import os import gradio as gr import torch import torch.nn as nn from torch.nn import functional as F import math from dataclasses import dataclass import tiktoken # Model Architecture (same as training) class CausalSelfAttention(nn.Module): def __init__(self, config): super().__init__() assert config.n_embd % config.n_head == 0 self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd) self.c_proj = nn.Linear(config.n_embd, config.n_embd) self.c_proj.NANOGPT_SCALE_INIT = 1 self.n_head = config.n_head self.n_embd = config.n_embd self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size)).view(1, 1, config.block_size, config.block_size)) def forward(self, x): B, T, C = x.size() qkv = self.c_attn(x) q, k, v = qkv.split(self.n_embd, dim=2) k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf')) att = F.softmax(att, dim=-1) y = att @ v y = y.transpose(1, 2).contiguous().view(B, T, C) y = self.c_proj(y) return y class MLP(nn.Module): def __init__(self, config): super().__init__() self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd) self.gelu = nn.GELU(approximate='tanh') self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd) self.c_proj.NANOGPT_SCALE_INIT = 1 def forward(self, x): x = self.c_fc(x) x = self.gelu(x) x = self.c_proj(x) return x class Block(nn.Module): def __init__(self, config): super().__init__() self.ln_1 = nn.LayerNorm(config.n_embd) self.attn = CausalSelfAttention(config) self.ln_2 = nn.LayerNorm(config.n_embd) self.mlp = MLP(config) def forward(self, x): x = x + self.attn(self.ln_1(x)) x = x + self.mlp(self.ln_2(x)) return x @dataclass class GPTConfig: block_size: int = 1024 vocab_size: int = 50257 n_layer: int = 6 n_head: int = 6 n_embd: int = 384 class GPT(nn.Module): def __init__(self, config): super().__init__() self.config = config self.transformer = nn.ModuleDict(dict( wte = nn.Embedding(config.vocab_size, config.n_embd), wpe = nn.Embedding(config.block_size, config.n_embd), h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]), ln_f = nn.LayerNorm(config.n_embd), )) self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) self.transformer.wte.weight = self.lm_head.weight def forward(self, idx, targets=None): B, T = idx.size() assert T <= self.config.block_size pos = torch.arange(0, T, dtype=torch.long, device=idx.device) pos_emb = self.transformer.wpe(pos) tok_emb = self.transformer.wte(idx) x = tok_emb + pos_emb for block in self.transformer.h: x = block(x) x = self.transformer.ln_f(x) logits = self.lm_head(x) loss = None if targets is not None: loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1)) return logits, loss # Load model and tokenizer device = 'cuda' if torch.cuda.is_available() else 'cpu' config = GPTConfig() model = GPT(config) model_path = os.path.join("models", "best_model.pt") model.load_state_dict(torch.load(model_path, map_location=device)) model.to(device) model.eval() enc = tiktoken.get_encoding('gpt2') print(f"✅ Model loaded on {device}!") def generate(prompt: str, max_new_tokens: int = 30, top_k: int = 50, temperature: float = 1.0): tokens = enc.encode(prompt) max_ctx = config.block_size if len(tokens) > max_ctx - 1: tokens = tokens[-(max_ctx - 1):] x = torch.tensor([tokens], dtype=torch.long, device=device) with torch.no_grad(): for _ in range(max_new_tokens): logits, _ = model(x) logits = logits[:, -1, :] / max(1e-8, temperature) probs = F.softmax(logits, dim=-1) topk_probs, topk_idx = torch.topk(probs, top_k, dim=-1) ix = torch.multinomial(topk_probs, 1) next_token = torch.gather(topk_idx, -1, ix) x = torch.cat([x, next_token], dim=1) out_tokens = x[0].tolist() return enc.decode(out_tokens) # Example prompts for dropdown example_prompts = [ "To be, or not to be, that is the question:", "O Romeo, Romeo! wherefore art thou Romeo?", "Once more unto the breach, dear friends, once more;", "All the world's a stage,", "The lady doth protest too much, methinks." ] with gr.Blocks() as demo: gr.Markdown("# GPT-2 (124M) Shakespeare Text Generator") gr.Markdown( "GPT-2 (124M) model trained from scratch on Shakespeare's works. " "Start with a prompt and generate Shakespearean-style text!" ) with gr.Row(): inp = gr.Textbox(lines=3, placeholder="Enter prompt here...", label="Prompt") out = gr.Textbox(lines=10, label="Generated Text") with gr.Row(): max_tokens = gr.Slider(1, 200, value=30, step=1, label="Max new tokens") topk = gr.Slider(1, 200, value=50, step=1, label="Top-k") temp = gr.Slider(0.01, 2.0, value=1.0, step=0.01, label="Temperature") with gr.Row(): example_dropdown = gr.Dropdown( choices=example_prompts, label="Choose example prompt", interactive=True ) clear_btn = gr.Button("Clear output") def use_example(prompt): return prompt def clear_output(): return "" example_dropdown.change(fn=use_example, inputs=example_dropdown, outputs=inp) clear_btn.click(fn=clear_output, inputs=[], outputs=out) btn = gr.Button("Generate") btn.click(fn=generate, inputs=[inp, max_tokens, topk, temp], outputs=out) if __name__ == "__main__": demo.launch(server_name="0.0.0.0", share=False)