agent.py

# agent.py (Updated for Hugging Face inference with FLAN-T5)

from transformers import pipeline

# Load the Hugging Face inference pipeline
qa_pipeline = pipeline("text2text-generation", model="google/flan-t5-base")

def answer_question(question: str, file_context: str = None, do_search: bool = False) -> str:
    """
    Answers the question using file context (if any) with a Hugging Face model.

    Args:
        question (str): The question to be answered.
        file_context (str, optional): Optional context extracted from a file.
        do_search (bool): Ignored for local mode.

    Returns:
        str: The generated answer.
    """
    prompt = question if not file_context else f"Context: {file_context}\nQuestion: {question}"
    try:
        result = qa_pipeline(prompt, max_length=256, do_sample=False)
        return result[0]["generated_text"].strip()
    except Exception as e:
        return f"[ERROR] Hugging Face pipeline failed: {e}"