app.py

"""
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;
    background: linear-gradient(135deg, #0a0a0f 0%, #1a1a2e 50%, #16213e 100%);
    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)