ability to have dashboard

README.md

@@ -0,0 +1,92 @@
+# Video Inspiration Finder 🎯
+
+AI-powered YouTube video recommendation system that learns your preferences to suggest coding videos you'll love.
+
+## Features
+
+- 🔍 **Smart Search**: Finds viral coding videos from YouTube
+- 🤖 **Machine Learning**: RandomForest model learns your preferences  
+- 📊 **Web Dashboard**: YouTube-like interface showing personalized recommendations
+- 🔒 **Privacy First**: All data stored locally in SQLite
+- ⚡ **Fast Setup**: One command installation and execution
+
+## Quick Start
+
+### Option 1: Command Line Interface
+```bash
+./setup.sh
+```
+This will:
+1. Create virtual environment
+2. Install dependencies  
+3. Search for videos
+4. Start interactive rating session
+
+### Option 2: Web Dashboard
+```bash
+./setup.sh  # First time setup
+python run_dashboard.py
+```
+Opens a YouTube-like dashboard at `http://localhost:5000`
+
+## How It Works
+
+1. **Search Phase**: Queries YouTube API for trending coding videos
+2. **Rating Phase**: You rate videos as like/dislike with optional notes
+3. **Learning Phase**: After 10+ ratings, ML model activates
+4. **Recommendation Phase**: AI suggests videos based on your preferences
+
+## Project Structure
+
+```
+src/
+├── database/     # SQLite operations
+├── youtube/      # YouTube API integration  
+├── ml/          # Machine learning pipeline
+└── rating/      # Interactive rating system
+
+main.py           # CLI application
+dashboard_api.py  # Web API server
+templates/        # Dashboard HTML/CSS/JS
+```
+
+## ML Pipeline
+
+- **Features**: 11 extracted features (keywords, engagement, sentiment)
+- **Model**: RandomForest with 100 trees
+- **Training**: Incremental learning after each rating
+- **Prediction**: Confidence scores for video recommendations
+
+## Requirements
+
+- Python 3.7+
+- YouTube Data API v3 key
+- SQLite (included with Python)
+
+## Setup
+
+1. Get YouTube API key from [Google Cloud Console](https://console.cloud.google.com/)
+2. Create `.env` file:
+   ```
+   YOUTUBE_API_KEY=your_api_key_here
+   ```
+3. Run `./setup.sh`
+
+## Dashboard Features
+
+- 📱 Responsive YouTube-like design
+- 🎯 AI confidence scores for each recommendation
+- 🔄 Real-time model status (learning vs trained)
+- 🖱️ Click videos to open in YouTube
+- 📊 Visual feedback on model training progress
+
+## Commands
+
+```bash
+./setup.sh              # Full setup and CLI
+python main.py           # CLI only
+python run_dashboard.py  # Web dashboard
+python dashboard_api.py  # API server only
+```
+
+Built with Python, Flask, scikit-learn, and YouTube Data API v3.

