app.py

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

# Load the tokenizer and the model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')

# Load the best model weights
model.load_state_dict(torch.load('best_model.pth', map_location=torch.device('cpu')))

# Set the model to evaluation mode
model.eval()

# Define the text generation function
def generate_text(prompt, max_length=50, num_return_sequences=1):
    inputs = tokenizer(prompt, return_tensors='pt')
    outputs = model.generate(
        inputs.input_ids,
        max_length=max_length,
        num_return_sequences=num_return_sequences,
        do_sample=True,
        top_k=50,
        top_p=0.95,
        temperature=1.0
    )
    return [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]

# Define the Gradio interface
interface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.inputs.Textbox(lines=2, placeholder="Enter your prompt here..."),
        gr.inputs.Slider(minimum=10, maximum=200, default=50, label="Max Length"),
        gr.inputs.Slider(minimum=1, maximum=5, default=1, label="Number of Sequences")
    ],
    outputs=gr.outputs.Textbox(),
    title="GPT-2 Text Generator",
    description="Enter a prompt to generate text using GPT-2.",
)

# Launch the Gradio interface
interface.launch()