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| from transformers import TextClassificationPipeline, AutoTokenizer, AutoModelForSequenceClassification | |
| from nooffense.sentence_encoder import SentenceEncoder | |
| import numpy as np | |
| 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" | |
| ] | |
| sentence_list = [] #global variable go brr | |
| def normalize_outputs(pred): | |
| values = np.asarray([p[1] for p in pred]) | |
| normalized = (values-min(values))/(max(values)-min(values)) | |
| new_preds = {p[0]:float(v) for p,v in zip(pred, normalized)} | |
| return new_preds | |
| def clear_sentences(): | |
| sentence_list.clear() | |
| return None | |
| def display_list(text): | |
| sentence_list.append(text) | |
| new_text = '\n'.join(sentence_list) | |
| return new_text | |
| def sentiment_analysis(text, model_choice): | |
| model = SentenceEncoder(models[model_choice]) | |
| pred = model.find_most_similar(text, sentence_list) | |
| return normalize_outputs(pred) | |
| with gr.Blocks() as embed_interface: | |
| gr.HTML("""<h1 style="font-weight:600;font-size:50;margin-top:4px;margin-bottom:4px;text-align:center;">No Offense Sentence Similarity</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") | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_text2 = gr.Textbox(label ='Add Sentence', placeholder='asdas') | |
| with gr.Column(): | |
| input_text3 = gr.Textbox(label ='Sentences List', placeholder='asdasd') | |
| with gr.Row(): | |
| add_button = gr.Button('Add') | |
| clear_button = gr.Button('Clear') | |
| the_button = gr.Button("Run") | |
| with gr.Column(): | |
| output_window = gr.Label(num_top_classes=5, show_label=False) | |
| clear_button.click(clear_sentences, outputs=[input_text3]) | |
| add_button.click(display_list, inputs=[input_text2], outputs=[input_text3]) | |
| the_button.click(sentiment_analysis, inputs=[input_text, model_choice], outputs=[output_window]) |