app.py

import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
import gradio as gr

# Check if a GPU is available and use it, otherwise use CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load the pre-trained model and tokenizer from the saved directory
model_path = "blexus_pretrained_test"
tokenizer = GPT2Tokenizer.from_pretrained(model_path)
model = GPT2LMHeadModel.from_pretrained(model_path).to(device)

# Set model to evaluation mode
model.eval()

# Function to generate text based on input prompt
def generate_text(prompt):
    # Tokenize and encode the input prompt
    input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)

    # Generate continuation
    with torch.no_grad():
        generated_ids = model.generate(
            input_ids, 
            max_length=50,                # Maximum length of generated text
            num_return_sequences=1,        # Generate 1 sequence
            pad_token_id=tokenizer.eos_token_id,  # Use EOS token for padding
            do_sample=True,                # Enable sampling
            top_k=50,                      # Top-k sampling
            top_p=0.95                     # Nucleus sampling
        )

    # Decode the generated text
    generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
    return generated_text

# Create a Gradio interface
interface = gr.Interface(
    fn=generate_text,                      # Function to call when interacting with the UI
    inputs="text",                         # Input type: Single-line text
    outputs="text",                        # Output type: Text (the generated output)
    title="Quble Text Generation",         # Title of the UI
    description="Enter a prompt to generate text using Quble."  # Simple description
)

# Launch the Gradio app
interface.launch()