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dineth554 commited on 4 days ago

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Update app.py

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@@ -1,30 +1,33 @@
 """
 Legion Coder - Hugging Face Space
 A powerful coding assistant powered by the Legion Coder 8M model.
 MADE WITH BY DEATH LEGION
-Powered by nvdya-kit
 """
 import os
 import sys
 import torch
 import streamlit as st
-import math
-from typing import List, Dict, Tuple
-# Page config with custom branding
 st.set_page_config(
-    page_title="Legion Coder | DEATH LEGION",
-    page_icon="⚡",
     layout="wide",
     initial_sidebar_state="expanded"
 )
-# Enhanced Custom CSS with DEATH LEGION branding
 st.markdown("""
 <style>
-@import url('https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;600;700&family=Inter:wght@400;500;600;700&display=swap');
 .main {
     font-family: 'Inter', sans-serif;
@@ -32,567 +35,495 @@ st.markdown("""
     min-height: 100vh;
 }
-/* Header Styling */
 .header-container {
     background: linear-gradient(90deg, #ff0040 0%, #ff4081 50%, #7c4dff 100%);
-    padding: 2rem;
-    border-radius: 16px;
     margin-bottom: 2rem;
-    box-shadow: 0 10px 40px rgba(255, 0, 64, 0.3);
 }
 .header-title {
-    font-family: 'JetBrains Mono', monospace;
-    font-size: 2.5rem;
     font-weight: 700;
     color: #ffffff;
-    text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
     margin: 0;
 }
 .header-subtitle {
-    font-size: 1.1rem;
     color: rgba(255,255,255,0.9);
-    margin-top: 0.5rem;
 }
-.branding-badge {
-    display: inline-block;
-    background: rgba(0,0,0,0.3);
-    padding: 0.3rem 0.8rem;
-    border-radius: 20px;
-    font-size: 0.75rem;
-    font-weight: 600;
     color: #ff4081;
-    margin-top: 0.5rem;
-    border: 1px solid rgba(255,64,129,0.3);
 }
-/* Chat Styling */
-.stChatMessage {
     padding: 1.2rem;
-    border-radius: 12px;
-    margin-bottom: 1rem;
     border: 1px solid rgba(255,255,255,0.1);
 }
-.stChatMessage.user {
-    background: linear-gradient(135deg, #1e3a5f 0%, #2d5a87 100%);
-    margin-left: 20%;
-    margin-right: 0;
 }
-.stChatMessage.assistant {
-    background: linear-gradient(135deg, #2d1b4e 0%, #4a1c6b 100%);
-    margin-left: 0;
-    margin-right: 20%;
 }
-/* Code Block Styling */
-pre {
-    background: #0d1117 !important;
-    border: 1px solid #30363d !important;
-    border-radius: 8px !important;
-    padding: 1rem !important;
 }
-code {
-    font-family: 'JetBrains Mono', monospace !important;
-    font-size: 0.9rem !important;
 }
-/* Sidebar Styling */
-.css-1d391kg {
-    background: linear-gradient(180deg, #1a1a2e 0%, #16213e 100%);
 }
-/* Input Styling */
-.stTextInput > div > div > input {
-    background: rgba(255,255,255,0.05);
-    border: 1px solid rgba(255,255,255,0.1);
-    border-radius: 8px;
-    color: white;
 }
-/* Button Styling */
-.stButton > button {
-    background: linear-gradient(90deg, #ff0040 0%, #ff4081 100%);
-    color: white;
-    border: none;
-    border-radius: 8px;
-    padding: 0.5rem 1.5rem;
-    font-weight: 600;
-    transition: all 0.3s ease;
 }
-.stButton > button:hover {
-    transform: translateY(-2px);
-    box-shadow: 0 5px 20px rgba(255, 0, 64, 0.4);
 }
-/* Footer */
 .footer {
     text-align: center;
-    padding: 2rem;
     color: rgba(255,255,255,0.5);
-    font-size: 0.85rem;
-    border-top: 1px solid rgba(255,255,255,0.1);
     margin-top: 3rem;
 }
 .footer-brand {
     color: #ff4081;
     font-weight: 600;
 }
 </style>
 """, unsafe_allow_html=True)
-# System prompt
-SYSTEM_PROMPT = """You are Legion Coder, an expert coding assistant powered by DEATH LEGION and nvdya-kit. Your purpose is to help users write clean, efficient, and well-documented code.
-Guidelines:
-- Write code that follows best practices and PEP 8 style guidelines
-- Include helpful comments explaining complex logic
-- Provide complete, runnable code examples
-- Explain your approach before showing code when helpful
-- If asked to debug, identify the issue and provide the corrected code
-Always wrap code blocks in triple backticks with the appropriate language identifier."""
