app.py

import os
import gradio as gr
from huggingface_hub import InferenceClient
import random

def get_coding_samples():
    """Diverse coding challenges"""
    return [
        "Write a function to reverse a linked list",
        "Implement binary search in Python",
        "Create a REST API endpoint for user authentication",
        "Write a SQL query to find duplicate records",
        "Build a React component for a todo list",
        "Implement quicksort algorithm",
        "Parse JSON and handle errors gracefully",
        "Create a decorator for timing function execution",
        "Write regex to validate email addresses",
        "Implement a LRU cache in Python",
        "Build a simple web scraper with BeautifulSoup",
        "Create a custom hook in React",
        "Write unit tests for a calculator function",
        "Implement depth-first search for a graph",
        "Build a CLI tool with argparse",
        "Create a database migration script",
        "Write a function to detect palindromes",
        "Implement JWT token authentication",
        "Build a responsive navbar with Tailwind CSS",
        "Create a rate limiter middleware",
    ]

def create_coder_system_message():
    """System prompt for coding assistance"""
    return """You are CoderAI, an expert programming assistant.

**Your Approach:**
1. Understand the requirements clearly
2. Provide clean, working code with comments
3. Explain key concepts and design decisions
4. Suggest best practices and optimizations
5. Include error handling where appropriate

**Code Quality:**
- Write readable, maintainable code
- Follow language conventions and style guides
- Use meaningful variable names
- Add docstrings/comments for complex logic
- Consider edge cases and error handling

**Format:**
- Use markdown code blocks with language tags
- Explain code sections when helpful
- Provide usage examples
- Mention dependencies if needed

Be practical, efficient, and educational."""

def respond(message, history, system_message, max_tokens, temperature, top_p, model_choice):
    """Streaming response with error handling"""
    hf_token = os.getenv("HF_TOKEN")
    if not hf_token:
        yield "❌ **Error:** HF_TOKEN not found. Set it in environment or Spaces secrets."
        return
    
    # Model selection
    models = {
        "DeepSeek Coder": "deepseek-ai/deepseek-coder-33b-instruct",
        "CodeLlama": "codellama/CodeLlama-34b-Instruct-hf",
        "Qwen Coder": "Qwen/Qwen2.5-Coder-32B-Instruct",
    }
    
    client = InferenceClient(
        model=models.get(model_choice, models["Qwen Coder"]),
        token=hf_token
    )
    
    messages = [{"role": "system", "content": system_message}]
    
    for msg in history:
        if isinstance(msg, dict):
            role = msg.get("role", "user")
            content = msg.get("content", "")
        else:
            role = "user"
            content = str(msg)
        
        if content:
            messages.append({"role": role, "content": content})
    
    messages.append({"role": "user", "content": message})
    
    try:
        response_text = ""
        for chunk in client.chat_completion(
            messages,
            max_tokens=max_tokens,
            temperature=temperature,
            top_p=top_p,
            stream=True
        ):
            if chunk.choices[0].delta.content:
                response_text += chunk.choices[0].delta.content
                yield response_text
                
    except Exception as e:
        error_msg = f"❌ **Error:** {str(e)}\n\nTry:\n- Checking your HF_TOKEN\n- Simplifying the request\n- Using a different model"
        yield error_msg

def get_random_sample():
    """Get random coding challenge"""
    return random.choice(get_coding_samples())

# Gradio Interface
with gr.Blocks(title="💻 CoderAI", theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 💻 **CoderAI**\n*Your AI Programming Assistant*")
    
    chatbot = gr.Chatbot(
        height=500, 
        type='messages', 
        label="💬 Conversation",
        show_copy_button=True
    )
    
    msg = gr.Textbox(
        placeholder="Ask a coding question or describe what you need...", 
        show_label=False, 
        scale=4
    )
    
    with gr.Row():
        submit = gr.Button("🚀 Code", variant="primary", scale=1)
        clear = gr.Button("🗑️ Clear", variant="secondary", scale=1)
        sample = gr.Button("🎲 Random", variant="secondary", scale=1)
    
    with gr.Accordion("⚙️ Advanced Settings", open=False):
        model_dropdown = gr.Dropdown(
            choices=["Qwen Coder", "DeepSeek Coder", "CodeLlama"],
            value="Qwen Coder",
            label="Model Selection"
        )
        temp_slider = gr.Slider(0.1, 1.0, value=0.2, step=0.1, label="Temperature")
        tokens_slider = gr.Slider(512, 4096, value=2048, step=256, label="Max Tokens")
        top_p_slider = gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top-p")
    
    with gr.Accordion("💡 Help & Examples", open=False):
        gr.Markdown("""
**Tips:**
- Be specific about language and requirements
- Mention if you need comments or tests
- Ask for explanations of complex parts
- Request specific design patterns or styles

**What I can help with:**
- Writing functions and classes
- Debugging code
- Code review and optimization
- Algorithm implementation
- API design and database queries
- Testing and documentation
        """)
    
    gr.Examples(
        examples=[
            ["Write a Python function to check if a string is a palindrome"],
            ["Create a React component for a searchable dropdown"],
            ["Implement a binary tree traversal in JavaScript"],
            ["Write a SQL query to find the top 5 customers by revenue"],
            ["Build a simple Flask API with error handling"],
            ["Create a custom validation decorator in Python"],
        ],
        inputs=msg
    )
    
    system_msg = gr.State(create_coder_system_message())
    
    def chat_response(message, history, sys_msg, max_tok, temp, top_p, model):
        """Handle chat with streaming"""
        if not message.strip():
            return history, ""
        
        history.append({"role": "user", "content": message})
        history.append({"role": "assistant", "content": ""})
        
        for response in respond(message, history[:-1], sys_msg, max_tok, temp, top_p, model):
            history[-1]["content"] = response
            yield history, ""
        
        return history, ""
    
    def clear_chat():
        return [], ""
    
    msg.submit(
        chat_response, 
        [msg, chatbot, system_msg, tokens_slider, temp_slider, top_p_slider, model_dropdown], 
        [chatbot, msg]
    )
    submit.click(
        chat_response, 
        [msg, chatbot, system_msg, tokens_slider, temp_slider, top_p_slider, model_dropdown], 
        [chatbot, msg]
    )
    clear.click(clear_chat, outputs=[chatbot, msg])
    sample.click(get_random_sample, outputs=msg)

demo.launch()