interface
+- `POST /search` - Search endpoint (accepts JSON with `query` field)
+- `GET /health` - Health check endpoint
+
+## Example Queries
+
+- "machine learning AI"
+- "cloud infrastructure AWS"
+- "financial reports revenue"
+- "marketing SEO strategies"

gitignore

@@ -0,0 +1,45 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+env/
+ENV/
+.venv
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Model cache
+.cache/
+models/
+
+# NLTK data
+nltk_data/

main.py

@@ -0,0 +1,464 @@
+# main.py - FastAPI Backend
+
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import HTMLResponse
+from pydantic import BaseModel
+import nltk
+from nltk.tokenize import sent_tokenize
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+
+# Download required NLTK data
+nltk.download('punkt', quiet=True)
+nltk.download('punkt_tab')
+
+# Initialize FastAPI app
+app = FastAPI(title="Simple Search Engine")
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Define the document database
+documents = {
+    "doc1": """
+    A new AI analytics tool has been released by TechCorp. 
+    This tool uses advanced machine learning algorithms to process large datasets. 
+    It can provide real-time insights and predictive analytics for businesses. 
+    The tool integrates seamlessly with existing data infrastructure. 
+    Companies can now make data-driven decisions faster than ever before. 
+    The AI engine continuously learns from new data to improve accuracy.
+    """,
+    
+    "doc2": """
+    The quarterly finance report shows strong revenue growth. 
+    Operating expenses have decreased by 15% compared to last quarter. 
+    Net profit margins have improved significantly across all divisions. 
+    The company's cash flow remains healthy with substantial reserves. 
+    Investment in new projects is expected to yield returns next year. 
+    Shareholders can expect increased dividends this quarter.
+    """,
+    
+    "doc3": """
+    Cloud infrastructure services from AWS and Azure are becoming essential. 
+    Companies are migrating their legacy systems to the cloud for better scalability. 
+    AWS offers a wide range of compute and storage options. 
+    Azure provides excellent integration with Microsoft enterprise products. 
+    Both platforms support hybrid cloud deployments for flexibility. 
+    Security and compliance features are continuously being enhanced.
+    """,
+    
+    "doc4": """
+    Our new marketing campaign focuses on SEO optimization strategies. 
+    We are targeting high-value keywords to increase organic traffic. 
+    Social media engagement has improved by 40% this month. 
+    Content marketing efforts are driving more qualified leads. 
+    The campaign includes email marketing and paid search ads. 
+    We expect to see ROI improvements within the next quarter.
+    """,
+    
+    "doc5": """
+    The AI tool leverages machine learning for predictive maintenance. 
+    Machine learning models can detect patterns in equipment behavior. 
+    This AI-powered solution reduces downtime and operational costs. 
+    Deep learning techniques are applied to analyze sensor data. 
+    The system continuously learns and adapts to new scenarios. 
+    AI and machine learning are transforming industrial operations.
+    """
+}
+
+# Function to chunk documents
+def chunk_documents(documents, sentences_per_chunk=3):
+    chunks = []
+    chunk_metadata = []
+    
+    for doc_id, text in documents.items():
+        sentences = sent_tokenize(text.strip())
+        for i in range(0, len(sentences), sentences_per_chunk):
+            chunk = ' '.join(sentences[i:i+sentences_per_chunk])
+            chunks.append(chunk)
+            chunk_metadata.append({
+                'doc_id': doc_id,
+                'chunk_index': i // sentences_per_chunk,
+                'text': chunk
+            })
+    
+    return chunks, chunk_metadata
+
+# Initialize model and process documents at startup
+print("Initializing search engine...")
+model = SentenceTransformer('all-MiniLM-L6-v2')
+chunks, chunk_metadata = chunk_documents(documents)
+chunk_embeddings = model.encode(chunks)
+print(f"Search engine ready! {len(chunks)} chunks indexed.")
+
+# Pydantic models
+class SearchQuery(BaseModel):
+    query: str
+
+class SearchResult(BaseModel):
+    rank: int
+    doc_id: str
+    similarity_score: float
+    text: str
+
+# API Endpoints
+@app.get("/")
+async def read_root():
+    html_content = """
+    <!DOCTYPE html>
+    <html lang="en">
+    <head>
+        <meta charset="UTF-8">
+        <meta name="viewport" content="width=device-width, initial-scale=1.0">
+        <title>Simple Search Engine</title>
+        <style>
+            * {
+                margin: 0;
+                padding: 0;
+                box-sizing: border-box;
+            }
+            
+            body {
+                font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+                background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+                min-height: 100vh;
+                padding: 20px;
+            }
+            
+            .container {
+                max-width: 900px;
+                margin: 0 auto;
+            }
+            
+            .header {
+                text-align: center;
+                color: white;
+                margin-bottom: 40px;
+                padding-top: 60px;
+            }
+            
+            .header h1 {
+                font-size: 3em;
+                margin-bottom: 10px;
+                text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
+            }
+            
+            .header p {
+                font-size: 1.2em;
+                opacity: 0.9;
+            }
+            
+            .search-box {
+                background: white;
+                border-radius: 50px;
+                padding: 10px 20px;
+                box-shadow: 0 8px 30px rgba(0,0,0,0.3);
+                display: flex;
+                align-items: center;
+                margin-bottom: 40px;
+            }
+            
+            .search-box input {
+                flex: 1;
+                border: none;
+                outline: none;
+                font-size: 1.1em;
+                padding: 10px;
+            }
+            
+            .search-box button {
+                background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+                border: none;
+                color: white;
+                padding: 12px 30px;
+                border-radius: 25px;
+                font-size: 1em;
+                cursor: pointer;
+                transition: transform 0.2s;
+                font-weight: bold;
+            }
+            
+            .search-box button:hover {
+                transform: scale(1.05);
+            }
+            
+            .search-box button:active {
+                transform: scale(0.95);
+            }
+            
+            .loading {
+                text-align: center;
+                color: white;
+                font-size: 1.2em;
+                margin: 20px 0;
+                display: none;
+            }
+            
+            .loading.show {
+                display: block;
+            }
+            
+            .results {
+                display: none;
+            }
+            
+            .results.show {
+                display: block;
+            }
+            
+            .result-card {
+                background: white;
+                border-radius: 15px;
+                padding: 25px;
+                margin-bottom: 20px;
+                box-shadow: 0 4px 15px rgba(0,0,0,0.2);
+                transition: transform 0.2s, box-shadow 0.2s;
+                animation: slideIn 0.5s ease-out;
+            }
+            
+            @keyframes slideIn {
+                from {
+                    opacity: 0;
+                    transform: translateY(20px);
+                }
+                to {
+                    opacity: 1;
+                    transform: translateY(0);
+                }
+            }
+            