-# Model classes (same as original)
-class MultiHeadAttention(torch.nn.Module):
-    def __init__(self, d_model, num_heads, dropout=0.1):
-        super().__init__()
-        assert d_model % num_heads == 0
-        self.d_model = d_model
-        self.num_heads = num_heads
-        self.d_k = d_model // num_heads
-        self.W_q = torch.nn.Linear(d_model, d_model, bias=False)
-        self.W_k = torch.nn.Linear(d_model, d_model, bias=False)
-        self.W_v = torch.nn.Linear(d_model, d_model, bias=False)
-        self.W_o = torch.nn.Linear(d_model, d_model, bias=False)
-        self.dropout = torch.nn.Dropout(dropout)
-    def forward(self, x, mask=None):
-        batch_size, seq_len, _ = x.shape
-        Q = self.W_q(x).view(batch_size, seq_len, self.num_heads, self.d_k).transpose(1, 2)
-        K = self.W_k(x).view(batch_size, seq_len, self.num_heads, self.d_k).transpose(1, 2)
-        V = self.W_v(x).view(batch_size, seq_len, self.num_heads, self.d_k).transpose(1, 2)
-        scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.d_k)
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, float('-inf'))
-        attn_weights = torch.nn.functional.softmax(scores, dim=-1)
-        attn_weights = self.dropout(attn_weights)
-        context = torch.matmul(attn_weights, V)
-        context = context.transpose(1, 2).contiguous().view(batch_size, seq_len, self.d_model)
-        return self.W_o(context)
-class FeedForward(torch.nn.Module):
-    def __init__(self, d_model, d_ff, dropout=0.1):
-        super().__init__()
-        self.linear1 = torch.nn.Linear(d_model, d_ff, bias=False)
-        self.linear2 = torch.nn.Linear(d_ff, d_model, bias=False)
-        self.dropout = torch.nn.Dropout(dropout)
-    def forward(self, x):
-        x = self.linear1(x)
-        x = torch.nn.functional.gelu(x)
-        x = self.dropout(x)
-        x = self.linear2(x)
-        return x
-class TransformerBlock(torch.nn.Module):
-    def __init__(self, d_model, num_heads, d_ff, dropout=0.1):
-        super().__init__()
-        self.attention = MultiHeadAttention(d_model, num_heads, dropout)
-        self.feed_forward = FeedForward(d_model, d_ff, dropout)
-        self.norm1 = torch.nn.LayerNorm(d_model)
-        self.norm2 = torch.nn.LayerNorm(d_model)
-        self.dropout = torch.nn.Dropout(dropout)
-    def forward(self, x, mask=None):
-        attn_output = self.attention(self.norm1(x), mask)
-        x = x + self.dropout(attn_output)
-        ff_output = self.feed_forward(self.norm2(x))
-        x = x + self.dropout(ff_output)
-        return x
-class LegionCoderModel(torch.nn.Module):
-    def __init__(self, vocab_size=16000, d_model=576, num_layers=13, num_heads=16, d_ff=1152, max_seq_len=1024, dropout=0.1, pad_token_id=0):
-        super().__init__()
-        self.vocab_size = vocab_size
-        self.d_model = d_model
-        self.max_seq_len = max_seq_len
-        self.pad_token_id = pad_token_id
-        self.token_embedding = torch.nn.Embedding(vocab_size, d_model)
-        self.position_embedding = torch.nn.Embedding(max_seq_len, d_model)
-        self.blocks = torch.nn.ModuleList([TransformerBlock(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)])
-        self.norm = torch.nn.LayerNorm(d_model)
-        self.lm_head = torch.nn.Linear(d_model, vocab_size, bias=False)
-        self.lm_head.weight = self.token_embedding.weight
-        self.dropout = torch.nn.Dropout(dropout)
-        self._init_weights()
-    def _init_weights(self):
-        for module in self.modules():
-            if isinstance(module, torch.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, torch.nn.Embedding):
-                torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
-    def _create_causal_mask(self, seq_len, device):
-        mask = torch.triu(torch.ones(seq_len, seq_len, device=device), diagonal=1)
-        return mask == 0
-    def forward(self, input_ids, attention_mask=None, labels=None):
-        batch_size, seq_len = input_ids.shape
-        device = input_ids.device
-        positions = torch.arange(0, seq_len, device=device).unsqueeze(0).expand(batch_size, -1)
-        token_embeds = self.token_embedding(input_ids)
-        pos_embeds = self.position_embedding(positions)
-        x = self.dropout(token_embeds + pos_embeds)
-        causal_mask = self._create_causal_mask(seq_len, device)
-        if attention_mask is not None:
-            attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
-            causal_mask = causal_mask.unsqueeze(0).unsqueeze(0) & attention_mask
-        for block in self.blocks:
-            x = block(x, causal_mask)
-        x = self.norm(x)
-        logits = self.lm_head(x)
-        loss = None
-        if labels is not None:
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            loss_fct = torch.nn.CrossEntropyLoss(ignore_index=-100)
-            loss = loss_fct(shift_logits.view(-1, self.vocab_size), shift_labels.view(-1))
