model.py

import os
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

MODEL_NAME = "bigcode/starcoderbase-1b"
HF_TOKEN = os.getenv("HUGGINGFACE_TOKEN")

device = "cpu"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=HF_TOKEN)

if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token  

model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    token=HF_TOKEN,
    torch_dtype=torch.float32,  
    trust_remote_code=True
).to(device)

def generate_code(prompt: str, max_tokens: int = 256):
    # 🛠 **Improve Speed & Support Multi-language**
    formatted_prompt = f"{prompt}\n### Code:\n"  # Hint that code follows

    inputs = tokenizer(
        formatted_prompt, 
        return_tensors="pt", 
        truncation=True,  
        max_length=512  # ⏩ Reduce max length to speed up processing
    ).to(device)

    output = model.generate(
        **inputs,
        max_new_tokens=max_tokens,
        pad_token_id=tokenizer.pad_token_id,
        do_sample=True,  
        top_p=0.90,  # 🎯 Prioritize better predictions
        temperature=0.6  # 🔥 More deterministic output
    )
    
    generated_code = tokenizer.decode(output[0], skip_special_tokens=True)

    # Remove the input prompt from the output
    if generated_code.startswith(formatted_prompt):
        generated_code = generated_code[len(formatted_prompt):]

    return generated_code.strip()