+            .result-card:hover {
+                transform: translateY(-5px);
+                box-shadow: 0 6px 25px rgba(0,0,0,0.3);
+            }
+            
+            .result-header {
+                display: flex;
+                justify-content: space-between;
+                align-items: center;
+                margin-bottom: 15px;
+            }
+            
+            .result-rank {
+                background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+                color: white;
+                padding: 5px 15px;
+                border-radius: 20px;
+                font-weight: bold;
+                font-size: 0.9em;
+            }
+            
+            .result-doc {
+                color: #666;
+                font-size: 0.9em;
+                font-weight: 600;
+            }
+            
+            .result-score {
+                background: #e8f5e9;
+                color: #2e7d32;
+                padding: 5px 12px;
+                border-radius: 15px;
+                font-size: 0.85em;
+                font-weight: bold;
+            }
+            
+            .result-text {
+                color: #333;
+                line-height: 1.6;
+                font-size: 1em;
+            }
+            
+            .no-results {
+                text-align: center;
+                color: white;
+                font-size: 1.2em;
+                margin-top: 40px;
+                display: none;
+            }
+            
+            .no-results.show {
+                display: block;
+            }
+            
+            .stats {
+                text-align: center;
+                color: white;
+                margin-bottom: 30px;
+                font-size: 1.1em;
+                opacity: 0.9;
+            }
+        </style>
+    </head>
+    <body>
+        <div class="container">
+            <div class="header">
+                <h1>🔍 SimpleSearch</h1>
+                <p>Your intelligent document search engine</p>
+            </div>
+            
+            <div class="search-box">
+                <input type="text" id="searchInput" placeholder="Search for documents..." />
+                <button onclick="performSearch()">Search</button>
+            </div>
+            
+            <div class="loading" id="loading">
+                <p>🔄 Searching...</p>
+            </div>
+            
+            <div class="stats" id="stats"></div>
+            
+            <div class="results" id="results"></div>
+            
+            <div class="no-results" id="noResults">
+                <p>No results found. Try a different query!</p>
+            </div>
+        </div>
+        
+        <script>
+            // Allow Enter key to trigger search
+            document.getElementById('searchInput').addEventListener('keypress', function(e) {
+                if (e.key === 'Enter') {
+                    performSearch();
+                }
+            });
+            
+            async function performSearch() {
+                const query = document.getElementById('searchInput').value.trim();
+                
+                if (!query) {
+                    alert('Please enter a search query!');
+                    return;
+                }
+                
+                // Show loading, hide results
+                document.getElementById('loading').classList.add('show');
+                document.getElementById('results').classList.remove('show');
+                document.getElementById('noResults').classList.remove('show');
+                document.getElementById('stats').innerHTML = '';
+                
+                try {
+                    const response = await fetch('/search', {
+                        method: 'POST',
+                        headers: {
+                            'Content-Type': 'application/json',
+                        },
+                        body: JSON.stringify({ query: query })
+                    });
+                    
+                    if (!response.ok) {
+                        throw new Error('Search failed');
+                    }
+                    
+                    const data = await response.json();
+                    displayResults(data, query);
+                    
+                } catch (error) {
+                    console.error('Error:', error);
+                    alert('Search failed. Please try again.');
+                } finally {
+                    document.getElementById('loading').classList.remove('show');
+                }
+            }
+            
+            function displayResults(results, query) {
+                const resultsDiv = document.getElementById('results');
+                const noResultsDiv = document.getElementById('noResults');
+                const statsDiv = document.getElementById('stats');
+                
+                if (results.length === 0) {
+                    noResultsDiv.classList.add('show');
+                    return;
+                }
+                
+                statsDiv.innerHTML = `Found <strong>${results.length}</strong> results for "<strong>${query}</strong>"`;
+                
+                resultsDiv.innerHTML = '';
+                
+                results.forEach(result => {
+                    const card = document.createElement('div');
+                    card.className = 'result-card';
+                    card.style.animationDelay = `${(result.rank - 1) * 0.1}s`;
+                    
+                    card.innerHTML = `
+                        <div class="result-header">
+                            <div style="display: flex; gap: 10px; align-items: center;">
+                                <span class="result-rank">Rank ${result.rank}</span>
+                                <span class="result-doc">${result.doc_id.toUpperCase()}</span>
+                            </div>
+                            <span class="result-score">Score: ${result.similarity_score.toFixed(4)}</span>
+                        </div>
+                        <div class="result-text">${result.text}</div>
+                    `;
+                    
+                    resultsDiv.appendChild(card);
+                });
+                
+                resultsDiv.classList.add('show');
+            }
+        </script>
+    </body>
+    </html>
+    """
+    return HTMLResponse(content=html_content)
+
+@app.post("/search", response_model=list[SearchResult])
+async def search(search_query: SearchQuery):
+    """
+    Search endpoint that takes a query and returns top 5 relevant chunks
+    """
+    if not search_query.query.strip():
+        raise HTTPException(status_code=400, detail="Query cannot be empty")
+    
+    try:
+        # Encode the query
+        query_embedding = model.encode([search_query.query])
+        
+        # Calculate cosine similarity
+        similarities = cosine_similarity(query_embedding, chunk_embeddings)[0]
+        
+        # Create results
+        results = []
+        for idx, score in enumerate(similarities):
+            results.append({
+                'chunk_index': idx,
+                'doc_id': chunk_metadata[idx]['doc_id'],
+                'similarity_score': float(score),
+                'text': chunk_metadata[idx]['text']
+            })
+        
+        # Sort by similarity score
+        results_sorted = sorted(results, key=lambda x: x['similarity_score'], reverse=True)
+        
+        # Return top 5 results
+        top_results = []
+        for rank, result in enumerate(results_sorted[:5], 1):
+            top_results.append(SearchResult(
+                rank=rank,
+                doc_id=result['doc_id'],
+                similarity_score=result['similarity_score'],
+                text=result['text']
+            ))
+        
+        return top_results
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Search error: {str(e)}")
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint"""
+    return {"status": "healthy", "total_chunks": len(chunks)}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

