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| import gradio as gr | |
| from gradio.mix import Parallel | |
| title = 'Comparing different dialogue summarization models' | |
| description = 'Here we are going to compare different summarization model namely BART-LARGE model which is trained on samsum data and t5small model which is also trained on the same dataset.' | |
| example = [[''' A: Hello B. How are you? | |
| B: I'm good. How's your course going on these days? | |
| A: It's going well. Pretty tiring but overall fun. | |
| B: That's great to hear. If you are free wanna grab a cup of coffee? | |
| A: Sure!!!! I recently discovered a new cafe near my home. | |
| B: Awesome. Let's go.'''], | |
| ['''Rohit: Hi, how’re you? | |
| Mahesh: I’m fine. What about you? | |
| Rohit: Good. How’s your work going on? | |
| Mahesh: Not great. | |
| Rohit: Why? What happened? | |
| Mahesh: My workplace is far from my home. Most of my time is spent commuting and I'm not able to give time to my family. | |
| Rohit: Oh!! That sounds taxing. What are you planning to do now? | |
| Mahesh: I will again start applying for jobs near my home. | |
| Rohit: Best of luck man!!''']] | |
| model1 = gr.Interface.load("huggingface/anegi/t5smallmodel", | |
| title = 'BART-Large-cnn-samsum', | |
| description = 'This is a pre trained model' | |
| ) | |
| model2 = gr.Interface.load("huggingface/philschmid/bart-large-cnn-samsum", | |
| title = 'T5-small model', | |
| description = 'This is a self trained model', | |
| ) | |
| model3 = gr.Interface.load("huggingface/lidiya/bart-large-xsum-samsum", | |
| title = 'T5-small model', | |
| description = 'This is a self trained model', | |
| ) | |
| Parallel(model1, model2 ,model3, | |
| title = title, | |
| description = description, | |
| inputs = gr.inputs.Textbox(lines = 7, label = 'Input Text', placeholder = 'Please enter your dialogue text here'), | |
| layout='vertically', | |
| examples = example, | |
| theme = 'peach' ).launch() | |