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import torch
# from transformers import AutoTokenizer, AutoModelForCausalLM
from llm_utils import tokenizer, model, generate_completion
from smolagents import tool
import warnings
warnings.filterwarnings("ignore")
@tool
def check_significant_change(previous_context: str, current_context: str) -> int:
"""
Compare previous and current context; return 1 if major change (new scene/env), else 0.
Args:
previous_context (str): The previous context text
current_context (str): The current context text
Returns:
int: 1 if major significant change detected, 0 otherwise
"""
prompt = f"""
Compare these two contexts and determine if there is a major significant change (like a new scene, environment, or dramatic shift in situation). Reply with only "change" for a major significant change, or "unchange" if the contexts are similar or show minor differences.
Previous: {previous_context}
Current: {current_context}
Answer (change or unchange):"""
# wrap in a chat template
messages = [
{"role": "system", "content": "You detect significant changes between contexts. Reply only with 'change' or 'unchange'."},
{"role": "user", "content": prompt}
]
# tokenize & move to device
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True
).to(model.device)
# generate
with torch.no_grad():
outputs = generate_completion(
**inputs,
max_new_tokens=10,
temperature=0.0,
do_sample=False,
pad_token_id=tokenizer.eos_token_id
)
# slice off prompt
prompt_len = inputs["input_ids"].shape[-1]
gen_ids = outputs[0][prompt_len:]
# decode response
raw = tokenizer.decode(gen_ids, skip_special_tokens=True)
response = raw.strip().lower()
# check for change indicators
if "change" in response and "unchange" not in response:
return 1
else:
return 0
if __name__ == "__main__":
# --- Test Cases ---
tests = [
("John types at his desk in the morning light.", "John now types with a cup of coffee beside him."),
("Sarah walks through the quiet library browsing books.", "She stands on a cliff overlooking crashing waves."),
("Morning vendors set up at the market.", "The empty market is silent under the moonlight.")
]
for i, (prev, curr) in enumerate(tests, 1):
result = check_significant_change(previous_context=prev, current_context=curr)
print(f"Test {i}: Prev='{prev}' | Curr='{curr}' -> Change Detected: {result}") |