app.py

import streamlit as st
import numpy as np
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer, Trainer, TrainingArguments, DataCollatorForLanguageModeling
from datasets import Dataset
import time
from datetime import datetime
import plotly.graph_objects as go

# Advanced Cyberpunk Styling
def setup_advanced_cyberpunk_style():
    st.markdown("""
        <style>
        @import url('https://fonts.googleapis.com/css2?family=Orbitron:wght@400;500;700&display=swap');
        @import url('https://fonts.googleapis.com/css2?family=Share+Tech+Mono&display=swap');
        /* Additional styling as provided previously */
        </style>
    """, unsafe_allow_html=True)

# Initialize Model and Tokenizer
def initialize_model():
    model = GPT2LMHeadModel.from_pretrained("gpt2")
    tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
    return model, tokenizer

# Prepare Dataset
def prepare_dataset(data, tokenizer, block_size=128):
    def tokenize_function(examples):
        return tokenizer(examples['text'], truncation=True, max_length=block_size, padding='max_length')

    raw_dataset = Dataset.from_dict({'text': data})
    tokenized_dataset = raw_dataset.map(tokenize_function, batched=True, remove_columns=['text'])
    tokenized_dataset = tokenized_dataset.map(
        lambda examples: {'labels': examples['input_ids']},
        batched=True
    )
    tokenized_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
    return tokenized_dataset

# Training Dashboard Class
class TrainingDashboard:
    def __init__(self):
        self.metrics = {
            'current_loss': 0,
            'best_loss': float('inf'),
            'generation': 0,
            'start_time': time.time(),
            'training_speed': 0
        }
        self.history = []
        
    def update(self, loss, generation):
        self.metrics['current_loss'] = loss
        self.metrics['generation'] = generation
        if loss < self.metrics['best_loss']:
            self.metrics['best_loss'] = loss
        elapsed_time = time.time() - self.metrics['start_time']
        self.metrics['training_speed'] = generation / elapsed_time
        self.history.append({'loss': loss, 'timestamp': datetime.now().strftime('%H:%M:%S')})
    
    def display(self):
        st.write(f"**Generation:** {self.metrics['generation']}")
        st.write(f"**Current Loss:** {self.metrics['current_loss']:.4f}")
        st.write(f"**Best Loss:** {self.metrics['best_loss']:.4f}")
        st.write(f"**Training Speed:** {self.metrics['training_speed']:.2f} generations/sec")

# Display Progress Bar
def display_progress(progress):
    st.markdown(f"""
        <div class="progress-bar-container">
            <div class="progress-bar" style="width: {progress * 100}%"></div>
        </div>
    """, unsafe_allow_html=True)

# Fitness Calculation (Placeholder for actual loss computation)
def compute_loss(model, dataset):
    # Placeholder for real loss computation with Trainer API or custom logic
    trainer = Trainer(
        model=model,
        args=TrainingArguments(output_dir="./results", per_device_train_batch_size=2, num_train_epochs=1),
        train_dataset=dataset,
        data_collator=DataCollatorForLanguageModeling(tokenizer=model.config._name_or_path, mlm=False),
    )
    train_result = trainer.train()
    return train_result.training_loss

# Training Loop with Loading Screen
def training_loop(dashboard, model, dataset, num_generations, population_size):
    with st.spinner("Training in progress..."):
        for generation in range(1, num_generations + 1):
            # Simulated population loop
            for individual in range(population_size):
                loss = compute_loss(model, dataset)
                dashboard.update(loss, generation)
                progress = generation / num_generations
                display_progress(progress)
                dashboard.display()
                time.sleep(1)  # Simulate delay for each individual training

# Main Function
def main():
    setup_advanced_cyberpunk_style()
    st.markdown('<h1 class="main-title">Neural Evolution GPT-2 Training Hub</h1>', unsafe_allow_html=True)

    # Load Model and Tokenizer
    model, tokenizer = initialize_model()

    # Prepare Data
    data = ["Sample training text"] * 10  # Replace with real data
    train_dataset = prepare_dataset(data, tokenizer)

    # Initialize Dashboard
    dashboard = TrainingDashboard()
    
    # Sidebar Configuration
    st.sidebar.markdown("### Training Parameters")
    num_generations = st.sidebar.slider("Generations", 1, 20, 5)
    population_size = st.sidebar.slider("Population Size", 4, 20, 6)
    
    # Run Training
    if st.button("Start Training"):
        training_loop(dashboard, model, train_dataset, num_generations, population_size)

if __name__ == "__main__":
    main()