app.py

import gradio as gr
import torch
from tiktoken import get_encoding
from model import GPT, GPTConfig  # Replace with your actual model file/module

# Load the GPT-2 tokenizer
tokenizer = get_encoding("gpt2")

# Load your custom model (adjust as necessary for your model's implementation)
model_path = "model.pth"  # Replace with the path to your model weights
model = GPT(GPTConfig())  # Initialize your custom model
model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
model.eval()  # Set the model to evaluation mode


# Function to tokenize input and generate text
def generate_text(prompt, max_length=50):
    # Tokenize the input
    input_ids = tokenizer.encode(prompt)
    input_tensor = torch.tensor([input_ids])  # Add batch dimension

    # Generate text using the model
    with torch.no_grad():
        output_ids = model.generate(input_tensor, max_length=max_length)  # Adjust if your model uses another method

    # Decode the output back to text
    generated_text = tokenizer.decode(output_ids[0].tolist())
    return generated_text


# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Custom Transformer Text Generation")
    gr.Markdown("Provide an input text prompt, and the model will generate text based on it.")

    with gr.Row():
        input_text = gr.Textbox(label="Input Prompt", placeholder="Enter your text here...", lines=2)
        max_len = gr.Slider(label="Max Output Length", minimum=10, maximum=100, value=50, step=5)

    output_text = gr.Textbox(label="Generated Text", lines=5)
    generate_button = gr.Button("Generate")

    generate_button.click(generate_text, inputs=[input_text, max_len], outputs=output_text)

# Run the app
if __name__ == "__main__":
    demo.launch()