app.py

import torch
import gradio as gr
from model import GPT, GPTConfig  # Assuming your model code is in a file named model.py
import tiktoken

# Load the trained model
def load_model(model_path):
    config = GPTConfig()  # Adjust this if you've changed the default config
    model = GPT(config)
    model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
    model.eval()
    return model

model = load_model('GPT_model.pth')  # Replace with the actual path to your .pth file
enc = tiktoken.get_encoding('gpt2')

def generate_text(prompt, max_length=100, temperature=0.7):
    input_ids = torch.tensor(enc.encode(prompt)).unsqueeze(0)
    
    with torch.no_grad():
        for _ in range(max_length):
            outputs = model(input_ids)
            next_token_logits = outputs[0][:, -1, :] / temperature
            next_token = torch.multinomial(torch.softmax(next_token_logits, dim=-1), num_samples=1)
            input_ids = torch.cat([input_ids, next_token], dim=-1)
            
            if next_token.item() == enc.encode('\n')[0]:
                break
    
    generated_text = enc.decode(input_ids[0].tolist())
    return generated_text

# Gradio interface
iface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(label="Prompt", placeholder="Enter your prompt here..."),
        gr.Slider(minimum=10, maximum=200, value=100, step=1, label="Max Length"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature")
    ],
    outputs=gr.Textbox(label="Generated Text"),
    title="GPT-2 Text Generator",
    description="Enter a prompt and generate text using a fine-tuned GPT-2 model."
)

# Launch the app
iface.launch()