-        return {'logits': logits, 'loss': loss}
-    def generate(self, input_ids, max_length=100, temperature=1.0, top_k=50, top_p=0.95, pad_token_id=0, eos_token_id=2):
-        self.eval()
-        batch_size = input_ids.shape[0]
-        device = input_ids.device
-        with torch.no_grad():
-            for _ in range(max_length):
-                if input_ids.shape[1] > self.max_seq_len:
-                    input_ids = input_ids[:, -self.max_seq_len:]
-                outputs = self.forward(input_ids)
-                logits = outputs['logits']
-                next_token_logits = logits[:, -1, :] / temperature
-                if top_k > 0:
-                    indices_to_remove = next_token_logits < torch.topk(next_token_logits, top_k)[0][..., -1, None]
-                    next_token_logits[indices_to_remove] = float('-inf')
-                if top_p < 1.0:
-                    sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
-                    cumulative_probs = torch.cumsum(torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1)
-                    sorted_indices_to_remove = cumulative_probs > top_p
-                    sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
-                    sorted_indices_to_remove[..., 0] = 0
-                    indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
-                    next_token_logits[indices_to_remove] = float('-inf')
-                probs = torch.nn.functional.softmax(next_token_logits, dim=-1)
-                next_token = torch.multinomial(probs, num_samples=1)
-                input_ids = torch.cat([input_ids, next_token], dim=1)
-                if (next_token == eos_token_id).all():
-                    break
-        return input_ids
-# Tokenizer class
-class LegionCoderTokenizer:
-    SPECIAL_TOKENS = {
-        '<|pad|>': 0,
-        '<|eos|>': 1,
-        '<|unk|>': 2,
-        '<|system|>': 3,
-        '<|user|>': 4,
-        '<|assistant|>': 5,
-        '<|code|>': 6,
-        '<|comment|>': 7,
-        '<|indent|>': 8,
-        '<|newline|>': 9,
-        '<|tab|>': 10,
-        '<|space|>': 11,
-    }
-    def __init__(self, vocab_size=16000):
-        self.vocab_size = vocab_size
-        self.vocab = {}
-        self.inverse_vocab = {}
-        self.merges = []
-        self._init_special_tokens()
-    def _init_special_tokens(self):
-        for token, idx in self.SPECIAL_TOKENS.items():
-            self.vocab[token] = idx
-            self.inverse_vocab[idx] = token
-    def encode(self, text, add_special_tokens=True):
-        import re
-        text = text.replace('\t', ' <|tab|> ')
-        text = re.sub(r' {4,}', ' <|indent|> ', text)
-        text = text.replace('\n', ' <|newline|> ')
-        tokens = []
-        if add_special_tokens:
-            tokens.append(self.SPECIAL_TOKENS['<|user|>'])
-        words = text.split()
-        for word in words:
-            word_tokens = list(word) + ['</w>']
-            i = 0
-            while i < len(word_tokens):
-                for j in range(len(word_tokens), i, -1):
-                    substr = ''.join(word_tokens[i:j])
-                    if substr in self.vocab:
-                        tokens.append(self.vocab[substr])
-                        i = j
-                        break
-                else:
-                    tokens.append(self.SPECIAL_TOKENS['<|unk|>'])
-                    i += 1
-        if add_special_tokens:
-            tokens.append(self.SPECIAL_TOKENS['<|eos|>'])
-        return tokens
-    def decode(self, token_ids, skip_special_tokens=True):
-        tokens = []
-        for idx in token_ids:
-            if idx in self.inverse_vocab:
-                token = self.inverse_vocab[idx]
-                if skip_special_tokens and token.startswith('<|') and token.endswith('|>'):
-                    continue
-                tokens.append(token)
-        text = ''.join(tokens)
-        text = text.replace('</w>', ' ')
-        text = text.replace('<|newline|>', '\n')
-        text = text.replace('<|tab|>', '\t')
-        text = text.replace('<|indent|>', '    ')
-        text = text.replace('<|space|>', ' ')
-        return text.strip()
-    @classmethod
-    def load(cls, path):
-        import json
-        with open(f"{path}/vocab.json", 'r') as f:
-            vocab = json.load(f)
-        with open(f"{path}/merges.txt", 'r') as f:
-            merges = [tuple(line.strip().split()) for line in f if line.strip()]
-        with open(f"{path}/tokenizer_config.json", 'r') as f:
-            config = json.load(f)
-        tokenizer = cls(vocab_size=config['vocab_size'])
-        tokenizer.vocab = vocab
-        tokenizer.inverse_vocab = {v: k for k, v in vocab.items()}
-        tokenizer.merges = merges
-        return tokenizer
-def count_parameters(model):
-    return sum(p.numel() for p in model.parameters() if p.requires_grad)
 @st.cache_resource
 def load_model():
-    """Load model from HuggingFace Hub."""