quick_start.sh

@@ -0,0 +1,52 @@
+#!/bin/bash
+
+# Quick Start Script for Local Testing
+# Run this before deploying to Hugging Face to test locally
+
+echo "🚀 Starting Simple Search Engine Setup..."
+echo ""
+
+# Check if Python is installed
+if ! command -v python3 &> /dev/null; then
+    echo "❌ Python 3 is not installed. Please install Python 3.8 or higher."
+    exit 1
+fi
+
+echo "✅ Python 3 found"
+
+# Create virtual environment
+echo "📦 Creating virtual environment..."
+python3 -m venv venv
+
+# Activate virtual environment
+echo "🔧 Activating virtual environment..."
+if [[ "$OSTYPE" == "msys" || "$OSTYPE" == "win32" ]]; then
+    # Windows
+    source venv/Scripts/activate
+else
+    # Linux/Mac
+    source venv/bin/activate
+fi
+
+# Install requirements
+echo "📥 Installing dependencies..."
+pip install -r requirements.txt
+
+# Download NLTK data
+echo "📚 Downloading NLTK data..."
+python -c "import nltk; nltk.download('punkt'); nltk.download('punkt_tab')"
+
+echo ""
+echo "✅ Setup complete!"
+echo ""
+echo "🌐 To start the server locally, run:"
+echo "   python3 main.py"
+echo ""
+echo "   or"
+echo ""
+echo "   uvicorn main:app --reload --host 0.0.0.0 --port 8000"
+echo ""
+echo "📱 Then open your browser to: http://localhost:8000"
+echo ""
+echo "🚀 Ready to deploy to Hugging Face? Follow the DEPLOYMENT_GUIDE.md"
+echo ""

requirements.txt

@@ -0,0 +1,7 @@
+fastapi
+uvicorn
+sentence-transformers
+scikit-learn
+nltk
+numpy
+pydantic