dashboard_api.py

@@ -0,0 +1,91 @@
+import os
+from flask import Flask, jsonify, render_template
+from flask_cors import CORS
+from dotenv import load_dotenv
+
+from src.database.manager import setup_database_tables
+from src.database.preference_operations import get_training_data_from_database, get_unrated_videos_with_features_from_database, get_rated_count_from_database
+from src.database.video_operations import get_unrated_videos_from_database
+from src.ml.model_training import create_recommendation_model, train_model_on_user_preferences
+from src.ml.predictions import predict_video_preferences_with_model
+
+load_dotenv()
+
+app = Flask(__name__)
+CORS(app)
+
+class DashboardAPI:
+    def __init__(self):
+        self.db_path = "video_inspiration.db"
+        self.model = None
+        self.model_trained = False
+        setup_database_tables(self.db_path)
+        self._initialize_model()
+
+    def _initialize_model(self):
+        rated_count = get_rated_count_from_database(self.db_path)
+        if rated_count >= 10:
+            self.model = create_recommendation_model()
+            training_data = get_training_data_from_database(self.db_path)
+            success = train_model_on_user_preferences(self.model, training_data)
+            if success:
+                self.model_trained = True
+
+    def get_recommendations(self):
+        if self.model_trained and self.model:
+            video_features = get_unrated_videos_with_features_from_database(self.db_path)
+            recommendations = predict_video_preferences_with_model(self.model, video_features)
+            return recommendations[:12]  # Return 12 videos for dashboard
+        else:
+            fallback_videos = get_unrated_videos_from_database(12, self.db_path)
+            for video in fallback_videos:
+                video['like_probability'] = 0.5  # Default probability
+            return fallback_videos
+
+dashboard_api = DashboardAPI()
+
+@app.route('/')
+def dashboard():
+    return render_template('dashboard.html')
+
+@app.route('/api/recommendations')
+def get_recommendations():
+    try:
+        recommendations = dashboard_api.get_recommendations()
+        
+        formatted_recommendations = []
+        for video in recommendations:
+            formatted_recommendations.append({
+                'id': video['id'],
+                'title': video['title'],
+                'channel_name': video['channel_name'],
+                'view_count': video['view_count'],
+                'url': video['url'],
+                'thumbnail': f"https://img.youtube.com/vi/{video['id']}/hqdefault.jpg",
+                'confidence': round(video.get('like_probability', 0.5) * 100),
+                'views_formatted': format_view_count(video['view_count'])
+            })
+        
+        return jsonify({
+            'success': True,
+            'videos': formatted_recommendations,
+            'model_trained': dashboard_api.model_trained,
+            'total_ratings': get_rated_count_from_database(dashboard_api.db_path)
+        })
+    
+    except Exception as e:
+        return jsonify({
+            'success': False,
+            'error': str(e)
+        }), 500
+
+def format_view_count(count):
+    if count >= 1000000:
+        return f"{count/1000000:.1f}M views"
+    elif count >= 1000:
+        return f"{count/1000:.1f}K views"
+    else:
+        return f"{count} views"
+
+if __name__ == '__main__':
+    app.run(debug=True, port=5001)

run_dashboard.py

@@ -0,0 +1,64 @@
+#!/usr/bin/env python3
+import os
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+def check_database_exists():
+    return Path("video_inspiration.db").exists()
+
+def check_has_videos():
+    if not check_database_exists():
+        return False
+    
+    import sqlite3
+    try:
+        conn = sqlite3.connect("video_inspiration.db")
+        cursor = conn.cursor()
+        cursor.execute("SELECT COUNT(*) FROM videos")
+        count = cursor.fetchone()[0]
+        conn.close()
+        return count > 0
+    except:
+        return False
+
+def main():
+    print("🚀 Video Inspiration Dashboard")
+    print("=" * 40)
+    
+    # Check if database and videos exist
+    if not check_has_videos():
+        print("⚠️  No videos found in database!")
+        print("\nOptions:")
+        print("1. Run main application first to search and rate videos")
+        print("2. Continue with empty dashboard (demo mode)")
+        
+        choice = input("\nEnter choice (1/2): ").strip()
+        
+        if choice == "1":
+            print("\n🔍 Running main application first...")
+            try:
+                subprocess.run([sys.executable, "main.py"], check=True)
+            except KeyboardInterrupt:
+                print("\n👋 Switching to dashboard...")
+                time.sleep(1)
+            except Exception as e:
+                print(f"Error running main app: {e}")
+                return
+    
+    # Start dashboard
+    print("\n🌐 Starting dashboard server...")
+    print("📱 Dashboard will be available at: http://localhost:5000")
+    print("🛑 Press Ctrl+C to stop the server")
+    print("-" * 40)
+    
+    try:
+        subprocess.run([sys.executable, "dashboard_api.py"], check=True)
+    except KeyboardInterrupt:
+        print("\n👋 Dashboard stopped!")
+    except Exception as e:
+        print(f"Error starting dashboard: {e}")
+
+if __name__ == "__main__":
+    main()

