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app.py

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+"""
+Shakespeare Text Generator - Hugging Face Gradio App
+=====================================================
+A GPT model trained on Shakespeare's works to generate text in Shakespearean style.
+"""
+
+import gradio as gr
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+import math
+from dataclasses import dataclass
+import tiktoken
+
+# ============================================================================
+# MODEL ARCHITECTURE (Same as training)
+# ============================================================================
+
+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)
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
+        self.c_proj.NANGPT_SCALE_INIT = 1
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        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()
+        qkv = self.c_attn(x)
+        q, k, v = qkv.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)
+        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)
+        y = att @ v
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+        y = 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)
+        self.gelu = nn.GELU(approximate='tanh')
+        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd)
+        self.c_proj.NANOGPT_SCALE_INIT = 1
+
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = self.gelu(x)
+        x = self.c_proj(x)
+        return x
+
+class Block(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln_1 = nn.LayerNorm(config.n_embd)
+        self.attn = CausalSelfAttention(config)
+        self.ln_2 = nn.LayerNorm(config.n_embd)
+        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 = 50257
+    n_layer: int = 12
+    n_head: int = 12
+    n_embd: int = 768
+
+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),
+            h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
+            ln_f = nn.LayerNorm(config.n_embd),
+        ))
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        self.transformer.wte.weight = self.lm_head.weight
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            std = 0.02
+            if hasattr(module, 'NANGPT_SCALE_INIT'):
+                std *= (2 * self.config.n_layer) ** -0.5
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+            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):
+        B, T = idx.size()
+        assert T <= self.config.block_size
+        pos = torch.arange(0, T, dtype=torch.long, device=idx.device)
+        pos_emb = self.transformer.wpe(pos)
+        tok_emb = self.transformer.wte(idx)
+        x = tok_emb + pos_emb
+        for block in self.transformer.h:
+            x = block(x)
+        x = self.transformer.ln_f(x)
+        logits = self.lm_head(x)
+        loss = None
+        if targets is not None:
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
+        return logits, loss
+
+# ============================================================================
+# LOAD MODEL
+# ============================================================================
+
+print("Loading model...")
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+print(f"Using device: {device}")
+
+model = GPT(GPTConfig())
+# Load your trained checkpoint here
+checkpoint = torch.load('shakespeare_gpt_fp16.pt', map_location=device)
+model.load_state_dict({k: v.float() for k, v in checkpoint.items()})
+model.to(device)
+model.eval()
+
+enc = tiktoken.get_encoding('gpt2')
+
+# ============================================================================
+# GENERATION FUNCTION
+# ============================================================================
+
+def generate_shakespeare(prompt, max_length=100, temperature=0.8, top_k=50, num_samples=1):
+    """
+    Generate Shakespeare-style text from a prompt.
+    
+    Args:
+        prompt: Starting text
+        max_length: Maximum number of tokens to generate
+        temperature: Sampling temperature (higher = more random)
+        top_k: Number of top tokens to sample from
+        num_samples: Number of different samples to generate
+    """
+    if not prompt.strip():
+        return "Please enter a prompt to generate text."
+    
+    try:
+        # Encode the prompt
+        tokens = enc.encode(prompt)
+        if len(tokens) == 0:
+            return "Invalid prompt. Please try again."
+        
+        outputs = []
+        
+        for _ in range(num_samples):
+            x = torch.tensor(tokens, dtype=torch.long, device=device).unsqueeze(0)
+            
+            with torch.no_grad():
+                for _ in range(max_length):
+                    # Forward pass
+                    logits = model(x)[0]
+                    logits = logits[:, -1, :] / temperature
+                    
+                    # Top-k sampling
+                    probs = F.softmax(logits, dim=-1)
+                    topk_probs, topk_indices = torch.topk(probs, min(top_k, probs.size(-1)), dim=-1)
+                    ix = torch.multinomial(topk_probs, 1)
+                    xcol = torch.gather(topk_indices, -1, ix)
+                    x = torch.cat((x, xcol), dim=1)
