utils/models.py

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria, StoppingCriteriaList
from .prompts import format_rag_prompt
from .shared import generation_interrupt
import threading
import queue
import time # Added for sleep

models = {
    "Qwen2.5-1.5b-Instruct": "qwen/qwen2.5-1.5b-instruct",
    "Llama-3.2-1b-Instruct": "meta-llama/llama-3.2-1b-instruct",
    "Gemma-3-1b-it": "google/gemma-3-1b-it",
}

# List of model names for easy access
model_names = list(models.keys())

# Custom stopping criteria that checks the interrupt flag
class InterruptCriteria(StoppingCriteria):
    def __init__(self, interrupt_event):
        self.interrupt_event = interrupt_event
        
    def __call__(self, input_ids, scores, **kwargs):
        return self.interrupt_event.is_set()

def generate_summaries(example, model_a_name, model_b_name):
    """
    Generates summaries for the given example using the assigned models.
    """
    if generation_interrupt.is_set():
        return "", ""

    context_text = ""
    context_parts = []
    if "full_contexts" in example:
        for ctx in example["full_contexts"]:
            if isinstance(ctx, dict) and "content" in ctx:
                context_parts.append(ctx["content"])
        context_text = "\n---\n".join(context_parts)
    else:
        raise ValueError("No context found in the example.")

    question = example.get("question", "")

    if generation_interrupt.is_set():
        return "", ""

    # Use a queue to get results from threads
    result_queue_a = queue.Queue()
    thread_a = threading.Thread(target=run_inference, args=(models[model_a_name], context_text, question, result_queue_a))
    thread_a.start()

    summary_a = ""
    while thread_a.is_alive():
        if generation_interrupt.is_set():
            print(f"Interrupting model A ({model_a_name})...")
            # The InterruptCriteria within the thread will handle stopping generate
            # We return early from the main control flow.
            thread_a.join(timeout=1.0) # Give thread a moment to potentially stop
            return "", ""
        try:
            summary_a = result_queue_a.get(timeout=0.1) # Check queue periodically
            break # Got result
        except queue.Empty:
            continue # Still running, check interrupt again

    # If thread finished but we didn't get a result (e.g., interrupted just before putting in queue)
    if not summary_a and not result_queue_a.empty():
         summary_a = result_queue_a.get_nowait()
    elif not summary_a and generation_interrupt.is_set(): # Check interrupt again if thread finished quickly
         return "", ""


    if generation_interrupt.is_set(): # Check between models
        return summary_a, ""

    # --- Model B ---
    result_queue_b = queue.Queue()
    thread_b = threading.Thread(target=run_inference, args=(models[model_b_name], context_text, question, result_queue_b))
    thread_b.start()

    summary_b = ""
    while thread_b.is_alive():
        if generation_interrupt.is_set():
            print(f"Interrupting model B ({model_b_name})...")
            thread_b.join(timeout=1.0)
            return summary_a, "" # Return summary_a obtained so far
        try:
            summary_b = result_queue_b.get(timeout=0.1)
            break
        except queue.Empty:
            continue

    if not summary_b and not result_queue_b.empty():
        summary_b = result_queue_b.get_nowait()
    elif not summary_b and generation_interrupt.is_set():
         return summary_a, ""


    return summary_a, summary_b


# Modified run_inference to run in a thread and use a queue for results
def run_inference(model_name, context, question, result_queue):
    """
    Run inference using the specified model. Designed to be run in a thread.
    Puts the result or an error string into the result_queue.
    """
    # Check interrupt at the very beginning of the thread
    if generation_interrupt.is_set():
        result_queue.put("")
        return

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = None
    tokenizer = None
    result = ""

    try:
        tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left", token=True)
        accepts_sys = (
            "System role not supported" not in tokenizer.chat_template
            if tokenizer.chat_template else False # Handle missing chat_template
        )

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

        # Check interrupt before loading the model
        if generation_interrupt.is_set():
             result_queue.put("")
             return

        model = AutoModelForCausalLM.from_pretrained(
            model_name, torch_dtype=torch.bfloat16, attn_implementation="eager", token=True
        ).to(device)
        model.eval() # Set model to evaluation mode

        text_input = format_rag_prompt(question, context, accepts_sys)

        # Check interrupt before tokenization/template application
        if generation_interrupt.is_set():
             result_queue.put("")
             return

        actual_input = tokenizer.apply_chat_template(
            text_input,
            return_tensors="pt",
            tokenize=True,
            # Consider reducing max_length if context/question is very long
            # max_length=tokenizer.model_max_length, # Use model's max length
            # truncation=True, # Ensure truncation if needed
            max_length=2048, # Keep original max_length for now
            add_generation_prompt=True,
        ).to(device)

        # Ensure input does not exceed model max length after adding generation prompt
        # This check might be redundant if tokenizer handles it, but good for safety
        # if actual_input.shape[1] > tokenizer.model_max_length:
        #    # Handle too long input - maybe truncate manually or raise error
        #    print(f"Warning: Input length {actual_input.shape[1]} exceeds model max length {tokenizer.model_max_length}")
        #    # Simple truncation (might lose important info):
        #    # actual_input = actual_input[:, -tokenizer.model_max_length:]

        input_length = actual_input.shape[1]
        attention_mask = torch.ones_like(actual_input).to(device)

        # Check interrupt before generation
        if generation_interrupt.is_set():
            result_queue.put("")
            return

        stopping_criteria = StoppingCriteriaList([InterruptCriteria(generation_interrupt)])

        with torch.inference_mode():
            outputs = model.generate(
                actual_input,
                attention_mask=attention_mask,
                max_new_tokens=512,
                pad_token_id=tokenizer.pad_token_id,
                stopping_criteria=stopping_criteria,
                do_sample=True, # Consider adding sampling parameters if needed
                temperature=0.6,
                top_p=0.9,
            )

        # Check interrupt immediately after generation finishes or stops
        if generation_interrupt.is_set():
            result = "" # Discard potentially partial result if interrupted
        else:
            # Decode the generated tokens, excluding the input tokens
            result = tokenizer.decode(outputs[0][input_length:], skip_special_tokens=True)

        result_queue.put(result)

    except Exception as e:
        print(f"Error in inference thread for {model_name}: {e}")
        # Put error message in queue for the main thread to handle/display
        result_queue.put(f"Error generating response: {str(e)[:100]}...")

    finally:
        # Clean up resources within the thread
        del model
        del tokenizer
        del actual_input
        del outputs
        if torch.cuda.is_available():
            torch.cuda.empty_cache()