Create app.py

app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from dataclasses import dataclass
+import tiktoken
+import math
+
+# Paste your full GPT code here (copy your GPTConfig, LayerNorm, CausalSelfAttention, MLP, Block, GPT classes) 
+# For brevity, assuming GPTConfig and GPT are defined here exactly as your code.
+
+@dataclass
+class GPTConfig:
+    block_size: int
+    vocab_size: int
+    n_layer: int
+    n_head: int
+    n_embd: int
+    dropout: float = 0.1
+    bias: bool = True
+
+class LayerNorm(nn.Module):
+    def __init__(self, ndim, bias):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(ndim))
+        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
+    def forward(self, x):
+        return F.layer_norm(x, self.weight.shape, self.weight, self.bias, 1e-5)
+
+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, bias=config.bias)
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
+        self.attn_dropout = nn.Dropout(config.dropout)
+        self.resid_dropout = nn.Dropout(config.dropout)
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.flash = hasattr(F, 'scaled_dot_product_attention')
+        if not self.flash:
+            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()
+        q, k, v = self.c_attn(x).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)
+        if self.flash:
+            y = F.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.attn_dropout.p if self.training else 0.0, is_causal=True)
+        else:
+            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)
+            att = self.attn_dropout(att)
+            y = att @ v
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+        y = self.resid_dropout(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, bias=config.bias)
+        self.gelu = nn.GELU()
+        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
+        self.dropout = nn.Dropout(config.dropout)
+    def forward(self, x):
+        return self.dropout(self.c_proj(self.gelu(self.c_fc(x))))
+
+class Block(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln1 = LayerNorm(config.n_embd, config.bias)
+        self.attn = CausalSelfAttention(config)
+        self.ln2 = LayerNorm(config.n_embd, config.bias)
+        self.mlp = MLP(config)
+    def forward(self, x):
+        x = x + self.attn(self.ln1(x))
+        x = x + self.mlp(self.ln2(x))
+        return x
+
+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),
+            drop=nn.Dropout(config.dropout),
+            h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
+            ln_f=LayerNorm(config.n_embd, config.bias),
+        ))
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        self.transformer.wte.weight = self.lm_head.weight  # weight tying
+        self.apply(self._init_weights)
+        for pn, p in self.named_parameters():
+            if pn.endswith('c_proj.weight'):
+                nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer))
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, mean=0.0, std=0.02)
+    def forward(self, idx, targets=None):
+        device = idx.device
+        b, t = idx.size()
+        assert t <= self.config.block_size
+        pos = torch.arange(0, t, dtype=torch.long, device=device)
+        tok_emb = self.transformer.wte(idx)
+        pos_emb = self.transformer.wpe(pos)
+        x = self.transformer.drop(tok_emb + pos_emb)
+        for block in self.transformer.h:
+            x = block(x)
+        x = self.transformer.ln_f(x)
+        if targets is not None:
+            logits = self.lm_head(x)
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
+            return logits, loss
+        else:
+            logits = self.lm_head(x[:, [-1], :])
+            return logits, None
+    @torch.no_grad()
+    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None, top_p=None):
+        for _ in range(max_new_tokens):
+            idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
+            logits, _ = self(idx_cond)
+            logits = logits[:, -1, :] / temperature
+            if top_k is not None:
+                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                logits[logits < v[:, [-1]]] = -float('Inf')
+            if top_p is not None:
+                sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+                cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+                sorted_indices_to_remove[..., 0] = 0
+                indices_to_remove = sorted_indices[sorted_indices_to_remove]
+                logits[:, indices_to_remove] = -float('Inf')
+            probs = F.softmax(logits, dim=-1)
+            idx_next = torch.multinomial(probs, num_samples=1)
+            idx = torch.cat((idx, idx_next), dim=1)
+        return idx
+
+# --- Load checkpoint and tokenizer ---
+
+checkpoint_path = "best_model_params.pt"  # update path if needed
+
+config = GPTConfig(
+    vocab_size=50257,
+    block_size=128,
+    n_layer=6,
+    n_head=6,
+    n_embd=384,
+    dropout=0.1,
+    bias=True,
+)
+
+model = GPT(config)
+model.load_state_dict(torch.load(checkpoint_path, map_location="cpu"))
+model.eval()
+
+enc = tiktoken.get_encoding("gpt2")
+
+# --- Gradio interface ---
+
+samples = [
+    "The Fourth Amendment protects citizens against unreasonable searches and seizures.",
+    "Under the doctrine of stare decisis, courts follow precedent to ensure legal consistency.",
+    "The Commerce Clause grants Congress the power to regulate interstate commerce.",
+    "Due process requires that the government respect all legal rights owed to a person.",
+    "The principle of double jeopardy prevents a defendant from being tried twice for the same offense."
+]
+
+def generate_text(prompt, max_new_tokens=150, temperature=0.7, top_k=50, top_p=0.9):
+    input_ids = torch.tensor(enc.encode_ordinary(prompt)).unsqueeze(0)
+    with torch.no_grad():
+        output_ids = model.generate(
+            input_ids, max_new_tokens=max_new_tokens,
+            temperature=temperature, top_k=top_k, top_p=top_p
+        )
+    generated = enc.decode(output_ids.squeeze().tolist())
+    return generated
+
+import gradio as gr
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Legal GPT Text Generation Demo")
+
+    sample_dropdown = gr.Dropdown(label="Sample prompts", choices=samples, value=samples[0])
+    prompt_input = gr.Textbox(label="Input Prompt", lines=3, value=samples[0])
+
+    def update_prompt(selected):
+        return selected
+
+    sample_dropdown.change(update_prompt, inputs=sample_dropdown, outputs=prompt_input)
+
+    generate_button = gr.Button("Generate Text")
+    output_text = gr.Textbox(label="Generated Output", lines=15)
+
+    generate_button.click(generate_text, inputs=prompt_input, outputs=output_text)
+
+demo.launch()