search_more_videos.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+import os
+from dotenv import load_dotenv
+
+from src.database.manager import setup_database_tables
+from src.database.video_operations import save_videos_to_database, save_video_features_to_database
+from src.youtube.search import search_youtube_videos_by_query, get_coding_search_queries
+from src.youtube.details import get_video_details_from_youtube
+from src.youtube.utils import remove_duplicate_videos
+from src.ml.feature_extraction import extract_all_features_from_video
+
+load_dotenv()
+
+def search_more_videos():
+    api_key = os.getenv('YOUTUBE_API_KEY')
+    if not api_key:
+        print("Error: YOUTUBE_API_KEY not found in environment variables")
+        return
+    
+    db_path = "video_inspiration.db"
+    setup_database_tables(db_path)
+    
+    print("🔍 Searching for more coding videos...")
+    
+    # Use different/additional search queries to find new videos
+    additional_queries = [
+        "react tutorial 2024 millions views",
+        "python projects for beginners viral", 
+        "full stack web development course",
+        "machine learning crash course",
+        "javascript frameworks comparison",
+        "coding interview preparation",
+        "docker tutorial for developers",
+        "git and github tutorial",
+        "database design tutorial",
+        "API development tutorial"
+    ]
+    
+    all_videos = []
+    
+    for query in additional_queries:
+        print(f"  Searching: {query}")
+        video_ids = search_youtube_videos_by_query(api_key, query, 10)
+        videos = get_video_details_from_youtube(api_key, video_ids)
+        all_videos.extend(videos)
+    
+    unique_videos = remove_duplicate_videos(all_videos)
+    
+    if unique_videos:
+        save_videos_to_database(unique_videos, db_path)
+        
+        for video in unique_videos:
+            features = extract_all_features_from_video(video)
+            save_video_features_to_database(video['id'], features, db_path)
+        
+        print(f"✅ Found and saved {len(unique_videos)} new videos!")
+    else:
+        print("❌ No new videos found.")
+
+if __name__ == "__main__":
+    search_more_videos()

setup.sh

@@ -14,10 +14,93 @@ source venv/bin/activate
 
 # Install dependencies
 echo "📚 Installing dependencies..."
-pip install requests pandas scikit-learn numpy python-dotenv
+pip install requests pandas scikit-learn numpy python-dotenv flask flask-cors
 
 echo "✅ Setup complete!"
-echo "🚀 Running Video Inspiration Finder..."
 
