EuroLotto Predictor

# generate_lottery_dataset.py
# This script creates a comprehensive dataset of lottery prediction requests
# with various phrasings, natural language variations, and negative examples.

import random
import json
import csv
import re
import nltk
from nltk.corpus import wordnet

# Core function to generate diverse lottery requests in English
def generate_english_lottery_dataset(num_samples=8000):
    # Various lottery types from around the world
    lottery_types = [
        "EuroMillions", "Powerball", "Mega Millions", "EuroJackpot", 
        "Lotto", "National Lottery", "Lotto 6/49", "Thunderball"
    ]
    
    # Request verbs and phrases for variation
    request_verbs = [
        "predict", "generate", "give", "tell me", "share", "provide", 
        "create", "get", "show", "display", "reveal", "suggest"
    ]
    
    # Processing and saving 8000 diverse examples
    # 70% positive (lottery requests) and 30% negative (general queries)
    # ...
                        

# scrape_eurolotto_numbers.py
# This script collects historical EuroJackpot results from official sources
# using advanced scraping techniques with fallback mechanisms

import requests
from bs4 import BeautifulSoup
import pandas as pd
import json
import time
import random
from datetime import datetime
import os

class EurolottoScraper:
    def __init__(self, output_dir="data"):
        self.output_dir = output_dir
        self.headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
            'Accept-Language': 'en-US,en;q=0.9',
            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
            'Referer': 'https://www.google.com/'
        }
        
    def scrape_eurojackpot_official(self, num_pages=10):
        """Scrape from the official Eurojackpot results page"""
        base_url = "https://www.euro-jackpot.net/en/results-archive"
        results = []
        
        # Implementing respectful scraping with delays
        # Extracting dates, main numbers, and Euro numbers
        # ...
                                
    # Alternative source fallback mechanism 
    def scrape_eurolotto_alternative(self, num_results=100):
        # ...
                        

# train_lottery_model.py
# Custom training pipeline for lottery query classification

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import numpy as np
import pandas as pd
import json
import os
from datetime import datetime
from tqdm import tqdm

class SimpleTextClassifier(nn.Module):
    """Simple but effective text classifier with embedding and LSTM layers"""
    def __init__(self, vocab_size, embedding_dim=100, hidden_dim=128, num_classes=2):
        super(SimpleTextClassifier, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=0)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)
        self.dropout = nn.Dropout(0.2)
        self.fc = nn.Linear(hidden_dim, num_classes)
    
    def forward(self, x):
        embedded = self.embedding(x)
        lstm_out, (hidden, cell) = self.lstm(embedded)
        dropout_out = self.dropout(hidden.squeeze(0))
        output = self.fc(dropout_out)
        return output

# Training loop with learning rate scheduling and early stopping
def train(model, train_loader, val_loader, criterion, optimizer, epochs, device):
    best_val_accuracy = 0.0
    
    for epoch in range(epochs):
        # Training phase
        model.train()
        train_loss = 0.0
        train_correct = 0
        train_total = 0
        
        # Progress tracking with tqdm
        progress_bar = tqdm(train_loader, desc=f"Epoch {epoch+1}/{epochs}")
        
        for batch in progress_bar:
            # Training step implementation
            # ...
                        

# inference.py
# Inference engine for the lottery prediction model

class LotteryQueryProcessor:
    """Main class for processing lottery queries"""
    def __init__(self, model_dir="model", confidence_threshold=0.85):
        # Load the preprocessor, model, and supporting components
        self.preprocessor = TextPreprocessor.load(os.path.join(model_dir, "text_preprocessor.json"))
        
        # Initialize model with trained weights
        self.model = SimpleTextClassifier(
            vocab_size=self.preprocessor.vocab_size,
            embedding_dim=100,
            hidden_dim=128
        )
        self.model.load_state_dict(torch.load(os.path.join(model_dir, "best_model.pth")))
        self.model.eval()  # Set model to evaluation mode

        # Load responses and initialize number generator
        self.responses = self._load_responses(model_dir)
        self.number_generator = LotteryNumberGenerator()
        
        # Lottery-specific keywords for enhanced classification
        self.lottery_keywords = ["lottery", "lotto", "eurojackpot", "numbers", "predict"]
        self.confidence_threshold = confidence_threshold
    
    def process_query(self, query_text):
        """Process a user query and return an appropriate response"""
        # Preprocess the text
        sequence = self.preprocessor.transform(query_text)
        sequence_tensor = torch.tensor(sequence, dtype=torch.long)
        
        # Get model prediction with confidence
        with torch.no_grad():
            outputs = self.model(sequence_tensor)
            probabilities = torch.softmax(outputs, dim=1)[0]
            class_0_prob = probabilities[0].item()  # Not lottery
            class_1_prob = probabilities[1].item()  # Lottery
            
        # Enhanced decision logic with keyword verification
        is_lottery_query = self._determine_intent(
            query_text, class_1_prob, class_0_prob
        )
        
        # Generate appropriate response
        if is_lottery_query:
            return self.number_generator.generate_prediction()
        else:
            return self.responses["non_lottery"]
                        

def main():
    """Interactive command-line interface for the lottery query processor"""
    print("\n=== EuroLotto Predictor ===")
    print("Type 'quit' or 'exit' to end the session.")
    print("Type 'debug on' or 'debug off' to toggle debug mode.\n")
    
    try:
        processor = LotteryQueryProcessor()
        
        while True:
            query = input("\nEnter your query: ")
            
            # Handle special commands
            if query.lower() in ['quit', 'exit', 'q']:
                print("\nThank you for using EuroLotto Predictor. Goodbye!")
                break
            
            if query.lower() == 'debug on':
                debug_mode = True
                processor.debug_mode = True
                print("Debug mode turned ON")
                continue
                
            if query.lower() == 'debug off':
                debug_mode = False
                processor.debug_mode = False
                print("Debug mode turned OFF")
                continue
            
            # Process the query and display response
            response, is_lottery_query, confidence = processor.process_query(query)
            print(f"\n{response}")
            
if __name__ == "__main__":
    main()