app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from datasets import load_dataset
import spaces
from huggingface_hub import login
import os
from itertools import islice

login(token=os.environ.get("hf_token"))

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("flamiry/first")
tokenizer = AutoTokenizer.from_pretrained("flamiry/first")
tokenizer.pad_token = tokenizer.eos_token

# Move model to GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

@spaces.GPU
def train_model(start, end):
    start = int(start)
    end = int(end)
    try:
        dataset = load_dataset("allenai/c4", "sk", split="train", streaming=True)
        slovak_texts = [example['text'] for example in islice(dataset, start, end)]
        inputs = tokenizer(
            slovak_texts,
            return_tensors="pt",
            padding=True,
            truncation=True,
            max_length=512
        )
        
        # Move inputs to device
        inputs = {k: v.to(device) for k, v in inputs.items()}
        
        optimizer = torch.optim.Adam(model.parameters(), lr=5e-5)
        for epoch in range(2):
            optimizer.zero_grad()
            outputs = model(**inputs, labels=inputs['input_ids'])
            loss = outputs.loss
            loss.backward()
            optimizer.step()
        
        model.push_to_hub("flamiry/first")
        tokenizer.push_to_hub("flamiry/first")
        
        return f"✅ Training complete! Final Loss: {loss.item():.4f}"
    except Exception as e:
        return f"❌ Error: {str(e)}"

@spaces.GPU
def generate_text(prompt):
    try:
        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
        output = model.generate(input_ids, max_length=50)
        return tokenizer.decode(output[0], skip_special_tokens=True)
    except Exception as e:
        return f"❌ Error: {str(e)}"

with gr.Blocks() as demo:
    gr.Markdown("# Slovak LLM Training")
    
    with gr.Tab("Train Model"):
        gr.Markdown("Click to train the model on Slovak data")
        start_input = gr.Textbox(label="Start")
        end_input = gr.Textbox(label="End")
        train_btn = gr.Button("Start Training", variant="primary")
        train_output = gr.Textbox(label="Result", interactive=False)
        train_btn.click(train_model, inputs=[start_input, end_input], outputs=train_output)
    
    with gr.Tab("Generate Text"):
        gr.Markdown("Generate Slovak text")
        prompt_input = gr.Textbox(label="Prompt")
        gen_btn = gr.Button("Generate")
        gen_output = gr.Textbox(label="Generated Text", interactive=False)
        gen_btn.click(generate_text, inputs=prompt_input, outputs=gen_output)

demo.launch()