test.py

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load fine-tuned model and tokenizer
model_path = "./finetuned_codegen"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16)

# Set padding token
tokenizer.pad_token = tokenizer.eos_token

# Move model to CPU
device = torch.device("cpu")
model.to(device)

# Test prompts (including dataset prompts)
prompts = [
    "Write a Python program to print 'Hello, you name or any other thing!'"
]

# Generate code for each prompt
for prompt in prompts:
    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=128).to(device)
    outputs = model.generate(
        **inputs,
        max_length=200,
        num_return_sequences=1,
        pad_token_id=tokenizer.eos_token_id,
        do_sample=True,
        temperature=0.7,
        top_p=0.9
    )
    generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
    print(f"Prompt: {prompt}\nGenerated Code:\n{generated_code}\n{'-'*50}")