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app.py

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+# imports
+import gradio as gr
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+print("done importing packages...")
+
+with open('dataset.txt', 'r') as f:
+    text = f.read()
+
+# hyperparms
+batch_size = 16 
+block_size = 32
+max_iters = 5000
+eval_interval = 100
+learning_rate = 0.001
+eval_iters = 200
+n_embd = 64
+n_head = 4
+n_layer = 4
+dropout = 0.0
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+torch.manual_seed(1337)
+print("big brain stuff! setting up hyperparams")
+
+#chars and mapping
+chars = sorted(list(set(text)))
+vocab_size = len(chars)
+stoi = { ch:i for i,ch in enumerate(chars) }
+itos = { i:ch for i,ch in enumerate(chars) }
+encode = lambda s: [stoi[c] for c in s] 
+decode = lambda l: ''.join([itos[i] for i in l]) 
+print("making human language understandable for my computer brain")
+
+# training and test data split
+data = torch.tensor(encode(text), dtype=torch.long)
+n = int(0.9*len(data))
+train_data = data[:n]
+val_data = data[n:]
+
+# data loading
+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([data[i:i+block_size] for i in ix])
+    y = torch.stack([data[i+1:i+block_size+1] for i in ix])
+    x, y = x.to(device), y.to(device)
+    return x, y
+@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)
+            logits, loss = model(X, Y)
+            losses[k] = loss.item()
+        out[split] = losses.mean()
+    model.train()
+    return out
+
+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
+
+# bigram model
+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, device=device))
+        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
+        
+model = BigramLanguageModel()
+model.load_state_dict(torch.load("state.skibidi",map_location = torch.device(device)))
+m = model.to(device)
+
+def generate_text(input_word, max_new_tokens=100):
+    model.eval()
+    input_indices = torch.tensor([encode(input_word)], dtype=torch.long, device=device)
+    generated_indices = model.generate(input_indices, max_new_tokens=max_new_tokens)
+    return decode(generated_indices[0].tolist())
+
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(label="Prompt", placeholder="W Sigma GPT according to critics"),
+        gr.Slider(minimum=1, maximum=1000, step=1, label="Number of characters to generate", value=100)
+    ],
+    outputs=gr.Textbox(label="Generated Text"),
+    title="RizzlerGPT",
+    description="Best GPT in Ohio"
+)
+
+if __name__ == "__main__":
+    iface.launch()
+
+print("running!")

requirements.txt

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+huggingface_hub==0.22.2
+gradio
+torch == 2.4.0