interfaces/classifier_interface.py

from transformers import TextClassificationPipeline, AutoTokenizer, AutoModelForSequenceClassification
import gradio as gr
import os


models = [
    "Overfit-GM/bert-base-turkish-cased-offensive",
    "Overfit-GM/bert-base-turkish-uncased-offensive",
    "Overfit-GM/bert-base-turkish-128k-cased-offensive",
    "Overfit-GM/bert-base-turkish-128k-uncased-offensive",
    "Overfit-GM/convbert-base-turkish-mc4-cased-offensive",
    "Overfit-GM/convbert-base-turkish-mc4-uncased-offensive",
    "Overfit-GM/convbert-base-turkish-cased-offensive",
    "Overfit-GM/distilbert-base-turkish-cased-offensive",
    "Overfit-GM/electra-base-turkish-cased-discriminator-offensive",
    "Overfit-GM/electra-base-turkish-mc4-cased-discriminator-offensive",
    "Overfit-GM/electra-base-turkish-mc4-uncased-discriminator-offensive",
    "Overfit-GM/xlm-roberta-large-turkish-offensive",
    "Overfit-GM/mdeberta-v3-base-offensive"
]


model_box=[
    gr.load(models[0], src='models', hf_token=os.environ['API_KEY']),
    gr.load(models[1], src='models', hf_token=os.environ['API_KEY']),
    gr.load(models[2], src='models', hf_token=os.environ['API_KEY']),
    gr.load(models[3], src='models', hf_token=os.environ['API_KEY']),
    gr.load(models[4], src='models', hf_token=os.environ['API_KEY']),
    gr.load(models[5], src='models', hf_token=os.environ['API_KEY']),
    gr.load(models[6], src='models', hf_token=os.environ['API_KEY']),
    gr.load(models[7], src='models', hf_token=os.environ['API_KEY']),
    gr.load(models[8], src='models', hf_token=os.environ['API_KEY']),
    gr.load(models[9], src='models', hf_token=os.environ['API_KEY']),
    gr.load(models[10], src='models', hf_token=os.environ['API_KEY']),
    gr.load(models[11], src='models', hf_token=os.environ['API_KEY']),
    gr.load(models[12], src='models', hf_token=os.environ['API_KEY'])
]



def sentiment_analysis(text, model_choice):
    
    model = model_box[model_choice]
    output = model(text)
    return output

with gr.Blocks() as classifier_interface:
    gr.HTML("""<h1 style="font-weight:600;font-size:50;margin-top:4px;margin-bottom:4px;text-align:center;">No Offense Classifier</h1></div>""")
    with gr.Row():
        with gr.Column():
            model_choice = gr.Dropdown(label="Select Model", choices=[m for m in models], type="index", interactive=True)
            input_text = gr.Textbox(label="Input", placeholder="senin ben amk")
            the_button = gr.Button(label="Run")
        with gr.Column():
            output_window = gr.Label(num_top_classes=5)

    the_button.click(sentiment_analysis, inputs=[input_text, model_choice], outputs=[output_window])
    examples = gr.Examples(examples=["bu adamların ülkesine dönmesi lazım", "adam olsan oraya gitmezdin"],
                           inputs=[input_text])