app.py

# Forrest Sheng Bao
# 2025-05-25
# forrest@vectara.com

from typing import List, Literal
from IPython.display import display, Markdown
from transformers import AutoModelForSequenceClassification
from funix import funix

hhem = AutoModelForSequenceClassification.from_pretrained('vectara/hallucination_evaluation_model', trust_remote_code=True)

print ("Loading HHEM, this may take a while.")

@funix(
    title= "GUI demo for Vectara's HHEM-2.1-Open"
)
def HHEM(
    LLM_Prompt: str = "The sky is blue.",
    LLM_Response: str = "The ocean is blue."
    ) -> Markdown:
    """
    Vectara's Hughes Hallucination Evaluation Model (HHEM)  evaluates how well an LLM's output (called the "response" or the "hypothesis") is faithful/grounded to or supported by the input given to it (called the "prompt" or the "premise"). HHEM has two versions: [HHEM-Open](https://huggingface.co/vectara/hallucination_evaluation_model) and [HHEM Commercial](https://www.vectara.com/blog/hhem-2-1-a-better-hallucination-detection-model).

    To use the demo, fill in the "LLM_Prompt" and "LLM_Response" fields and click the run button. A placeholder example is prefilled for you. Feel free to replace it with your own examples and evaluate them.

    Args:
        LLM_Prompt (str): a.k.a. the "premise".
        LLM_Response (str): a.k.a. the "hypothesis".

    """

    if len(LLM_Prompt) + len(LLM_Response) > 500:
        return Markdown("""Your input is too long for this demo. Please shorten them such that their total length is under 500 characters.""")
        

    pairs = [(LLM_Prompt, LLM_Response)]

    score = hhem.predict(pairs)[0]

    verdict = "consistent" if score > 0.5 else "hallucinated"

    output_string = f"""
Given the **prompt**: 

> {LLM_Prompt}

and 

the **response**: 
> {LLM_Response}

HHEM's **judgement** is: <u>{verdict}</u> **with the score**: <u>{score:0.3f}</u>. 

Wanna know why? Check out [Vectara's Hallucination Corrector (VHC)](https://hcm.demo.vectara.dev/)

Note that in the industry, there are generally two definitions to hallucations:
* faithfulness: the LLM's response contains information not supported by the prompt given to it. 
* factuality: the LLM's response is not true per the world knowledge.

In HHEM, we take the faithfulness definition. 

See also: 
* [HHEM Leaderboard](https://huggingface.co/spaces/vectara/leaderboard)
* [Source code of this app](https://huggingface.co/spaces/vectara/hhem-2.1-open-demo/tree/main)

    """
    
    return output_string