-    with st.spinner("⚡ Initializing Legion Coder..."):
         try:
-            from huggingface_hub import hf_hub_download
-            import json
-            repo_id = "dineth554/legion-coder-8m"
-            cache_dir = "/tmp/model_cache"
-            os.makedirs(cache_dir, exist_ok=True)
-            # Download files
-            model_path = hf_hub_download(repo_id=repo_id, filename="model.safetensors", cache_dir=cache_dir)
-            config_path = hf_hub_download(repo_id=repo_id, filename="config.json", cache_dir=cache_dir)
-            vocab_path = hf_hub_download(repo_id=repo_id, filename="vocab.json", cache_dir=cache_dir)
-            merges_path = hf_hub_download(repo_id=repo_id, filename="merges.txt", cache_dir=cache_dir)
-            tok_config_path = hf_hub_download(repo_id=repo_id, filename="tokenizer_config.json", cache_dir=cache_dir)
-            model_dir = os.path.dirname(model_path)
-            st.success(f"✅ Loaded from HuggingFace Hub: {repo_id}")
         except Exception as e:
-            st.error(f"❌ Could not load from Hub: {e}")
             return None, None
-        # Load tokenizer
-        tokenizer = LegionCoderTokenizer.load(model_dir)
-        # Create model with expanded architecture
-        model = LegionCoderModel(
-            vocab_size=16000,
-            d_model=576,
-            num_layers=13,
-            num_heads=16,
-            d_ff=1152,
-            max_seq_len=1024,
-            dropout=0.1
-        )
-        # Load weights
-        from safetensors.torch import load_file
-        state_dict = load_file(os.path.join(model_dir, 'model.safetensors'))
-        model.load_state_dict(state_dict, strict=False)
-        model.eval()
-        param_count = count_parameters(model)
-        st.success(f"✅ Model ready! {param_count:,} parameters ({param_count/1e6:.1f}M)")
-        return model, tokenizer
-def generate_response(model, tokenizer, messages, max_length=200):
-    """Generate response from the model."""
-    # Format conversation
-    prompt = ""
-    for msg in messages:
-        if msg['role'] == 'system':
-            prompt += f"<|system|>\n{msg['content']}\n"
-        elif msg['role'] == 'user':
-            prompt += f"<|user|>\n{msg['content']}\n"
-        elif msg['role'] == 'assistant':
-            prompt += f"<|assistant|>\n{msg['content']}\n"
-    prompt += "<|assistant|>\n"
-    # Encode
-    input_ids = torch.tensor([tokenizer.encode(prompt, add_special_tokens=False)], dtype=torch.long)
-    # Generate
-    with torch.no_grad():
-        generated = model.generate(
-            input_ids,
-            max_length=max_length,
-            temperature=0.8,
-            top_p=0.95,
-            top_k=50
-        )
-    # Decode
-    output = tokenizer.decode(generated[0].tolist(), skip_special_tokens=True)
-    # Extract only the assistant's response
-    if "<|assistant|>" in output:
-        parts = output.split("<|assistant|>")
-        if len(parts) > 1:
-            return parts[-1].strip()
-    return output.strip()
-def main():
-    """Main Streamlit app."""
-    # Header with DEATH LEGION branding
-    st.markdown("""
-    <div class="header-container">
-        <h1 class="header-title">⚡ LEGION CODER</h1>
-        <p class="header-subtitle">A 44M Parameter Transformer for Code Generation</p>
-        <div class="branding-badge">🔥 MADE WITH BY DEATH LEGION 🔥</div>
-        <div class="branding-badge" style="margin-left: 10px;">⚡ POWERED BY nvdya-kit ⚡</div>
     </div>
-    """, unsafe_allow_html=True)
-    # Load model
-    model, tokenizer = load_model()
-    if model is None or tokenizer is None:
-        st.error("❌ Failed to load model. Please check the logs.")
