prepare_enhanced_data.py

"""
Enhanced Data Preparation for Maximum Accuracy
Comprehensive dataset creation with multiple high-quality sources
"""

import json
import requests
import time
import random
from pathlib import Path
from typing import List, Dict, Optional
import re

class EnhancedDataCollector:
    """Collect comprehensive training data for maximum accuracy"""
    
    def __init__(self, output_dir="./training_data"):
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(exist_ok=True)
        
        self.collected_data = []
        
        print("🚀 Enhanced Data Collector Initialized")
        print(f"📁 Output directory: {self.output_dir}")
    
    def collect_programming_knowledge(self):
        """Collect comprehensive programming knowledge"""
        print("💻 Collecting programming knowledge...")
        
        programming_data = [
            # Python fundamentals
            """Python is a high-level, interpreted programming language known for its simplicity and readability. Here are key concepts:

Variables and Data Types:
```python
# Basic data types
name = "Alice"          # String
age = 30               # Integer  
height = 5.6           # Float
is_student = True      # Boolean

# Collections
numbers = [1, 2, 3, 4, 5]           # List
coordinates = (10, 20)              # Tuple
student_info = {"name": "Bob", "grade": "A"}  # Dictionary
unique_items = {1, 2, 3, 4}         # Set
```

Functions and Control Flow:
```python
def calculate_average(numbers):
    if not numbers:
        return 0
    
    total = sum(numbers)
    count = len(numbers)
    return total / count

# Using the function
scores = [85, 92, 78, 96, 88]
avg_score = calculate_average(scores)
print(f"Average score: {avg_score}")

# Control structures
for score in scores:
    if score >= 90:
        print(f"Excellent: {score}")
    elif score >= 80:
        print(f"Good: {score}")
    else:
        print(f"Needs improvement: {score}")
```""",

            # JavaScript fundamentals
            """JavaScript is a versatile programming language primarily used for web development. Key concepts include:

Variables and Functions:
```javascript
// Variable declarations
const name = "Alice";           // Constant
let age = 25;                  // Mutable variable
var city = "New York";         // Function-scoped variable

// Functions
function greetUser(name, age) {
    return `Hello, ${name}! You are ${age} years old.`;
}

// Arrow functions (ES6+)
const calculateArea = (length, width) => length * width;

// Using functions
console.log(greetUser("Bob", 30));
console.log(calculateArea(5, 3));
```

Asynchronous Programming:
```javascript
// Promises
function fetchUserData(userId) {
    return new Promise((resolve, reject) => {
        setTimeout(() => {
            const user = { id: userId, name: "John Doe" };
            resolve(user);
        }, 1000);
    });
}

// Async/await syntax
async function getUser() {
    try {
        const user = await fetchUserData(123);
        console.log("User:", user);
    } catch (error) {
        console.error("Error:", error);
    }
}

getUser();
```""",

            # Data structures and algorithms
            """Data Structures and Algorithms are fundamental concepts in computer science:

Binary Search Implementation:
```python
def binary_search(arr, target):
    left, right = 0, len(arr) - 1
    
    while left <= right:
        mid = (left + right) // 2
        
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    
    return -1  # Target not found

# Usage example
sorted_numbers = [1, 3, 5, 7, 9, 11, 13, 15]
result = binary_search(sorted_numbers, 7)
print(f"Found at index: {result}")
```

Linked List Implementation:
```python
class ListNode:
    def __init__(self, val=0, next=None):
        self.val = val
        self.next = next

class LinkedList:
    def __init__(self):
        self.head = None
    
    def append(self, val):
        new_node = ListNode(val)
        if not self.head:
            self.head = new_node
            return
        
        current = self.head
        while current.next:
            current = current.next
        current.next = new_node
    
    def display(self):
        values = []
        current = self.head
        while current:
            values.append(current.val)
            current = current.next
        return values

# Usage
ll = LinkedList()
ll.append(1)
ll.append(2)
ll.append(3)
print(ll.display())  # [1, 2, 3]
```""",

