app.py

import time
import math
from contextlib import nullcontext
import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
import inspect
from dataclasses import dataclass
import streamlit as st
import pandas as pd
from rdkit import Chem
from rdkit.Chem import Draw


eval_interval = 250 # keep frequent because we'll overfit
eval_iters = 200
log_interval = 10 # don't print too too often
always_save_checkpoint = False
dataset = 'lipid_char'
gradient_accumulation_steps = 1
beta2 = 0.99 # make a bit bigger because number of tokens per iter is small
eval_only = False # if True, script exits right after the first eval
init_from = 'scratch' # 'scratch' or 'resume' or 'gpt2*'
bias = False # do we use bias inside LayerNorm and Linear layers?
weight_decay = 1e-1
beta1 = 0.9
grad_clip = 1.0 # clip gradients at this value, or disable if == 0.0
min_lr = 6e-5 # minimum learning rate, should be ~= learning_rate/10 per Chinchilla
backend = 'nccl' # 'nccl', 'gloo', etc.
device = 'cuda' # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1' etc., or try 'mps' on macbooks
dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float16' # 'float32', 'bfloat16', or 'float16', the latter will auto implement a GradScaler
config_keys = [k for k,v in globals().items() if not k.startswith('_') and isinstance(v, (int, float, bool, str))]
config = {k: globals()[k] for k in config_keys}
master_process = True
torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
device_type = 'cuda' if 'cuda' in device else 'cpu' # for later use in torch.autocast
ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]
ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(device_type=device_type, dtype=ptdtype)


@st.cache_resource
class LayerNorm(nn.Module):
    """ LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """

    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, input):
        return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)

@st.cache_resource
class CausalSelfAttention(nn.Module):

    def __init__(self, config):
        super().__init__()
        assert config.n_embd % config.n_head == 0
        # key, query, value projections for all heads, but in a batch
        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
        # output projection
        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
        # regularization
        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.dropout = config.dropout
        # flash attention make GPU go brrrrr but support is only in PyTorch >= 2.0
        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
        if not self.flash:
            print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
            # causal mask to ensure that attention is only applied to the left in the input sequence
            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() # batch size, sequence length, embedding dimensionality (n_embd)

        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
        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) # (B, nh, T, hs)
        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)

        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
        if self.flash:
            # efficient attention using Flash Attention CUDA kernels
            y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.dropout if self.training else 0, is_causal=True)
        else:
            # manual implementation of attention
            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 # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side

        # output projection
        y = self.resid_dropout(self.c_proj(y))
        return y

@st.cache_resource
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):
        x = self.c_fc(x)
        x = self.gelu(x)
        x = self.c_proj(x)
        x = self.dropout(x)
        return x

@st.cache_resource
class Block(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
        self.attn = CausalSelfAttention(config)
        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
        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 = 50304 # GPT-2 vocab_size of 50257, padded up to nearest multiple of 64 for efficiency
    n_layer: int = 12
    n_head: int = 12
    n_embd: int = 768
    dropout: float = 0.0
    bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster

@st.cache_resource
class GPT(nn.Module):

    def __init__(self, config):
        super().__init__()
        assert config.vocab_size is not None
        assert config.block_size is not None
        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, bias=config.bias),
        ))
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        self.transformer.wte.weight = self.lm_head.weight # https://paperswithcode.com/method/weight-tying

        # init all weights
        self.apply(self._init_weights)
        # apply special scaled init to the residual projections, per GPT-2 paper
        for pn, p in self.named_parameters():
            if pn.endswith('c_proj.weight'):
                torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * config.n_layer))
        # report number of parameters
        print("number of parameters: %.2fM" % (self.get_num_params()/1e6,))

    def get_num_params(self, non_embedding=True):
        """
        Return the number of parameters in the model.
        For non-embedding count (default), the position embeddings get subtracted.
        The token embeddings would too, except due to the parameter sharing these
        params are actually used as weights in the final layer, so we include them.
        """
        n_params = sum(p.numel() for p in self.parameters())
        if non_embedding:
            n_params -= self.transformer.wpe.weight.numel()
        return n_params