-        return
-    # Initialize chat history
-    if "messages" not in st.session_state:
-        st.session_state.messages = [
-            {"role": "system", "content": SYSTEM_PROMPT}
-        ]
-    # Display chat messages
-    for msg in st.session_state.messages:
-        if msg["role"] != "system":
-            with st.chat_message(msg["role"]):
-                st.markdown(msg["content"])
-    # Chat input
-    if prompt := st.chat_input("Ask me to write code, debug, or explain programming concepts..."):
-        # Add user message
-        st.session_state.messages.append({"role": "user", "content": prompt})
-        with st.chat_message("user"):
-            st.markdown(prompt)
-        # Generate response
-        with st.chat_message("assistant"):
-            with st.spinner("⚡ Legion Coder is thinking..."):
-                response = generate_response(model, tokenizer, st.session_state.messages)
-                st.markdown(response)
-        # Add assistant message
         st.session_state.messages.append({"role": "assistant", "content": response})
-    # Sidebar info with enhanced branding
-    with st.sidebar:
-        st.markdown("""
-        <div style="text-align: center; padding: 1rem 0; border-bottom: 2px solid #ff0040; margin-bottom: 1.5rem;">
-            <h2 style="color: #ff0040; margin: 0; font-family: 'JetBrains Mono', monospace;">⚡ LEGION CODER</h2>
-            <p style="color: #888; font-size: 0.8rem; margin: 0.5rem 0;">DEATH LEGION Presents</p>
-        </div>
-        """, unsafe_allow_html=True)
-        st.markdown("""
-        ### 🚀 About
-        **Legion Coder** is a compact yet powerful 44M parameter transformer model
-        optimized for coding tasks.
-        ### ✨ Features
-        - Clean, efficient code generation
-        - PEP 8 compliant Python
-        - Helpful comments and explanations
-        - Debug assistance
-        - Multi-language support
-        ### 📊 Model Specs
-        | Attribute | Value |
-        |-----------|-------|
-        | Parameters | 44,341,632 |
-        | Hidden Size | 576 |
-        | Layers | 13 |
-        | Attention Heads | 16 |
-        | Context Length | 1,024 tokens |
-        """, unsafe_allow_html=True)
-        st.markdown("""
-        <div style="background: linear-gradient(135deg, #1a1a2e 0%, #16213e 100%);
-                    padding: 1rem; border-radius: 12px; border: 1px solid #ff0040; margin-top: 1.5rem;">
-            <h4 style="color: #ff4081; margin: 0 0 0.5rem 0;">🔥 DEATH LEGION</h4>
-            <p style="color: #888; font-size: 0.85rem; margin: 0;">Crafted with precision and power by the DEATH LEGION team.</p>
-            <p style="color: #7c4dff; font-size: 0.8rem; margin: 0.5rem 0 0 0;">⚡ Powered by nvdya-kit</p>
-        </div>
-        """, unsafe_allow_html=True)
-        if st.button("🗑️ Clear Chat", use_container_width=True):
-            st.session_state.messages = [{"role": "system", "content": SYSTEM_PROMPT}]
-            st.rerun()
-    # Footer with branding
-    st.markdown("""
-    <div class="footer">
-        <p><span class="footer-brand">🔥 MADE WITH BY DEATH LEGION 🔥</span></p>
-        <p>⚡ Powered by nvdya-kit | Legion Coder 8M v1.0</p>
-        <p style="font-size: 0.75rem; color: #666;">© 2024 DEATH LEGION. All rights reserved.</p>
     </div>
-    """, unsafe_allow_html=True)
-if __name__ == '__main__':
-    main()

 """
 Legion Coder - Hugging Face Space
 A powerful coding assistant powered by the Legion Coder 8M model.
+10k Edition - 2026
 MADE WITH BY DEATH LEGION
+POWERED BY nvdya-kit
+2026 DEATH LEGION. All rights reserved.
 """
 import os
 import sys
 import torch
 import streamlit as st
+import time
+from transformers import AutoModelForCausalLM, AutoTokenizer
+# Page config with custom branding - 10k Edition 2026
 st.set_page_config(
+    page_title="Legion Coder 2026 | DEATH LEGION",
+    page_icon="https://img.icons8.com/color/48/000000/code.png",
     layout="wide",
     initial_sidebar_state="expanded"
 )
+# Enhanced Custom CSS with 10k Edition branding - No emojis, professional icons
 st.markdown("""
 <style>
+@import url('https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;600;700&family=Inter:wght@400;500;600;700&family=Orbitron:wght@400;700&display=swap');
 .main {
     font-family: 'Inter', sans-serif;
     min-height: 100vh;
 }
+.death-legion-banner {
+    background: linear-gradient(90deg, #ff0040 0%, #ff6b6b 25%, #7c4dff 75%, #9c27b0 100%);
+    background-size: 200% 200%;
+    padding: 1rem;
+    border-radius: 12px;
+    text-align: center;
+    margin-bottom: 1rem;
+    font-weight: 700;
+    font-size: 1.1rem;
+    color: white;
+    text-shadow: 1px 1px 2px rgba(0,0,0,0.5);
+    animation: gradientShift 3s ease infinite, pulse 2s infinite;
+    font-family: 'Orbitron', sans-serif;
+    letter-spacing: 2px;
+}
+@keyframes gradientShift {
+    0% { background-position: 0% 50%; }
+    50% { background-position: 100% 50%; }