            # Web development
            """Web Development encompasses frontend and backend technologies:

HTML Structure:
```html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Sample Web Page</title>
    <link rel="stylesheet" href="styles.css">
</head>
<body>
    <header>
        <nav>
            <ul>
                <li><a href="#home">Home</a></li>
                <li><a href="#about">About</a></li>
                <li><a href="#contact">Contact</a></li>
            </ul>
        </nav>
    </header>
    
    <main>
        <section id="home">
            <h1>Welcome to Our Website</h1>
            <p>This is a sample webpage demonstrating HTML structure.</p>
        </section>
    </main>
    
    <script src="script.js"></script>
</body>
</html>
```

CSS Styling:
```css
/* Reset and base styles */
* {
    margin: 0;
    padding: 0;
    box-sizing: border-box;
}

body {
    font-family: 'Arial', sans-serif;
    line-height: 1.6;
    color: #333;
}

/* Navigation styles */
nav ul {
    list-style: none;
    display: flex;
    justify-content: center;
    background-color: #2c3e50;
    padding: 1rem;
}

nav li {
    margin: 0 1rem;
}

nav a {
    color: white;
    text-decoration: none;
    padding: 0.5rem 1rem;
    border-radius: 4px;
    transition: background-color 0.3s;
}

nav a:hover {
    background-color: #34495e;
}

/* Responsive design */
@media (max-width: 768px) {
    nav ul {
        flex-direction: column;
    }
    
    nav li {
        margin: 0.25rem 0;
    }
}
```"""
        ]
        
        self.collected_data.extend(programming_data)
        print(f"✅ Collected {len(programming_data)} programming examples")
    
    def collect_science_knowledge(self):
        """Collect comprehensive science knowledge"""
        print("🔬 Collecting science knowledge...")
        
        science_data = [
            """Physics: Understanding the Natural World

Classical Mechanics:
Newton's laws of motion form the foundation of classical mechanics:

1. First Law (Inertia): An object at rest stays at rest, and an object in motion stays in motion at constant velocity, unless acted upon by an external force.

2. Second Law: The acceleration of an object is directly proportional to the net force acting on it and inversely proportional to its mass. F = ma

3. Third Law: For every action, there is an equal and opposite reaction.

Applications:
- Projectile motion: When you throw a ball, gravity acts as the constant downward force
- Orbital mechanics: Satellites orbit Earth due to gravitational force providing centripetal acceleration  
- Simple machines: Levers, pulleys, and inclined planes use mechanical advantage

Energy Conservation:
Energy cannot be created or destroyed, only transformed from one form to another:
- Kinetic energy: Energy of motion, KE = ½mv²
- Potential energy: Stored energy, PE = mgh (gravitational)
- Conservation: Total energy in an isolated system remains constant""",

            """Chemistry: The Science of Matter

Atomic Structure:
Atoms consist of protons, neutrons, and electrons:
- Protons: Positively charged, located in nucleus
- Neutrons: Neutral charge, located in nucleus  
- Electrons: Negatively charged, orbit nucleus in energy levels

Chemical Bonding:
- Ionic bonds: Transfer of electrons (Na+ + Cl- → NaCl)
- Covalent bonds: Sharing of electrons (H₂O, CO₂)
- Metallic bonds: "Sea" of electrons in metals

Chemical Reactions:
Reactions follow conservation laws and can be classified:
- Synthesis: A + B → AB
- Decomposition: AB → A + B
- Single replacement: A + BC → AC + B
- Double replacement: AB + CD → AD + CB
- Combustion: Fuel + O₂ → CO₂ + H₂O + energy

Balancing equations ensures conservation of mass:
CH₄ + 2O₂ → CO₂ + 2H₂O""",

            """Biology: The Study of Life

Cell Biology:
All living things are composed of cells:
- Prokaryotic cells: No membrane-bound nucleus (bacteria)
- Eukaryotic cells: Membrane-bound nucleus (plants, animals, fungi)

Cell organelles and their functions:
- Nucleus: Contains DNA, controls cell activities
- Mitochondria: "Powerhouses" - produce ATP energy
- Ribosomes: Protein synthesis
- Endoplasmic reticulum: Transport system
- Golgi apparatus: Packaging and shipping

Genetics and Heredity:
DNA structure: Double helix with complementary base pairs (A-T, G-C)
Gene expression: DNA → RNA → Protein (Central Dogma)
Inheritance patterns:
- Dominant and recessive alleles
- Mendelian inheritance
- Genetic variation through sexual reproduction

