import gradio as gr from gpt import GPTLanguageModel import torch import config as cfg torch.manual_seed(1337) with open('input.txt', 'r', encoding='utf-8') as f: text = f.read() chars = sorted(list(set(text))) vocab_size = len(chars) stoi = { ch:i for i,ch in enumerate(chars) } itos = { i:ch for i,ch in enumerate(chars) } encode = lambda s: [stoi[c] for c in s] decode = lambda l: ''.join([itos[i] for i in l]) model = GPTLanguageModel(vocab_size) model.load_state_dict(torch.load('model.pth', map_location=cfg.device)) m = model.to(cfg.device) def inference(input_text, count): encoded_text = [encode(input_text)] count = int(count) context = torch.tensor(encoded_text, dtype=torch.long, device=cfg.device) out_text = decode(m.generate(context, max_new_tokens=count)[0].tolist()) return out_text title = "ERAV1 Session 21" demo = gr.Interface( inference, inputs = [gr.Textbox(label="Text", placeholder="Enter text"), gr.Textbox(label="Tokens", placeholder="Enter number of tokens to be generated")], outputs = [gr.Textbox(label="Generated text")], title = title ) demo.launch()