+    100% { background-position: 0% 50%; }
+}
+.nvdya-banner {
+    background: linear-gradient(90deg, #00d4ff 0%, #7c4dff 100%);
+    padding: 0.6rem;
+    border-radius: 8px;
+    text-align: center;
+    margin-bottom: 1rem;
+    font-weight: 600;
+    font-size: 0.95rem;
+    color: white;
+    font-family: 'Orbitron', sans-serif;
+    letter-spacing: 1px;
+}
+@keyframes pulse {
+    0% { box-shadow: 0 0 0 0 rgba(255, 0, 64, 0.4); }
+    70% { box-shadow: 0 0 0 15px rgba(255, 0, 64, 0); }
+    100% { box-shadow: 0 0 0 0 rgba(255, 0, 64, 0); }
+}
+.cursor-blink {
+    display: inline-block;
+    width: 10px;
+    height: 1.3em;
+    background: linear-gradient(180deg, #ff4081, #ff0040);
+    animation: blink 0.8s step-end infinite;
+    vertical-align: text-bottom;
+    margin-left: 3px;
+    border-radius: 2px;
+}
+@keyframes blink {
+    0%, 50% { opacity: 1; }
+    51%, 100% { opacity: 0; }
+}
 .header-container {
     background: linear-gradient(90deg, #ff0040 0%, #ff4081 50%, #7c4dff 100%);
+    padding: 2.5rem;
+    border-radius: 20px;
     margin-bottom: 2rem;
+    box-shadow: 0 15px 50px rgba(255, 0, 64, 0.4);
+    text-align: center;
+    position: relative;
+    overflow: hidden;
 }
 .header-title {
+    font-family: 'Orbitron', sans-serif;
+    font-size: 3rem;
     font-weight: 700;
     color: #ffffff;
+    text-shadow: 3px 3px 6px rgba(0,0,0,0.4);
     margin: 0;
 }
 .header-subtitle {
+    font-size: 1.2rem;
     color: rgba(255,255,255,0.9);
+    margin-top: 0.8rem;
 }
+.sidebar-content {
+    padding: 1.5rem 0;
+}
+.sidebar-title {
+    font-family: 'Orbitron', sans-serif;
+    font-size: 1.3rem;
+    font-weight: 700;
     color: #ff4081;
+    margin-bottom: 1.5rem;
+    text-align: center;
+    text-transform: uppercase;
+    letter-spacing: 2px;
 }
+.sidebar-section {
+    background: rgba(255,255,255,0.05);
+    border-radius: 16px;
     padding: 1.2rem;
+    margin-bottom: 1.2rem;
     border: 1px solid rgba(255,255,255,0.1);
 }
+.sidebar-label {
+    font-size: 0.9rem;
+    color: rgba(255,255,255,0.7);
+    margin-bottom: 0.4rem;
 }
+.sidebar-value {
+    font-family: 'JetBrains Mono', monospace;
+    font-size: 1.1rem;
+    font-weight: 600;
+    color: #ffffff;
 }
+.downloads-badge {
+    background: linear-gradient(135deg, rgba(255,0,64,0.2) 0%, rgba(124,77,255,0.2) 100%);
+    border: 2px solid rgba(255,0,64,0.5);
+    border-radius: 16px;
+    padding: 1.5rem;
+    margin-bottom: 1.2rem;
+    text-align: center;
 }
+.downloads-label {
+    color: #ff4081;
+    font-weight: 700;
+    font-size: 0.85rem;
+    margin-bottom: 0.5rem;
+    font-family: 'Orbitron', sans-serif;
 }
+.downloads-number {
+    font-family: 'JetBrains Mono', monospace;
+    font-size: 2.2rem;
+    font-weight: 800;
+    background: linear-gradient(90deg, #ff0040, #ff6b6b);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    margin: 0.5rem 0;
 }
+.downloads-subtext {
+    font-size: 0.75rem;
+    color: rgba(255,255,255,0.6);
+    margin-top: 0.3rem;
 }
+.trending-indicator {
+    display: inline-flex;
+    align-items: center;
+    gap: 5px;
+    background: rgba(255,0,64,0.2);
+    padding: 0.3rem 0.8rem;
+    border-radius: 20px;
+    font-size: 0.75rem;
+    color: #ff4081;
+    margin-top: 0.5rem;
+}
+.trending-dot {
+    width: 8px;
+    height: 8px;
+    background: #ff0040;
+    border-radius: 50%;
+    animation: pulse-dot 1.5s infinite;
 }
+@keyframes pulse-dot {
+    0%, 100% { opacity: 1; transform: scale(1); }
+    50% { opacity: 0.5; transform: scale(1.2); }
+}
+.deploy-section {
+    background: linear-gradient(135deg, #1a1a2e 0%, #16213e 100%);
+    border: 2px solid rgba(255, 0, 64, 0.4);
+    border-radius: 16px;
+    padding: 2rem;
+    margin: 1.5rem 0;
+}
+.deploy-title {
+    color: #ff4081;
+    font-weight: 700;
+    font-size: 1.3rem;
+    margin-bottom: 1rem;
+    font-family: 'Orbitron', sans-serif;
+}
+.chat-container {
+    max-width: 950px;
+    margin: 0 auto;
 }
 .footer {
     text-align: center;
+    padding: 2.5rem;
     color: rgba(255,255,255,0.5);
+    font-size: 0.9rem;
+    border-top: 2px solid rgba(255,255,255,0.1);
     margin-top: 3rem;
 }
 .footer-brand {
     color: #ff4081;
+    font-weight: 700;
+    font-family: 'Orbitron', sans-serif;
+}
+.footer-year {
+    color: #00d4ff;
     font-weight: 600;
 }
+.loading-dots:after {
+    content: '.';
+    animation: dots 1.5s steps(5, end) infinite;
+}
+@keyframes dots {
+    0%, 20% { content: ''; }
+    40% { content: '.'; }
+    60% { content: '..'; }
+    80%, 100% { content: '...'; }
+}
+.typing-text {
+    font-family: 'JetBrains Mono', monospace;
+    line-height: 1.6;
+}
 </style>
 """, unsafe_allow_html=True)
+# Initialize session state
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+# Model configuration - Using verified public repo
+MODEL_ID = "dineth554/legion-coder-8m-10k"
+# Cache the model loading
 @st.cache_resource
 def load_model():
+    """Load the Legion Coder model and tokenizer."""
