app.py

from response_generation import ResponseGenerator
import gradio as gr

DEFAULT_MODEL = "shaneweisz/DialoGPT-finetuned-multiCONAN"
DECODING_CONFIG = {"max_new_tokens": 100, "min_new_tokens": 20, "no_repeat_ngram_size": 3, "num_beams": 10}

TITLE = "Automating Counterspeech in Dialogue Systems"
DESCRIPTION = """
Built by [Shane Weisz](https://shaneweisz.com) for my research project on _Automating Counterspeech in Dialogue Systems_ as part of the [MPhil in Machine Learning and Machine Intelligence](https://www.mlmi.eng.cam.ac.uk/) at Cambridge University.
<br/>
The project is supervised by [Dr Marcus Tomalin](https://www.crassh.cam.ac.uk/about/people/marcus-tomalin/) and forms part of the [Giving Voice to Digital Democracies](https://www.crassh.cam.ac.uk/research/projects-centres/giving-voice-to-digital-democracies/) project on the _The Social Impact of Artificially Intelligent Communications Technology_.
<br/>
The system is built by fine-tuning [DialoGPT](https://huggingface.co/microsoft/DialoGPT-medium#:~:text=DialoGPT%20is%20a%20SOTA%20large,single%2Dturn%20conversation%20Turing%20test) on the [MultiCONAN](https://github.com/marcoguerini/CONAN#Multitarget-CONAN) dataset, a dataset comprising a set of hate speech inputs and appropriate [counterspeech](https://dangerousspeech.org/counterspeech/) responses produced under the supervision of trained NGO operators from [Stop Hate UK](https://www.stophateuk.org/).
<br/><br/>
**Try it out**: Enter a hate speech comment (or select one of the provided examples below), click Submit, and see if the system generates an appropriate counterspeech response.
"""

ARTICLE = f"""
_Please note: This system is a prototype and cannot be guaranteed to always generate appropriate responses. Any inappropriate responses expressed by the system should not be construed as reflective of the views or values of the researchers._


**Model:** {DEFAULT_MODEL}<br>
**Decoding parameters:** {DECODING_CONFIG}
<br/><br/>

"""

model = ResponseGenerator(DEFAULT_MODEL, DECODING_CONFIG)

def respond(input):
    return model.respond(input)

demo = gr.Interface(fn=respond, inputs="text", outputs="text", examples=["Muslims are all terrorists", "Jews are selfish and greedy", "Why waste time listening to black women?"], cache_examples = False, title = TITLE, description = DESCRIPTION, article = ARTICLE)
demo.launch()