app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
from datasets import Dataset
import tempfile
import os

# Load base tokenizer and model
model_name = "arnir0/Tiny-LLM"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def fine_tune_and_generate(uploaded_file, prompt):
    # Save uploaded file temporarily
    with tempfile.NamedTemporaryFile(delete=False, suffix=".txt") as tmp:
        tmp.write(uploaded_file.read())
        tmp_path = tmp.name
    
    # Read lines from uploaded text file
    with open(tmp_path, "r", encoding="utf-8") as f:
        lines = [line.strip() for line in f.readlines() if line.strip()]
    
    # Clean up temp file
    os.remove(tmp_path)
    
    # Create dataset for fine-tuning
    dataset = Dataset.from_dict({"text": lines})
    
    # Tokenization function
    def tokenize_function(examples):
        return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=128)
    
    tokenized_dataset = dataset.map(tokenize_function, batched=True)
    
    # Set training args (very small for demo)
    training_args = TrainingArguments(
        output_dir="./fine_tuned",
        num_train_epochs=1,
        per_device_train_batch_size=2,
        logging_dir="./logs",
        logging_steps=10,
        save_strategy="no",
        learning_rate=5e-5,
        weight_decay=0.01,
    )
    
    # Trainer init
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_dataset,
    )
    
    # Fine-tune the model
    trainer.train()
    
    # Generate text from prompt
    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
    outputs = model.generate(input_ids, max_length=50, do_sample=True, top_p=0.95, top_k=50)
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    return generated_text

# Gradio interface
iface = gr.Interface(
    fn=fine_tune_and_generate,
    inputs=[
        gr.File(label="Upload training text (.txt)"),
        gr.Textbox(lines=2, placeholder="Enter prompt for generation", label="Prompt"),
    ],
    outputs="text",
    title="Tiny-LLM Fine-tune & Generate",
    description="Upload your text file to fine-tune Tiny-LLM and generate text from a prompt."
)

iface.launch()