Evolution:
Natural selection drives evolutionary change:
1. Variation exists in populations
2. Some variations are heritable
3. More offspring are produced than can survive
4. Individuals with favorable traits are more likely to survive and reproduce
5. Favorable traits become more common over time""",

            """Environmental Science: Understanding Earth's Systems

Ecosystems:
Complex networks of interactions between organisms and environment:
- Producers: Plants and algae that convert sunlight to energy
- Primary consumers: Herbivores that eat producers
- Secondary consumers: Carnivores that eat herbivores
- Decomposers: Bacteria and fungi that break down dead matter

Energy flows through ecosystems in one direction:
Sun → Producers → Primary consumers → Secondary consumers
Only about 10% of energy transfers between levels

Biogeochemical Cycles:
- Carbon cycle: CO₂ ↔ organic compounds, photosynthesis and respiration
- Water cycle: Evaporation, condensation, precipitation
- Nitrogen cycle: N₂ fixation, nitrification, denitrification

Human Impact:
- Climate change: Greenhouse gas emissions alter global temperature
- Biodiversity loss: Habitat destruction, pollution, overexploitation
- Pollution: Air, water, and soil contamination
- Resource depletion: Overconsumption of finite resources

Sustainable solutions:
- Renewable energy (solar, wind, hydroelectric)
- Conservation and efficiency
- Circular economy principles
- Ecosystem restoration"""
        ]
        
        self.collected_data.extend(science_data)
        print(f"✅ Collected {len(science_data)} science examples")
    
    def collect_math_knowledge(self):
        """Collect comprehensive mathematics knowledge"""
        print("📐 Collecting mathematics knowledge...")
        
        math_data = [
            """Calculus: The Mathematics of Change

Derivatives:
The derivative measures the rate of change of a function:

Basic rules:
- Power rule: d/dx[x^n] = nx^(n-1)
- Product rule: d/dx[f(x)g(x)] = f'(x)g(x) + f(x)g'(x)
- Chain rule: d/dx[f(g(x))] = f'(g(x)) · g'(x)

Applications:
- Velocity is the derivative of position: v(t) = dx/dt
- Acceleration is the derivative of velocity: a(t) = dv/dt
- Finding maximum and minimum values by setting f'(x) = 0

Example: Find the maximum of f(x) = -x² + 4x + 1
f'(x) = -2x + 4
Set f'(x) = 0: -2x + 4 = 0, so x = 2
f(2) = -4 + 8 + 1 = 5, so maximum is (2, 5)

Integrals:
The integral finds the area under a curve:
∫ f(x) dx represents the antiderivative of f(x)

Fundamental Theorem of Calculus:
∫[a to b] f(x) dx = F(b) - F(a), where F'(x) = f(x)

Applications:
- Area between curves
- Volume of solids of revolution
- Work and energy problems""",

            """Linear Algebra: Vectors and Matrices

Vectors:
Vectors represent quantities with both magnitude and direction:
- 2D vector: v = [3, 4] has magnitude |v| = √(3² + 4²) = 5
- Unit vector: v̂ = v/|v| has magnitude 1

Vector operations:
- Addition: [a, b] + [c, d] = [a+c, b+d]
- Scalar multiplication: k[a, b] = [ka, kb]
- Dot product: [a, b] · [c, d] = ac + bd

Matrices:
Rectangular arrays of numbers with defined operations:

Matrix multiplication: (AB)ij = Σk Aik · Bkj
Identity matrix: I = [[1, 0], [0, 1]] (2x2 example)
Inverse matrix: A · A⁻¹ = I

Applications:
- Solving systems of linear equations: Ax = b
- Computer graphics transformations
- Data analysis and machine learning""",

            """Statistics and Probability

Descriptive Statistics:
Measures of central tendency:
- Mean: Average of all values
- Median: Middle value when data is ordered
- Mode: Most frequently occurring value

Measures of spread:
- Range: Maximum - minimum
- Standard deviation: Measure of how spread out data is
- Variance: Square of standard deviation

Probability:
Basic principles:
- P(A) = (favorable outcomes) / (total outcomes)
- P(A and B) = P(A) · P(B) if A and B are independent
- P(A or B) = P(A) + P(B) - P(A and B)

Probability distributions:
- Normal distribution: Bell-shaped curve, many natural phenomena
- Binomial distribution: Fixed number of trials with two outcomes
- Poisson distribution: Rate of rare events