    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
            if module.bias is not None:
                torch.nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            torch.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, f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
        pos = torch.arange(0, t, dtype=torch.long, device=device) # shape (t)

        # forward the GPT model itself
        tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
        pos_emb = self.transformer.wpe(pos) # position embeddings of shape (t, n_embd)
        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:
            # if we are given some desired targets also calculate the loss
            logits = self.lm_head(x)
            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
        else:
            # inference-time mini-optimization: only forward the lm_head on the very last position
            logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim
            loss = None
        return logits, loss

    def crop_block_size(self, block_size):
        # model surgery to decrease the block size if necessary
        # e.g. we may load the GPT2 pretrained model checkpoint (block size 1024)
        # but want to use a smaller block size for some smaller, simpler model
        assert block_size <= self.config.block_size
        self.config.block_size = block_size
        self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:block_size])
        for block in self.transformer.h:
            if hasattr(block.attn, 'bias'):
                block.attn.bias = block.attn.bias[:,:,:block_size,:block_size]

    @classmethod
    def from_pretrained(cls, model_type, override_args=None):
        assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}
        override_args = override_args or {} # default to empty dict
        # only dropout can be overridden see more notes below
        assert all(k == 'dropout' for k in override_args)
        from transformers import GPT2LMHeadModel
        print("loading weights from pretrained gpt: %s" % model_type)

        # n_layer, n_head and n_embd are determined from model_type
        config_args = {
            'gpt2':         dict(n_layer=12, n_head=12, n_embd=768),  # 124M params
            'gpt2-medium':  dict(n_layer=24, n_head=16, n_embd=1024), # 350M params
            'gpt2-large':   dict(n_layer=36, n_head=20, n_embd=1280), # 774M params
            'gpt2-xl':      dict(n_layer=48, n_head=25, n_embd=1600), # 1558M params
        }[model_type]
        print("forcing vocab_size=50257, block_size=1024, bias=True")
        config_args['vocab_size'] = 50257 # always 50257 for GPT model checkpoints
        config_args['block_size'] = 1024 # always 1024 for GPT model checkpoints
        config_args['bias'] = True # always True for GPT model checkpoints
        # we can override the dropout rate, if desired
        if 'dropout' in override_args:
            print(f"overriding dropout rate to {override_args['dropout']}")
            config_args['dropout'] = override_args['dropout']
        # create a from-scratch initialized minGPT model
        config = GPTConfig(**config_args)
        model = GPT(config)
        sd = model.state_dict()
        sd_keys = sd.keys()
        sd_keys = [k for k in sd_keys if not k.endswith('.attn.bias')] # discard this mask / buffer, not a param

        # init a huggingface/transformers model
        model_hf = GPT2LMHeadModel.from_pretrained(model_type)
        sd_hf = model_hf.state_dict()

        # copy while ensuring all of the parameters are aligned and match in names and shapes
        sd_keys_hf = sd_hf.keys()
        sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.masked_bias')] # ignore these, just a buffer
        sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.bias')] # same, just the mask (buffer)
        transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight']
        # basically the openai checkpoints use a "Conv1D" module, but we only want to use a vanilla Linear
        # this means that we have to transpose these weights when we import them
        assert len(sd_keys_hf) == len(sd_keys), f"mismatched keys: {len(sd_keys_hf)} != {len(sd_keys)}"
        for k in sd_keys_hf:
            if any(k.endswith(w) for w in transposed):
                # special treatment for the Conv1D weights we need to transpose
                assert sd_hf[k].shape[::-1] == sd[k].shape
                with torch.no_grad():
                    sd[k].copy_(sd_hf[k].t())
            else:
                # vanilla copy over the other parameters
                assert sd_hf[k].shape == sd[k].shape
                with torch.no_grad():
                    sd[k].copy_(sd_hf[k])
        return model

