app.py

import gradio as gr

import torch
import torch.nn as nn
import torch.functional as F

n_embd = 64
dropout = 0.0
block_size = 32
vocab_size = 65
n_head = 4
n_layer = 4

class Head(nn.Module):

    def __init__(self, head_size):
        super().__init__()
        self.key = nn.Linear(n_embd, head_size, bias=False)
        self.query = nn.Linear(n_embd, head_size, bias=False)
        self.value = nn.Linear(n_embd, head_size, bias=False)
        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))

        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        B,T,C = x.shape
        k = self.key(x)   
        q = self.query(x) 
        wei = q @ k.transpose(-2,-1) * C**-0.5 
        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) 
        wei = F.softmax(wei, dim=-1) 
        wei = self.dropout(wei)

        v = self.value(x)
        out = wei @ v
        return out

class MultiHeadAttention(nn.Module):

    def __init__(self, num_heads, head_size):
        super().__init__()
        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
        self.proj = nn.Linear(n_embd, n_embd)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        out = torch.cat([h(x) for h in self.heads], dim=-1)
        out = self.dropout(self.proj(out))
        return out

class FeedFoward(nn.Module):

    def __init__(self, n_embd):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(n_embd, 4 * n_embd),
            nn.ReLU(),
            nn.Linear(4 * n_embd, n_embd),
            nn.Dropout(dropout),
        )

    def forward(self, x):
        return self.net(x)

class Block(nn.Module):

    def __init__(self, n_embd, n_head):
        super().__init__()
        head_size = n_embd // n_head
        self.sa = MultiHeadAttention(n_head, head_size)
        self.ffwd = FeedFoward(n_embd)
        self.ln1 = nn.LayerNorm(n_embd)
        self.ln2 = nn.LayerNorm(n_embd)

    def forward(self, x):
        x = x + self.sa(self.ln1(x))
        x = x + self.ffwd(self.ln2(x))
        return x

class BigramLanguageModel(nn.Module):

    def __init__(self):
        super().__init__()
        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
        self.position_embedding_table = nn.Embedding(block_size, n_embd)
        self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])
        self.ln_f = nn.LayerNorm(n_embd) 
        self.lm_head = nn.Linear(n_embd, vocab_size)

    def forward(self, idx, targets=None):
        B, T = idx.shape

        tok_emb = self.token_embedding_table(idx) 
        pos_emb = self.position_embedding_table(torch.arange(T)) 
        x = tok_emb + pos_emb 
        x = self.blocks(x) 
        x = self.ln_f(x) 
        logits = self.lm_head(x) 

        if targets is None:
            loss = None
        else:
            B, T, C = logits.shape
            logits = logits.view(B*T, C)
            targets = targets.view(B*T)
            loss = F.cross_entropy(logits, targets)

        return logits, loss

    def generate(self, idx, max_new_tokens):
        for _ in range(max_new_tokens):

            idx_cond = idx[:, -block_size:]
            logits, loss = self(idx_cond)
            logits = logits[:, -1, :] 
            probs = F.softmax(logits, dim=-1) 
            idx_next = torch.multinomial(probs, num_samples=1) 
            idx = torch.cat((idx, idx_next), dim=1) 

        return idx


chars = "\n !$&',-.3:;?ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
itos = { i:ch for i,ch in enumerate(chars) }
stoi = { ch:i for i,ch in enumerate(chars) }

decode = lambda l: ''.join([itos[i] for i in l])
encode = lambda s: [stoi[c] for c in s]

model = BigramLanguageModel()

def respond(
    message,
    history: list[tuple[str, str]],
):
    messages = [{"role": "system", "content": "Cocaine"}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    response = ""

    input_txt = encode(message)
    context = torch.tensor(input_txt).unsqueeze(0)
    # response = decode(model.generate(context, max_new_tokens=2000)[0].tolist())

    idx = context
    for _ in range(2000):

        idx_cond = idx[:, -block_size:]
        logits = model(idx_cond).logits
        logits = logits[:, -1, :] 
        probs = F.softmax(logits, dim=-1) 
        idx_next = torch.multinomial(probs, num_samples=1) 
        idx = torch.cat((idx, idx_next), dim=1) 

        yield decode(idx_next[0].tolist())

demo = gr.ChatInterface(respond)


if __name__ == "__main__":
    demo.launch()