gradio_app.py

import gradio as gr
import torch
from bigram_model import BigramLanguageModel, encode, decode

# Assuming 'BigramLanguageModel' and 'decode' are defined as in your code

class GradioInterface:
    def __init__(self, model_path=None):
        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
        self.model = self.load_model(model_path)
        self.model.eval()

    def load_model(self, model_path):
        model = BigramLanguageModel().to(self.device)
        if model_path:
            model.load_state_dict(torch.load(model_path, map_location=self.device))
        return model

    def generate_text(self, input_text, max_tokens=100):
        context = torch.tensor([encode(input_text)], dtype=torch.long, device=self.device)
        output = self.model.generate(context, max_new_tokens=max_tokens)
        return decode(output[0].tolist())

# Load the model
model_path = "models/lafontaine_gpt_v8_241011_1307.pth"
model_interface = GradioInterface(model_path)

# Define Gradio interface
gr_interface = gr.Interface(
    fn=model_interface.generate_text,
    inputs=["text", gr.Slider(50, 500)],
    outputs="text",
    description="Bigram Language Model text generation. Enter some text, and the model will continue it.",
    examples=[["Once upon a time"]]
)

# Launch the interface
gr_interface.launch()