-# Run the main script
-python main.py
+# Function to check if database has videos
+check_videos() {
+    if [ -f "video_inspiration.db" ]; then
+        video_count=$(sqlite3 video_inspiration.db "SELECT COUNT(*) FROM videos;" 2>/dev/null || echo "0")
+        echo $video_count
+    else
+        echo "0"
+    fi
+}
+
+# Function to check unrated videos count
+check_unrated_videos() {
+    if [ -f "video_inspiration.db" ]; then
+        unrated_count=$(sqlite3 video_inspiration.db "SELECT COUNT(*) FROM videos v LEFT JOIN preferences p ON v.id = p.video_id WHERE p.video_id IS NULL;" 2>/dev/null || echo "0")
+        echo $unrated_count
+    else
+        echo "0"
+    fi
+}
+
+# Check current state
+video_count=$(check_videos)
+unrated_count=$(check_unrated_videos)
+
+echo ""
+echo "📊 Current Status:"
+echo "   Total videos: $video_count"
+echo "   Unrated videos: $unrated_count"
+echo ""
+
+# Main menu
+echo "Choose what you want to do:"
+echo "1. 🌐 Launch Dashboard (recommended)"
+echo "2. 📱 Interactive CLI Rating Session"  
+echo "3. 🔍 Search for More Videos"
+echo "4. 🛠️ Full Setup (Search + Rate + Dashboard)"
+echo ""
+read -p "Enter choice (1-4): " choice
+
+case $choice in
+    1)
+        echo ""
+        echo "🌐 Launching Dashboard..."
+        if [ "$unrated_count" -eq "0" ] && [ "$video_count" -gt "0" ]; then
+            echo "⚠️  All videos are rated. Searching for more videos first..."
+            python search_more_videos.py
+        elif [ "$video_count" -eq "0" ]; then
+            echo "⚠️  No videos found. Searching for videos first..."
+            python main.py --search-only 2>/dev/null || python search_more_videos.py
+        fi
+        echo ""
+        echo "📱 Dashboard will be available at: http://localhost:5001"
+        echo "🛑 Press Ctrl+C to stop the server"
+        echo "----------------------------------------"
+        python dashboard_api.py
+        ;;
+    2)
+        echo ""
+        echo "📱 Starting Interactive Rating Session..."
+        python main.py
+        ;;
+    3)
+        echo ""
+        echo "🔍 Searching for more videos..."
+        python search_more_videos.py
+        echo ""
+        echo "✅ Search complete! You can now:"
+        echo "   • Run './setup.sh' again and choose option 1 for Dashboard"
+        echo "   • Run 'python dashboard_api.py' directly"
+        ;;
+    4)
+        echo ""
+        echo "🛠️  Running Full Setup..."
+        echo "🔍 Step 1: Searching for videos..."
+        python main.py --search-only 2>/dev/null || python search_more_videos.py
+        echo ""
+        echo "📱 Step 2: Starting rating session..."
+        echo "💡 Tip: Rate at least 10 videos to activate AI recommendations"
+        echo "   (You can press 'q' anytime to skip to dashboard)"
+        python main.py
+        ;;
+    *)
+        echo "❌ Invalid choice. Please run './setup.sh' again."
+        exit 1
+        ;;
+esac

src/rating/display.py

@@ -16,5 +16,7 @@ def display_session_type_message(is_ml_ready: bool, rated_count: int) -> str:
     if is_ml_ready:
         return "📊 ML Recommendations based on your preferences:"
     else:
-        remaining_needed = 10 - rated_count
+        remaining_needed = max(0, 10 - rated_count)
+        if remaining_needed == 0:
+            return "🎓 Ready to train ML model!"
         return f"📹 Unrated videos (need {remaining_needed} more to train ML):"