Statistical Inference:
- Hypothesis testing: Test claims about populations using samples
- Confidence intervals: Range of plausible values for parameters
- Regression analysis: Relationship between variables

Example: Testing if a coin is fair
H₀: p = 0.5 (null hypothesis)
H₁: p ≠ 0.5 (alternative hypothesis)
Use sample data to calculate test statistic and p-value"""
        ]
        
        self.collected_data.extend(math_data)
        print(f"✅ Collected {len(math_data)} mathematics examples")
    
    def collect_ai_ml_knowledge(self):
        """Collect AI and Machine Learning knowledge"""
        print("🤖 Collecting AI/ML knowledge...")
        
        ai_ml_data = [
            """Machine Learning Fundamentals

Supervised Learning:
Learning from labeled examples to make predictions on new data.

Linear Regression:
Predicts continuous values using the equation: y = mx + b
Cost function: Mean Squared Error (MSE) = (1/n)Σ(yi - ŷi)²
Goal: Minimize MSE by finding optimal m and b values

```python
# Simple linear regression example
import numpy as np
from sklearn.linear_model import LinearRegression

# Training data
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 6, 8, 10])

# Create and train model
model = LinearRegression()
model.fit(X, y)

# Make predictions
prediction = model.predict([[6]])
print(f"Predicted value: {prediction[0]}")  # Should be close to 12
```

Classification:
Predicting categories or classes.

Logistic Regression:
Uses sigmoid function: σ(z) = 1/(1 + e^(-z))
Output represents probability of belonging to positive class

Decision Trees:
Make decisions by asking yes/no questions about features
Advantages: Interpretable, handles non-linear relationships
Disadvantages: Prone to overfitting, unstable

Random Forest:
Ensemble of many decision trees
- Bootstrap aggregating (bagging) reduces overfitting
- Feature randomness increases diversity
- Voting mechanism for final prediction""",

            """Deep Learning and Neural Networks

Artificial Neural Networks:
Inspired by biological neurons, consist of interconnected nodes.

Perceptron (single neuron):
output = activation(Σ(wi * xi) + bias)

Common activation functions:
- Sigmoid: σ(x) = 1/(1 + e^(-x))
- ReLU: f(x) = max(0, x)
- Tanh: f(x) = (e^x - e^(-x))/(e^x + e^(-x))

Multi-layer Perceptron:
- Input layer: Receives features
- Hidden layer(s): Extract patterns and relationships
- Output layer: Produces final predictions

Backpropagation:
Algorithm for training neural networks:
1. Forward pass: Calculate outputs and loss
2. Backward pass: Calculate gradients using chain rule
3. Update weights: w = w - α * ∇w (gradient descent)

Deep Learning Architectures:

Convolutional Neural Networks (CNNs):
Specialized for image processing
- Convolutional layers: Apply filters to detect features
- Pooling layers: Reduce spatial dimensions
- Fully connected layers: Final classification

Recurrent Neural Networks (RNNs):
Process sequential data
- Hidden state carries information across time steps
- LSTM/GRU: Solve vanishing gradient problem

Transformers:
Attention mechanism: "Attention is all you need"
- Self-attention: Relates different positions in sequence
- Multi-head attention: Multiple parallel attention mechanisms
- Applications: NLP, computer vision, protein folding""",

            """Natural Language Processing

Text Preprocessing:
Prepare raw text for machine learning:
1. Tokenization: Split text into words/tokens
2. Lowercasing: Convert to lowercase
3. Remove punctuation and special characters
4. Remove stop words: "the", "and", "or", etc.
5. Stemming/Lemmatization: Reduce words to root form

Text Representation:
Convert text to numerical format:

Bag of Words:
Represent text as frequency count of words
Document: "I love machine learning. Machine learning is amazing."
Vector: [1, 1, 2, 2, 1, 1] for [I, love, machine, learning, is, amazing]

TF-IDF (Term Frequency-Inverse Document Frequency):
Weight words by importance across document collection
TF-IDF(t,d) = TF(t,d) × log(N/DF(t))

Word Embeddings:
Dense vector representations capturing semantic meaning
- Word2Vec: Skip-gram and CBOW models
- GloVe: Global vectors for word representation
- FastText: Subword information