    def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
        # start with all of the candidate parameters
        param_dict = {pn: p for pn, p in self.named_parameters()}
        # filter out those that do not require grad
        param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}
        # create optim groups. Any parameters that is 2D will be weight decayed, otherwise no.
        # i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't.
        decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
        nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
        optim_groups = [
            {'params': decay_params, 'weight_decay': weight_decay},
            {'params': nodecay_params, 'weight_decay': 0.0}
        ]
        num_decay_params = sum(p.numel() for p in decay_params)
        num_nodecay_params = sum(p.numel() for p in nodecay_params)
        print(f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters")
        print(f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters")
        # Create AdamW optimizer and use the fused version if it is available
        fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters
        use_fused = fused_available and device_type == 'cuda'
        extra_args = dict(fused=True) if use_fused else dict()
        optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, **extra_args)
        print(f"using fused AdamW: {use_fused}")
        return optimizer

    def estimate_mfu(self, fwdbwd_per_iter, dt):
        """ estimate model flops utilization (MFU) in units of A100 bfloat16 peak FLOPS """
        # first estimate the number of flops we do per iteration.
        # see PaLM paper Appendix B as ref: https://arxiv.org/abs/2204.02311
        N = self.get_num_params()
        cfg = self.config
        L, H, Q, T = cfg.n_layer, cfg.n_head, cfg.n_embd//cfg.n_head, cfg.block_size
        flops_per_token = 6*N + 12*L*H*Q*T
        flops_per_fwdbwd = flops_per_token * T
        flops_per_iter = flops_per_fwdbwd * fwdbwd_per_iter
        # express our flops throughput as ratio of A100 bfloat16 peak flops
        flops_achieved = flops_per_iter * (1.0/dt) # per second
        flops_promised = 312e12 # A100 GPU bfloat16 peak flops is 312 TFLOPS
        mfu = flops_achieved / flops_promised
        return mfu

    @torch.no_grad()
    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
        while True:
            # if the sequence context is growing too long we must crop it at block_size
            idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
            # forward the model to get the logits for the index in the sequence
            logits, _ = self(idx_cond)
            # pluck the logits at the final step and scale by desired temperature
            logits = logits[:, -1, :] / temperature
            # optionally crop the logits to only the top k options
            if top_k is not None:
                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
                logits[logits < v[:, [-1]]] = -float('Inf')
            # apply softmax to convert logits to (normalized) probabilities
            probs = F.softmax(logits, dim=-1)
            # sample from the distribution
            idx_next = torch.multinomial(probs, num_samples=1)
            if idx_next.item() == 0:
                break
            else:
                idx = torch.cat((idx, idx_next), dim=1)
        return idx

@st.cache_data
def canonicalize(smiles):
    mol = Chem.MolFromSmiles(smiles)
    if mol is not None:
        return Chem.MolToSmiles(mol)
    else:
        return None

@st.cache_data
def get_batch(split):
    data = train_data if split == 'train' else val_data
    ix = torch.randint(len(data) - block_size, (batch_size,))
    x = torch.stack([torch.from_numpy((data[i:i+block_size]).astype(np.int64)) for i in ix])
    y = torch.stack([torch.from_numpy((data[i+1:i+1+block_size]).astype(np.int64)) for i in ix])
    if device_type == 'cuda':
        x, y = x.pin_memory().to(device, non_blocking=True), y.pin_memory().to(device, non_blocking=True)
    else:
        x, y = x.to(device), y.to(device)
    return x, y


st.subheader(":rainbow[LiPT: Generate New Molecules With Your Training Data]")
col1, col2, col3 = st.columns(3)
uploaded_file = st.file_uploader("Upload SMILES Data (SMILES should be in a column named smiles)", type="csv")
split_ratio = st.slider("Train-Test Split Ratio", min_value=0.01, max_value=0.99, value=0.9)
st.write("Train on {:.1f}% of data. Test on {:.1f}% of data.".format(split_ratio*100, 100-split_ratio*100))