templates/dashboard.html

@@ -0,0 +1,382 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Video Inspiration Dashboard</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        body {
+            font-family: 'Roboto', sans-serif;
+            background-color: #0f0f0f;
+            color: #ffffff;
+            overflow-x: hidden;
+        }
+
+        .header {
+            background-color: #212121;
+            padding: 12px 16px;
+            display: flex;
+            align-items: center;
+            justify-content: space-between;
+            border-bottom: 1px solid #333;
+            position: sticky;
+            top: 0;
+            z-index: 100;
+        }
+
+        .logo-section {
+            display: flex;
+            align-items: center;
+            gap: 24px;
+        }
+
+        .logo {
+            display: flex;
+            align-items: center;
+            gap: 8px;
+            text-decoration: none;
+            color: white;
+        }
+
+        .logo-icon {
+            width: 32px;
+            height: 32px;
+            background: linear-gradient(45deg, #ff0000, #cc0000);
+            border-radius: 6px;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            font-weight: bold;
+            font-size: 18px;
+        }
+
+        .logo-text {
+            font-size: 20px;
+            font-weight: 500;
+        }
+
+        .search-container {
+            flex-grow: 1;
+            max-width: 640px;
+            margin: 0 40px;
+            display: flex;
+        }
+
+        .search-box {
+            flex-grow: 1;
+            padding: 12px 16px;
+            border: 1px solid #333;
+            border-radius: 40px 0 0 40px;
+            background-color: #121212;
+            color: white;
+            font-size: 16px;
+            outline: none;
+        }
+
+        .search-box:focus {
+            border-color: #3ea6ff;
+        }
+
+        .search-button {
+            padding: 12px 20px;
+            border: 1px solid #333;
+            border-left: none;
+            border-radius: 0 40px 40px 0;
+            background-color: #222;
+            cursor: pointer;
+            color: white;
+        }
+
+        .status-bar {
+            background-color: #1a1a1a;
+            padding: 16px;
+            text-align: center;
+            border-bottom: 1px solid #333;
+        }
+
+        .status-trained {
+            color: #00d400;
+        }
+
+        .status-learning {
+            color: #ffaa00;
+        }
+
+        .main-content {
+            padding: 24px;
+        }
+
+        .section-title {
+            font-size: 20px;
+            font-weight: 500;
+            margin-bottom: 16px;
+            display: flex;
+            align-items: center;
+            gap: 12px;
+        }
+
+        .ai-badge {
+            background: linear-gradient(45deg, #00d4aa, #007acc);
+            padding: 4px 8px;
+            border-radius: 12px;
+            font-size: 12px;
+            font-weight: 600;
+        }
+
+        .video-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fill, minmax(320px, 1fr));
+            gap: 24px;
+            margin-top: 24px;
+        }
+
+        .video-card {
+            background-color: transparent;
+            border-radius: 12px;
+            overflow: hidden;
+            cursor: pointer;
+            transition: transform 0.2s ease;
+        }
+
+        .video-card:hover {
+            transform: scale(1.02);
+        }
+
+        .video-thumbnail {
+            position: relative;
+            width: 100%;
+            height: 180px;
+            background-color: #333;
+            border-radius: 12px;
+            overflow: hidden;
+        }
+
+        .video-thumbnail img {
+            width: 100%;
+            height: 100%;
+            object-fit: cover;
+        }
+
+        .confidence-badge {
+            position: absolute;
+            top: 8px;
+            right: 8px;
+            background: rgba(0, 0, 0, 0.8);
+            padding: 4px 8px;
+            border-radius: 12px;
+            font-size: 12px;
+            font-weight: 600;
+        }
+
+        .confidence-high {
+            color: #00d400;
+        }
+
+        .confidence-medium {
+            color: #ffaa00;
+        }
+
+        .confidence-low {
+            color: #ff6b6b;
+        }
+
+        .video-info {
+            padding: 12px 0;
+        }
+
+        .video-title {
+            font-size: 16px;
+            font-weight: 500;
+            line-height: 1.3;
+            margin-bottom: 4px;
+            display: -webkit-box;
+            -webkit-line-clamp: 2;
+            -webkit-box-orient: vertical;
+            overflow: hidden;
+        }
+
+        .video-meta {
+            display: flex;
+            align-items: center;
+            gap: 8px;
+            color: #aaa;
+            font-size: 14px;
+        }
+
+        .loading {
+            text-align: center;
+            padding: 60px;
+            color: #aaa;
+        }
+
+        .loading-spinner {
+            width: 40px;
+            height: 40px;
+            border: 4px solid #333;
+            border-top: 4px solid #ff0000;
+            border-radius: 50%;
+            animation: spin 1s linear infinite;
+            margin: 0 auto 16px;
+        }
+
+        @keyframes spin {
+            0% { transform: rotate(0deg); }
+            100% { transform: rotate(360deg); }
+        }
+
+        .error {
+            background-color: #ff4444;
+            color: white;
+            padding: 16px;
+            border-radius: 8px;
+            margin: 20px;
+            text-align: center;
