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)

# Ensure the tokenizer has a pad token set
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token  # Set pad_token to eos_token

model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    token=HF_TOKEN,
    torch_dtype=torch.float32,  # Ensure compatibility with CPU
    trust_remote_code=True
).to(device)

def generate_code(prompt: str, max_tokens: int = 256):
    formatted_prompt = f"# Python\n{prompt}\n\n"  # Ensure the model understands it's code

    inputs = tokenizer(
        formatted_prompt, 
        return_tensors="pt", 
        padding=True, 
        truncation=True,  
        max_length=1024  # Explicit max length to prevent issues
    ).to(device)

    output = model.generate(
        **inputs,
        max_new_tokens=max_tokens,
        pad_token_id=tokenizer.pad_token_id,
        do_sample=True,  # Enable randomness for better outputs
        top_p=0.95,  # Nucleus sampling to improve generation
        temperature=0.7  # Control creativity
    )
    
    generated_code = tokenizer.decode(output[0], skip_special_tokens=True)

    # Clean the output: remove the repeated prompt at the start
    if generated_code.startswith(formatted_prompt):
        generated_code = generated_code[len(formatted_prompt):]

    return generated_code.strip()