| import gradio as gr |
| from transformers import pipeline |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
|
|
| def check_spam(text): |
| classifier = pipeline("text-classification") |
| model_name = "ericjedha/spammy_overfit" |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| classifier = pipeline("text-classification", model = model, tokenizer = tokenizer) |
| result = classifier(text)[0] |
| return f"Label: {result['label']}, Score: {result['score']:.2f}" |
|
|
| with gr.Blocks() as demo: |
| with gr.Row(): |
| with gr.Column(): |
| sms = gr.Textbox(label="SMS à checker") |
| spam_check_btn = gr.Button("Check") |
| with gr.Column(): |
| result = gr.Textbox(label="Résultat") |
| |
| spam_check_btn.click(check_spam, inputs=sms, outputs=result) |
|
|
| demo.launch(share=True) |
|
|
