| import gradio as gr | |
| from transformers import pipeline | |
| summarizer = pipeline('summarization', model='Eitanli/resume_label_summary_model') | |
| def predict(text, min_length, max_length): | |
| summary = summarizer(text, min_length=min_length, max_length=max_length) | |
| return summary[0]['summary_text'] | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=[ | |
| gr.Textbox(label="Paste resume here"), | |
| gr.Slider(2, 20, step=1.0, value=2, label="min_length", info="Choose between 2 and 20"), | |
| gr.Slider(10, 30, step=1.0, value=10, label="max_length", info="Choose between 10 and 30")], | |
| outputs="text") | |
| iface.launch() |