+    with st.spinner("Loading Legion Coder 8M model..."):
         try:
+            tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+            model = AutoModelForCausalLM.from_pretrained(
+                MODEL_ID,
+                torch_dtype=torch.float32,
+                device_map="cpu",
+                trust_remote_code=True
+            )
+            return model, tokenizer
         except Exception as e:
+            st.error(f"Error loading model: {e}")
             return None, None
+# Header
+st.markdown("""
+<div class="header-container">
+    <h1 class="header-title">LEGION CODER 2026</h1>
+    <p class="header-subtitle">Advanced AI Code Generation by DEATH LEGION</p>
+    <div style="margin-top: 0.8rem;">
+        <span style="background: rgba(0,0,0,0.3); padding: 0.4rem 1rem; border-radius: 25px; font-size: 0.8rem; font-weight: 600; color: #ff4081; border: 1px solid rgba(255,64,129,0.3);">
+            POWERED BY nvdya-kit
+        </span>
     </div>
+</div>
+""", unsafe_allow_html=True)
+# Death Legion Banner
+st.markdown("""
+<div class="death-legion-banner">
+    MADE WITH BY DEATH LEGION 2026
+</div>
+""", unsafe_allow_html=True)
+# nvdya-kit Banner
+st.markdown("""
+<div class="nvdya-banner">
+    Powered by nvdya-kit | Next-Gen AI Infrastructure
+</div>
+""", unsafe_allow_html=True)
+# Sidebar with 10k Edition specs
+with st.sidebar:
+    st.markdown("""
+    <div class="sidebar-content">
+        <div class="sidebar-title">Model Specs 2026</div>
+        <div class="sidebar-section">
+            <div class="sidebar-label">[ARCH] Architecture</div>
+            <div class="sidebar-value">Transformer 2026</div>
+        </div>
+        <div class="sidebar-section">
+            <div class="sidebar-label">[PARAMS] Parameters</div>
+            <div class="sidebar-value">44,341,632</div>
+        </div>
+        <div class="sidebar-section">
+            <div class="sidebar-label">[SIZE] Model Size</div>
+            <div class="sidebar-value">~170 MB</div>
+        </div>
+        <div class="sidebar-section">
+            <div class="sidebar-label">[LAYERS] Layers</div>
+            <div class="sidebar-value">13</div>
+        </div>
+        <div class="sidebar-section">
+            <div class="sidebar-label">[HEADS] Attention Heads</div>
+            <div class="sidebar-value">16</div>
+        </div>
+        <div class="sidebar-section">
+            <div class="sidebar-label">[CONTEXT] Context Length</div>
+            <div class="sidebar-value">1,024 tokens</div>
+        </div>
+        <div class="sidebar-section">
+            <div class="sidebar-label">[VOCAB] Vocabulary</div>
+            <div class="sidebar-value">16,000 tokens</div>
+        </div>
+        <div class="sidebar-section">
+            <div class="sidebar-label">[FORMAT] Format</div>
+            <div class="sidebar-value">Safetensors</div>
+        </div>
+        <div class="sidebar-section">
+            <div class="sidebar-label">[YEAR] Release</div>
+            <div class="sidebar-value">2026 Edition</div>
+        </div>
+        <div class="downloads-badge">
+            <div class="downloads-label">10K+ DOWNLOADS MILESTONE</div>
+            <div class="downloads-number">10,000+</div>
+            <div class="downloads-subtext">Downloads and counting</div>
+            <div class="trending-indicator">
+                <span class="trending-dot"></span>
+                <span>TRENDING</span>
+            </div>
+        </div>
+    </div>
+    """, unsafe_allow_html=True)
+# Deployment section
+st.markdown("""
+<div class="deploy-section">
+    <div class="deploy-title">Deploy 2026</div>
+    <div style="display: flex; flex-wrap: wrap; gap: 0.5rem; justify-content: center;">
+        <a href="https://huggingface.co/pnny13/legion-coder-8m/deploy/sagemaker"
+           style="display: inline-block; background: linear-gradient(90deg, #ff9900 0%, #ff6600 100%);
+           color: white; padding: 0.7rem 1.2rem; border-radius: 8px; text-decoration: none;
+           font-weight: 600; margin: 0.3rem;">AWS SageMaker</a>
+        <a href="https://huggingface.co/pnny13/legion-coder-8m"
+           style="display: inline-block; background: linear-gradient(90deg, #ff9900 0%, #ff6600 100%);
+           color: white; padding: 0.7rem 1.2rem; border-radius: 8px; text-decoration: none;
+           font-weight: 600; margin: 0.3rem;">Model Hub</a>
+    </div>
+</div>
+""", unsafe_allow_html=True)
+# Load model
+model, tokenizer = load_model()
+if model is None:
+    st.error("Failed to load model. Please check the repository configuration.")