with col1:
    max_iters = st.number_input("Training Iterations", min_value=1000, value=4500, step=1)
    dropout = st.number_input("Dropout", min_value=0.0, max_value=0.9, value=0.05)
    learning_rate = st.number_input("Learning Rate", min_value=6e-5, max_value=1.0, value=1e-3, format="%.5f") # with baby networks can afford to go a bit higher
    
with col2:
    model_complexity = st.number_input("Model Complexity", min_value=1, value=3, step=1)
    grad_clip = st.number_input("Gradient Clipping", min_value=0.01, max_value=100.0, value=1.0) # clip gradients at this value, or disable if == 0.0
    temperature = st.number_input("Randomness of Generation", min_value=0.01, max_value=100.0, value=1.0) # 1.0 = no change, < 1.0 = less random, > 1.0 = more random, in predictions

with col3:
    batch_size = st.number_input("Batch Size", min_value=2, value=64, step=1)
    block_size = st.number_input("Context Window", min_value=2, value=256, step=1) # context of up to 256 previous characters
    num_samples = st.number_input(":violet[**Number of Molecules to Generate**]", min_value=1, value=20, step=1)
    
tokens_per_iter = gradient_accumulation_steps * batch_size * block_size
decay_lr = st.checkbox("Learning Rate Decay", value=True) # whether to decay the learning rate
n_layer = model_complexity
n_head = model_complexity
n_embd = 64 * model_complexity

if uploaded_file and st.button(":orange[**Train Model and Generate Molecules**]"):
    lipid_data = pd.read_csv(uploaded_file).drop_duplicates(subset=['smiles'])
    lipid_data['smiles'] = lipid_data['smiles'].apply(canonicalize)
    lipid_data = lipid_data.dropna().sample(frac=1).reset_index(drop=True)
    data = '\n'.join(lipid_data['smiles'])
    chars = sorted(list(set(data)))
    vocab_size = len(chars)
    st.write("Number of Valid Lipids: {:d}".format(len(lipid_data)))
    st.write("All unique characters:", ''.join(chars))
    st.write(f"Vocabulary size: {vocab_size:,}")
    stoi = {ch:i for i,ch in enumerate(chars)}
    itos = {i:ch for i,ch in enumerate(chars)}
    def encode(s):
        return [stoi[c] for c in s]
    def decode(l):
        return ''.join([itos[i] for i in l])
    lr_decay_iters = max_iters # make equal to max_iters usually
    warmup_iters = max_iters // 50
    n = len(data)
    train_data = data[:int(n * split_ratio)]
    val_data = data[int(n * split_ratio):]
    train_ids = encode(train_data)
    val_ids = encode(val_data)
    st.write(f"{len(train_ids):,} tokens for training")
    st.write(f"{len(val_ids):,} tokens for testing")
    st.write(":orange[Patience is key 😎]")
    train_data = np.array(train_ids, dtype=np.uint16)
    val_data = np.array(val_ids, dtype=np.uint16)
    meta = {'vocab_size': vocab_size, 'itos': itos, 'stoi': stoi}
    
    iter_num = 0
    best_val_loss = 1e9
    meta_vocab_size = meta['vocab_size']
    model_args = dict(n_layer=n_layer, n_head=n_head, n_embd=n_embd, block_size=block_size, bias=bias, vocab_size=None, dropout=dropout)
    model_args['vocab_size'] = meta_vocab_size
    gptconf = GPTConfig(**model_args)
    model = GPT(gptconf)
    if block_size < model.config.block_size:
        model.crop_block_size(block_size)
        model_args['block_size'] = block_size
    model.to(device)
    scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16'))
    optimizer = model.configure_optimizers(weight_decay, learning_rate, (beta1, beta2), device_type)
    checkpoint = None
    