Language Models:
Predict probability of word sequences

N-gram models:
P(word|context) based on previous n-1 words

Neural Language Models:
- LSTM-based: Process sequences sequentially  
- Transformer-based: Process sequences in parallel
- GPT: Generative Pre-trained Transformer for text generation
- BERT: Bidirectional Encoder Representations from Transformers"""
        ]
        
        self.collected_data.extend(ai_ml_data)
        print(f"✅ Collected {len(ai_ml_data)} AI/ML examples")
    
    def collect_conversational_data(self):
        """Collect high-quality conversational examples"""
        print("💬 Collecting conversational data...")
        
        conversational_data = [
            """Q: What's the difference between machine learning and artificial intelligence?

A: Artificial Intelligence (AI) is the broader concept of machines being able to carry out tasks in a way that we would consider "smart." Machine Learning (ML) is a subset of AI that focuses on the idea that machines should be able to learn and adapt through experience.

Think of it this way: AI is the goal (creating intelligent machines), while ML is one of the methods to achieve that goal (by learning from data). Other AI approaches include rule-based systems, expert systems, and symbolic reasoning.

For example, a chess program that uses pre-programmed rules is AI but not ML. However, a chess program that improves by playing many games and learning from wins/losses is both AI and ML.

Q: Can you explain overfitting in simple terms?

A: Overfitting happens when a machine learning model learns the training data too well - including the noise and random fluctuations - rather than learning the general patterns.

Imagine studying for an exam by memorizing every single practice question and answer, word for word. You might get perfect scores on those exact practice questions, but when faced with new questions on the actual exam, you'd struggle because you didn't learn the underlying concepts.

That's overfitting: the model performs extremely well on training data but poorly on new, unseen data. Signs include:
- Very low training error but high validation error
- Large gap between training and validation performance
- Model complexity that's too high for the amount of data

Solutions include regularization, cross-validation, getting more data, or using simpler models.""",

            """Q: How do I choose the right programming language to learn?

A: The best programming language depends on your goals and interests:

**For Beginners:**
- **Python**: Excellent starter language with simple syntax. Great for data science, AI, web development, automation
- **JavaScript**: Essential for web development, runs in browsers and servers

**For Specific Goals:**
- **Web Development**: JavaScript (frontend), Python/JavaScript/PHP (backend)
- **Mobile Apps**: Swift (iOS), Kotlin/Java (Android), or React Native/Flutter for cross-platform
- **Data Science/AI**: Python (most popular), R for statistics
- **Game Development**: C# (Unity), C++ (performance-critical games)
- **Enterprise Applications**: Java, C#

**My Recommendation for Most People:**
Start with Python because:
1. Readable syntax that's beginner-friendly
2. Huge ecosystem of libraries
3. Versatile - used in web dev, data science, AI, automation
4. Strong job market
5. Great learning resources available

Once you're comfortable with programming concepts in Python, learning other languages becomes much easier because the core logic and problem-solving skills transfer.

Q: What's the most important thing to focus on when learning to code?

A: Problem-solving skills matter more than memorizing syntax. Focus on:

1. **Understanding the logic** rather than memorizing code
2. **Breaking down complex problems** into smaller, manageable pieces
3. **Practice regularly** - coding is a skill that requires consistent practice
4. **Reading and understanding others' code** - you'll learn patterns and best practices
5. **Building projects** - apply what you learn to create something real

Remember: every programmer looks up syntax and uses documentation. What separates good programmers is their ability to think through problems logically.""",

            """Q: Explain blockchain technology like I'm 10 years old.

A: Imagine you and your friends have a notebook where you write down who owes money to whom. But instead of one notebook that one person keeps, everyone has their own identical copy of the notebook.

When someone wants to make a transaction (like "Alice gives Bob $5"), they announce it to everyone. Everyone checks their notebook to make sure Alice actually has $5 to give. If everyone agrees, they all write down this new transaction in their notebooks.

This is how blockchain works:
- The "notebook" is the blockchain (a chain of blocks containing transactions)
- "Everyone" is the network of computers
- The checking process is called consensus
- Once everyone agrees and writes it down, it's very hard to cheat or change

Why is this useful?
1. **No single point of failure** - if one notebook gets lost, others remain
2. **Transparent** - everyone can see all transactions
3. **Secure** - very hard to fake transactions when everyone is watching
4. **No middleman needed** - no bank required to verify transactions

Bitcoin is the most famous use of blockchain, but it can be used for many things beyond digital money, like tracking supply chains or storing medical records securely.