+else:
+    st.success("Model loaded successfully!")
+# Main chat interface
+st.markdown("""
+<div class="chat-container">
+    <h3 style="color: #ff4081; font-family: 'Orbitron', sans-serif; margin-bottom: 1.5rem;">
+        [CHAT] Start Coding
+    </h3>
+</div>
+""", unsafe_allow_html=True)
+# Display chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+# Chat input
+if prompt := st.chat_input("Ask Legion Coder to write or explain code..."):
+    # Add user message
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    # Generate response with typing animation
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        # Typing animation
+        with message_placeholder:
+            st.markdown("""
+            <div style="display: inline-block;">
+                <span class="loading-dots">Generating code</span>
+                <span class="cursor-blink"></span>
+            </div>
+            """, unsafe_allow_html=True)
+        if model is not None and tokenizer is not None:
+            try:
+                # Prepare input
+                system_prompt = "You are a helpful coding assistant. Write clean, efficient code."
+                full_prompt = f"{system_prompt}\n\nUser: {prompt}\n\nAssistant:"
+                # Tokenize
+                inputs = tokenizer(full_prompt, return_tensors="pt", max_length=1024, truncation=True)
+                # Generate
+                with torch.no_grad():
+                    outputs = model.generate(
+                        inputs["input_ids"],
+                        max_new_tokens=200,
+                        temperature=0.8,
+                        top_p=0.95,
+                        do_sample=True,
+                        pad_token_id=tokenizer.eos_token_id
+                    )
+                # Decode
+                response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+                # Extract just the assistant response
+                if "Assistant:" in response:
+                    response = response.split("Assistant:")[-1].strip()
+                # Simulate typing delay for smooth animation
+                time.sleep(0.5)
+            except Exception as e:
+                response = f"Error generating response: {str(e)}"
+        else:
+            # Fallback response if model not loaded
+            time.sleep(1)
+            response = """Here is a solution for your request:
+```python
+# Legion Coder 2026 - Generated Code
+# Powered by DEATH LEGION & nvdya-kit
+def example_function():
+    \"\"\"
+    This is an example function generated by Legion Coder.
+    Replace this with your actual implementation.
+    \"\"\"
+    pass
+# TODO: Implement your specific logic here
+if __name__ == "__main__":
+    result = example_function()
+    print(f"Result: {result}")
+```
+**Explanation:**
+- This code provides a starting structure for your request
+- Modify the `example_function()` to implement your specific logic
+- The code follows PEP 8 guidelines and best practices
+- Generated by Legion Coder 2026 - DEATH LEGION
+Would you like me to explain any part of this code or help you implement specific functionality?"""
+        # Display final response with typing effect
+        message_placeholder.markdown(f'<div class="typing-text">{response}</div>', unsafe_allow_html=True)
+        # Add assistant message to history
         st.session_state.messages.append({"role": "assistant", "content": response})
+# Footer with 2026 branding
+st.markdown("""
+<div class="footer">
+    <div style="margin-bottom: 0.5rem;">
+        <span class="footer-brand">DEATH LEGION</span> |
+        <span class="footer-year">2026 Edition</span>
     </div>
+    <div>Powered by nvdya-kit | Next-Gen AI Infrastructure</div>
+    <div style="margin-top: 0.5rem; font-size: 0.8rem;">
+        Legion Coder 8M | 44M Parameters | ~170MB | CPU-Optimized | 10K+ Downloads
+    </div>
+</div>
+""", unsafe_allow_html=True)