    @torch.no_grad()
    def estimate_loss():
        out = {}
        model.eval()
        for split in ['train', 'val']:
            losses = torch.zeros(eval_iters)
            for k in range(eval_iters):
                X, Y = get_batch(split)
                with ctx:
                    logits, loss = model(X, Y)
                losses[k] = loss.item()
            out[split] = losses.mean()
        model.train()
        return out
    
    def get_lr(it):
        if it < warmup_iters:
            return learning_rate * it / warmup_iters
        if it > lr_decay_iters:
            return min_lr
        decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)
        assert 0 <= decay_ratio <= 1
        coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) # coeff ranges 0..1
        return min_lr + coeff * (learning_rate - min_lr)
        
    X, Y = get_batch('train') # fetch the very first batch
    t0 = time.time()
    local_iter_num = 0 # number of iterations in the lifetime of this process
    raw_model =  model # unwrap DDP container if needed
    running_mfu = -1.0
    
    while True:
        lr = get_lr(iter_num) if decay_lr else learning_rate
        for param_group in optimizer.param_groups:
            param_group['lr'] = lr
    
        if iter_num % eval_interval == 0 and master_process:
            losses = estimate_loss()
            print(f"step {iter_num}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")

            if losses['val'] < best_val_loss or always_save_checkpoint:
                best_val_loss = losses['val']
                if iter_num > 0:
                    checkpoint = {
                        'model': raw_model.state_dict(),
                        'optimizer': optimizer.state_dict(),
                        'model_args': model_args,
                        'iter_num': iter_num,
                        'best_val_loss': best_val_loss,
                        'config': config,
                    }
                    print("saving checkpoint")
                    torch.save(checkpoint, 'ckpt.pt')
        if iter_num == 0 and eval_only:
            break
        for micro_step in range(gradient_accumulation_steps):
            with ctx:
                logits, loss = model(X, Y)
                loss = loss / gradient_accumulation_steps # scale the loss to account for gradient accumulation
            X, Y = get_batch('train')
            scaler.scale(loss).backward()
        if grad_clip != 0.0:
            scaler.unscale_(optimizer)
            torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
        scaler.step(optimizer)
        scaler.update()
        optimizer.zero_grad(set_to_none=True)
        t1 = time.time()
        dt = t1 - t0
        t0 = t1
        if iter_num % log_interval == 0 and master_process:
            lossf = loss.item() * gradient_accumulation_steps
            if local_iter_num >= 5: # let the training loop settle a bit
                mfu = raw_model.estimate_mfu(batch_size * gradient_accumulation_steps, dt)
                running_mfu = mfu if running_mfu == -1.0 else 0.9*running_mfu + 0.1*mfu
            print(f"iter {iter_num}: loss {lossf:.4f}, time {dt*1000:.2f}ms, mfu {running_mfu*100:.2f}%")
        iter_num += 1
        local_iter_num += 1
        if iter_num > max_iters:
            break
        
    start = "\n"
    max_new_tokens = 512 # number of tokens generated in each sample
    top_k = 100 # retain only the top_k most likely tokens, clamp others to have 0 probability
    start_ids = encode(start)
    x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])
    gen_smiles = []
    with torch.no_grad():
        with ctx:
            while len(gen_smiles) < num_samples:
                y = model.generate(x, max_new_tokens, temperature=temperature, top_k=top_k)
                smiles = decode(y[0].tolist()).replace('\n', '')
                mol = Chem.MolFromSmiles(smiles)
                if mol:
                    smiles = Chem.MolToSmiles(mol)
                    if (smiles not in gen_smiles) and (smiles not in data):
                        gen_smiles.append(smiles)
    
    print(gen_smiles)
    smiles_df = pd.DataFrame(gen_smiles, columns=['smiles'])
    st.table(smiles_df)
    csv_data = smiles_df.to_csv(index=False)
    st.download_button(label="Download CSV", data=csv_data, file_name="generated_smiles.csv", mime="text/csv")
    st.image(Draw.MolToImage(Chem.MolFromSmiles(gen_smiles[0]), size=(600, 600)), caption='First Generated Molecule', use_column_width=True)