Q: What career advice would you give to someone starting in tech?

A: Here's practical advice for starting a tech career:

**1. Start with Fundamentals**
- Learn problem-solving and logical thinking
- Master one programming language well before jumping to others
- Understand basic computer science concepts (data structures, algorithms)

**2. Build a Portfolio**
- Create projects that demonstrate your skills
- Contribute to open-source projects
- Document your learning journey (blog, GitHub)

**3. Network and Learn from Others**
- Join tech communities (Reddit, Discord, local meetups)
- Find mentors or experienced developers to learn from
- Attend conferences, workshops, and webinars

**4. Focus on Continuous Learning**
- Technology changes rapidly - stay curious
- Follow industry trends and best practices
- Don't try to learn everything; specialize while staying adaptable

**5. Soft Skills Matter**
- Communication is crucial (explaining technical concepts clearly)
- Teamwork and collaboration
- Time management and project planning

**6. Be Patient and Persistent**
- Imposter syndrome is common - everyone feels it
- Rejection is part of the process - keep applying and improving
- Focus on growth over perfection"""
        ]
        
        self.collected_data.extend(conversational_data)
        print(f"✅ Collected {len(conversational_data)} conversational examples")
    
    def save_training_dataset(self):
        """Save all collected data to files"""
        print("💾 Saving training dataset...")
        
        # Create comprehensive training file
        training_file = self.output_dir / "comprehensive_training_data.json"
        
        # Format data for training
        training_examples = []
        for i, text in enumerate(self.collected_data):
            training_examples.append({
                "id": i,
                "text": text.strip(),
                "source": "comprehensive_knowledge_base",
                "quality": "high",
                "length": len(text.strip())
            })
        
        # Save as JSON
        with open(training_file, 'w', encoding='utf-8') as f:
            json.dump(training_examples, f, indent=2, ensure_ascii=False)
        
        print(f"✅ Saved {len(training_examples)} training examples to {training_file}")
        
        # Create a text version for easy reading
        text_file = self.output_dir / "comprehensive_training_data.txt"
        with open(text_file, 'w', encoding='utf-8') as f:
            for i, text in enumerate(self.collected_data):
                f.write(f"=== EXAMPLE {i+1} ===\n")
                f.write(text.strip())
                f.write("\n\n" + "="*50 + "\n\n")
        
        print(f"✅ Saved text version to {text_file}")
        
        return training_file, text_file
    
    def collect_all_data(self):
        """Collect comprehensive training data from all sources"""
        print("🚀 Starting comprehensive data collection...")
        print("=" * 60)
        
        # Collect from all sources
        self.collect_programming_knowledge()
        self.collect_science_knowledge() 
        self.collect_math_knowledge()
        self.collect_ai_ml_knowledge()
        self.collect_conversational_data()
        
        # Save everything
        json_file, text_file = self.save_training_dataset()
        
        print("\n🎉 Data collection complete!")
        print("=" * 60)
        print(f"📊 Total examples collected: {len(self.collected_data)}")
        print(f"📁 JSON file: {json_file}")
        print(f"📁 Text file: {text_file}")
        
        # Calculate statistics
        total_chars = sum(len(text) for text in self.collected_data)
        avg_length = total_chars / len(self.collected_data) if self.collected_data else 0
        
        print(f"📈 Total characters: {total_chars:,}")
        print(f"📈 Average example length: {avg_length:.0f} characters")
        print("\n✅ Enhanced dataset ready for training!")
        
        return json_file, text_file

def main():
    """Main function to collect enhanced training data"""
    print("🚀 Enhanced Data Preparation for Illuminator Model")
    print("=" * 60)
    
    # Create data collector
    collector = EnhancedDataCollector()
    
    # Collect comprehensive data
    json_file, text_file = collector.collect_all_data()
    
    print(f"\n🎯 Next steps:")
    print(f"1. Review the collected data: {text_file}")
    print(f"2. Use the JSON file for training: {json_file}")
    print(f"3. Run the enhanced training script with this data")
    print(f"4. Deploy to Hugging Face Hub")

if __name__ == "__main__":
    main()