app.py

import gradio as gr
import os
from setfit import SetFitModel

model_names = ['java-summary', 'java-pointer', 'java-deprecation', 'java-rational', 'java-ownership', 'java-usage', 'java-expand', 
          'pharo-example', 'pharo-key-implementation', 'pharo-responsibilities', 'pharo-collaborators',
          'python-summary', 'pharo-parameters', 'pharo-usage', 'pharo-development-notes', 'pharo-expand']

models = {}
for model_name in model_names:
    models[model_name] = SetFitModel.from_pretrained("dvilasuero/setfit-mini-imdb")#f'AISE-TUDelft/{model_name}'


def classify(text, model_name):
    if models[model_name]([text])[0]:
        return 'True'
    else:
        return 'False'
    
iface = gr.Interface(fn=classify, 
                     inputs=["text", gr.inputs.Dropdown(model_names, label='class')], 
                     outputs="text",
                    title='STACC',
                    description='''# STACC: a set of SentenceTransformer Assisted Comment Classifiers 📚

This app showcases STACC, a collection of SetFit-based Comment classifiers created for the [NLBSE-2023 tool competition](https://nlbse2023.github.io/tools/). More details on the tool itself can be found in the [GitHub repo.](https://github.com/AISE-TUDelft/STACC) 

To use the app, write a comment in the text box, and select the class you wish to test. Press Submit and watch the magic happen ✨
''')
iface.launch()