app.py

import torch
import torch.nn as nn
from torch.nn import functional as F
import numpy as np
import random
import re
import gradio as gr

# hyperparameters
batch_size = 16 # how many independent sequences will we process in parallel?
block_size = 32 # what is the maximum context length for predictions?
max_iters = 5000
eval_interval = 100
learning_rate = 1e-3
device = 'cuda' if torch.cuda.is_available() else 'cpu'
eval_iters = 200
n_embd = 64
n_head = 4
n_layer = 4
dropout = 0.0
# ------------

torch.manual_seed(1337)

class Head(nn.Module):
    """ one head of self-attention """

    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)   # (B,T,C)
        q = self.query(x) # (B,T,C)
        # compute attention scores ("affinities")
        wei = q @ k.transpose(-2,-1) * C**-0.5 # (B, T, C) @ (B, C, T) -> (B, T, T)
        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
        wei = F.softmax(wei, dim=-1) # (B, T, T)
        wei = self.dropout(wei)
        # perform the weighted aggregation of the values
        v = self.value(x) # (B,T,C)
        out = wei @ v # (B, T, T) @ (B, T, C) -> (B, T, C)
        return out

class MultiHeadAttention(nn.Module):
    """ multiple heads of self-attention in parallel """

    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):
    """ a simple linear layer followed by a non-linearity """

    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):
    """ Transformer block: communication followed by computation """

    def __init__(self, n_embd, n_head):
        # n_embd: embedding dimension, n_head: the number of heads we'd like
        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

# super simple bigram model
class BigramLanguageModel(nn.Module):
    def __init__(self, dataset_text, n_embd):
        super().__init__()

        # Compute character-related parameters
        self.chars = sorted(list(set(dataset_text)))
        self.vocab_size = len(self.chars)
        self.stoi = {ch: i for i, ch in enumerate(self.chars)}
        self.itos = {i: ch for ch, i in self.stoi.items()}

        self.token_embedding_table = nn.Embedding(self.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, self.vocab_size)
        self.encode = lambda s: [self.stoi[c] for c in s] # encoder: take a string, output a list of integers
        self.decode = lambda l: ''.join([self.itos[i] for i in l]) # decoder: take a list of integers, output a string

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

        # idx and targets are both (B,T) tensor of integers
        tok_emb = self.token_embedding_table(idx) # (B,T,C)
        pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C)
        x = tok_emb + pos_emb # (B,T,C)
        x = self.blocks(x) # (B,T,C)
        x = self.ln_f(x) # (B,T,C)
        logits = self.lm_head(x) # (B,T,vocab_size)

        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):
        # idx is (B, T) array of indices in the current context
        for _ in range(max_new_tokens):
            # crop idx to the last block_size tokens
            idx_cond = idx[:, -block_size:]
            # get the predictions
            logits, loss = self(idx_cond)
            # focus only on the last time step
            logits = logits[:, -1, :] # becomes (B, C)
            # apply softmax to get probabilities
            probs = F.softmax(logits, dim=-1) # (B, C)
            # sample from the distribution
            idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)
            # append sampled index to the running sequence
            idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)
        return idx

# Reading shakespeare data
with open('input.txt', 'r', encoding='utf-8') as f:
    shakespeare_text = f.read()


# Load the shakespeaere model
shakespeare_model = BigramLanguageModel(shakespeare_text, n_embd).to(device)  # Initialize an instance of your model
shakespeare_model.load_state_dict(torch.load('GPT_Shakespeare_language_model.pth', map_location=torch.device('cpu')))
shakespeare_model.eval()  # Set the model to evaluation mode


def generate_shakespeare_outputs(prompt=None, max_new_tokens=2000):
  if prompt:
    context = torch.tensor(shakespeare_model.encode(prompt), dtype=torch.long, device=device).view(1, -1)
  else:
    context = torch.zeros((1, 1), dtype=torch.long, device=device)
  text_output = shakespeare_model.decode(shakespeare_model.generate(context, max_new_tokens=max_new_tokens)[0].tolist())
  return text_output



icon_html = '<i class="fas fa-chart-bar"></i>'
title = f"""
<div style="background-color: #f5f1f2; padding: 10px; display: flex; align-items: center;">
    {icon_html} <span style="margin-left: 10px;">Nano GPT</span>
</div>
"""
description = f"""
<div style="background-color: #f1f1f5; padding: 10px; display: flex; align-items: center;">
    {icon_html}
    <span style="margin-left: 10px;">
        <p><strong>Nano GPT trained on <a href='https://www.kaggle.com/datasets/mikeortman/wikipedia-sentences'>Shakespeare dataset</a>. It is trained on a very small amount of data to understand how GPT's are trained and built. The implementation can be found <a href='https://github.com/karpathy/nanoGPT'>here.</a>"</strong></p>
    </span>
</div>
"""

shakespeare_interface = gr.Interface(generate_shakespeare_outputs,
                    inputs=[gr.Textbox(label="Enter any prompt ", type="text", value="Romeo"),
                            gr.Slider(minimum=100, maximum=5000, step=100, value=2000, label="Max new tokens")],
                    outputs=gr.Textbox(label="Output generated", type="text"), description=description)

demo = gr.TabbedInterface([shakespeare_interface], tab_names=["Shakespeare Data"], 
                          title=title)



demo.launch()