| 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() |