app.py

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

# def get_model(model_name='Overfit-GM/temp_dist'):
#     id2label = {0: 'INSULT', 1: 'OTHER',
#                 2: 'PROFANITY', 3: 'RACIST', 4: 'SEXIST'}
#     label2id = {v: k for k, v in id2label.items()}
#     tokenizer = AutoTokenizer.from_pretrained(model_name)
#     model = AutoModelForSequenceClassification.from_pretrained(model_name,
#                                                                problem_type="single_label_classification",
#                                                                id2label=id2label,
#                                                                label2id=label2id,
#                                                                num_labels=5,
#                                                                output_hidden_states=False,
#                                                                )
#     return model, tokenizer


models = [
    "Overfit-GM/temp_dist",
    "deprem-ml/deprem_bert_128k"
]

model_box=[
    gr.load(models[0], src='models'),
    gr.load(models[1], src='models'),
]

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

with gr.Blocks() as demo:
    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])

demo.launch()