agents/software_engineer_agent.py

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
from langchain_core.messages import AIMessage
import asyncio
from typing import Generator, Dict, Any

MODEL_REPO = "Rahul-8799/software_engineer_mellum"

tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_REPO,
    torch_dtype=torch.float16,
    device_map="auto"
)

async def stream_inference(prompt: str) -> Generator[str, None, None]:
    """Stream the model's output token by token"""
    input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
    
    for _ in range(100):
        output_ids = model.generate(
            input_ids,
            max_new_tokens=1,
            pad_token_id=tokenizer.eos_token_id
        )
        
        new_token = output_ids[0][-1]
        if new_token == tokenizer.eos_token_id:
            break
            
        token_text = tokenizer.decode([new_token])
        yield token_text
        
        input_ids = output_ids
        await asyncio.sleep(0.05)

async def run(state: Dict[str, Any]) -> Dict[str, Any]:
    """Software Engineer generates responsive and interactive UI code"""
    messages = state["messages"]
    prompt = messages[-1].content
    
    # Enhance the prompt with modern web development requirements
    enhanced_prompt = f"""
    Generate modern, responsive, and interactive UI code following these requirements:
    1. Use Tailwind CSS for responsive design
    2. Implement JavaScript for interactivity
    3. Add smooth animations and transitions
    4. Ensure mobile-first approach
    5. Include proper error handling
    6. Add loading states and feedback
    
    Original requirements: {prompt}
    
    Generate the following files:
    
    1. index.html - Main HTML structure
    2. styles.css - Custom styles (if needed beyond Tailwind)
    3. script.js - Interactive features
    4. tailwind.config.js - Tailwind configuration
    
    Format the output as:
    
    ## HTML Structure
    ```html
    [HTML code]
    ```
    
    ## CSS Styles
    ```css
    [CSS code]
    ```
    
    ## JavaScript
    ```javascript
    [JavaScript code]
    ```
    
    ## Tailwind Config
    ```javascript
    [Tailwind configuration]
    ```
    """
    
    # Stream the output
    output = ""
    async for token in stream_inference(enhanced_prompt):
        output += token
    
    return {
        "messages": [AIMessage(content=output)],
        "chat_log": state["chat_log"] + [{"role": "Software Engineer", "content": output}],
        "dev_output": output,
    }