+        }
+
+        @media (max-width: 768px) {
+            .search-container {
+                margin: 0 16px;
+                max-width: none;
+            }
+            
+            .video-grid {
+                grid-template-columns: repeat(auto-fill, minmax(280px, 1fr));
+                gap: 16px;
+            }
+            
+            .main-content {
+                padding: 16px;
+            }
+        }
+    </style>
+</head>
+<body>
+    <header class="header">
+        <div class="logo-section">
+            <a href="/" class="logo">
+                <div class="logo-icon">VI</div>
+                <span class="logo-text">Video Inspiration</span>
+            </a>
+        </div>
+        
+        <div class="search-container">
+            <input type="text" class="search-box" placeholder="Search coding videos..." disabled>
+            <button class="search-button">🔍</button>
+        </div>
+        
+        <div style="width: 200px;"></div>
+    </header>
+
+    <div class="status-bar" id="statusBar">
+        <div class="loading">
+            <div class="loading-spinner"></div>
+            Loading your personalized recommendations...
+        </div>
+    </div>
+
+    <main class="main-content">
+        <h1 class="section-title" id="sectionTitle" style="display: none;">
+            Recommended for you
+            <span class="ai-badge">AI POWERED</span>
+        </h1>
+        
+        <div class="video-grid" id="videoGrid">
+            <!-- Videos will be loaded here -->
+        </div>
+    </main>
+
+    <script>
+        async function loadRecommendations() {
+            try {
+                const response = await fetch('/api/recommendations');
+                const data = await response.json();
+                
+                if (!data.success) {
+                    throw new Error(data.error || 'Failed to load recommendations');
+                }
+                
+                updateStatusBar(data.model_trained, data.total_ratings);
+                displayVideos(data.videos);
+                
+            } catch (error) {
+                console.error('Error loading recommendations:', error);
+                document.getElementById('videoGrid').innerHTML = 
+                    `<div class="error">Failed to load recommendations: ${error.message}</div>`;
+            }
+        }
+
+        function updateStatusBar(modelTrained, totalRatings) {
+            const statusBar = document.getElementById('statusBar');
+            const sectionTitle = document.getElementById('sectionTitle');
+            
+            if (modelTrained) {
+                statusBar.innerHTML = `
+                    <div class="status-trained">
+                        🤖 AI Model Active • Trained on ${totalRatings} video ratings • Showing personalized recommendations
+                    </div>
+                `;
+                sectionTitle.innerHTML = 'AI Recommendations for You <span class="ai-badge">PERSONALIZED</span>';
+            } else {
+                statusBar.innerHTML = `
+                    <div class="status-learning">
+                        📚 Learning Mode • Need ${10 - totalRatings} more ratings to activate AI recommendations
+                    </div>
+                `;
+                sectionTitle.innerHTML = 'Popular Coding Videos <span class="ai-badge">TRENDING</span>';
+            }
+            
+            sectionTitle.style.display = 'flex';
+        }
+
+        function displayVideos(videos) {
+            const videoGrid = document.getElementById('videoGrid');
+            
+            if (videos.length === 0) {
+                videoGrid.innerHTML = '<div class="loading">No videos available. Please run the main application to search for videos.</div>';
+                return;
+            }
+            
+            videoGrid.innerHTML = videos.map(video => `
+                <div class="video-card" onclick="openVideo('${video.url}')">
+                    <div class="video-thumbnail">
+                        <img src="${video.thumbnail}" alt="${video.title}" onerror="this.src='data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 width=%22320%22 height=%22180%22><rect width=%22100%%22 height=%22100%%22 fill=%22%23333%22/><text x=%2250%%22 y=%2250%%22 text-anchor=%22middle%22 dy=%22.3em%22 fill=%22%23999%22>No Image</text></svg>'">
+                        <div class="confidence-badge ${getConfidenceClass(video.confidence)}">
+                            ${video.confidence}% match
+                        </div>
+                    </div>
+                    <div class="video-info">
+                        <div class="video-title">${video.title}</div>
+                        <div class="video-meta">
+                            <span>${video.channel_name}</span>
+                            <span>•</span>
+                            <span>${video.views_formatted}</span>
+                        </div>
+                    </div>
+                </div>
+            `).join('');
+        }
+
+        function getConfidenceClass(confidence) {
+            if (confidence >= 70) return 'confidence-high';
+            if (confidence >= 50) return 'confidence-medium';
+            return 'confidence-low';
+        }
+
+        function openVideo(url) {
+            window.open(url, '_blank');
+        }
+
+        // Load recommendations when page loads
+        document.addEventListener('DOMContentLoaded', loadRecommendations);
+        
+        // Refresh every 5 minutes
+        setInterval(loadRecommendations, 300000);
+    </script>
+</body>
+</html>