+                    
+                    # Stop if we exceed block size
+                    if x.size(1) >= model.config.block_size:
+                        break
+            
+            # Decode the output
+            output_tokens = x[0].tolist()
+            generated_text = enc.decode(output_tokens)
+            outputs.append(generated_text)
+        
+        # Return all samples separated by dividers
+        if num_samples == 1:
+            return outputs[0]
+        else:
+            return "\n\n" + "="*60 + "\n\n".join(outputs)
+    
+    except Exception as e:
+        return f"Error generating text: {str(e)}"
+
+# ============================================================================
+# GRADIO INTERFACE
+# ============================================================================
+
+# Create the interface
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # 🎭 Shakespeare Text Generator
+        
+        Generate text in the style of William Shakespeare using a GPT model trained on his complete works.
+        Enter a prompt and watch the Bard's AI apprentice continue the story!
+        
+        **Model Details**: GPT-2 124M architecture trained on Shakespeare's plays and sonnets (Loss: 0.095)
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            prompt_input = gr.Textbox(
+                label="📝 Enter Your Prompt",
+                placeholder="To be or not to be...",
+                lines=4,
+                value="To be or not to be"
+            )
+            
+            with gr.Accordion("⚙️ Advanced Settings", open=False):
+                max_length_slider = gr.Slider(
+                    minimum=20,
+                    maximum=300,
+                    value=100,
+                    step=10,
+                    label="Max Length (tokens)",
+                    info="Maximum number of tokens to generate"
+                )
+                
+                temperature_slider = gr.Slider(
+                    minimum=0.1,
+                    maximum=1.5,
+                    value=0.8,
+                    step=0.1,
+                    label="Temperature",
+                    info="Higher = more creative, Lower = more focused"
+                )
+                
+                top_k_slider = gr.Slider(
+                    minimum=10,
+                    maximum=100,
+                    value=50,
+                    step=10,
+                    label="Top-K",
+                    info="Number of top tokens to sample from"
+                )
+                
+                num_samples_slider = gr.Slider(
+                    minimum=1,
+                    maximum=3,
+                    value=1,
+                    step=1,
+                    label="Number of Samples",
+                    info="Generate multiple variations"
+                )
+            
+            generate_btn = gr.Button("🎨 Generate", variant="primary", size="lg")
+        
+        with gr.Column(scale=1):
+            output_text = gr.Textbox(
+                label="📜 Generated Text",
+                lines=15
+            )
+    
+    # Examples
+    gr.Markdown("### 📚 Try These Examples:")
+    gr.Examples(
+        examples=[
+            ["To be or not to be", 100, 0.8, 50, 1],
+            ["What's in a name?", 120, 0.7, 40, 1],
+            ["All the world's a stage", 150, 0.9, 50, 1],
+            ["Romeo, Romeo, wherefore art thou", 100, 0.8, 50, 1],
+            ["Friends, Romans, countrymen", 130, 0.75, 45, 1],
+            ["Now is the winter of our discontent", 110, 0.85, 50, 1],
+        ],
+        inputs=[prompt_input, max_length_slider, temperature_slider, top_k_slider, num_samples_slider],
+        outputs=output_text,
+        fn=generate_shakespeare,
+        cache_examples=False
+    )
+    
+    # Connect the button
+    generate_btn.click(
+        fn=generate_shakespeare,
+        inputs=[prompt_input, max_length_slider, temperature_slider, top_k_slider, num_samples_slider],
+        outputs=output_text
+    )
+    
+    gr.Markdown(
+        """
+        ---
+        ### 💡 Tips for Best Results:
+        - Start with famous Shakespeare quotes for coherent continuations
+        - Use **lower temperature** (0.5-0.7) for more focused, coherent text
+        - Use **higher temperature** (0.9-1.2) for more creative, diverse outputs
+        - Adjust **Top-K** to control vocabulary diversity
+        - Try generating multiple samples to see different variations
+        
+        ### ⚠️ Note:
+        This model was trained on Shakespeare's works and will generate text in Early Modern English style.
+        Results may vary based on the prompt and parameters.
+        """
+    )
+
+# ============================================================================
+# LAUNCH
+# ============================================================================
+
+if __name__ == "__main__":
+    demo.launch(share=False)

requirements.txt

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+torch>=2.0.0
+gradio>=4.0.0
+tiktoken>=0.5.0

shakespeare_gpt_fp16.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d9b79c5d5951dc9234779974e12132032f76cecc0a2f1d99a33a8cf2c803506f
+size 351291857