| import gradio as gr |
| import os |
| os.environ["KERAS_BACKEND"] = "tensorflow" |
| import keras |
| import keras_nlp |
|
|
|
|
| css = """ |
| body { |
| margin: 0; |
| height: 100%; |
| } |
| body::before { |
| content: ''; |
| position: fixed; |
| top: 0; |
| left: 0; |
| width: 100vw; |
| height: 100vh; |
| background-image: url('https://stsci-opo.org/STScI-01J6D97YGSQACWK990TH56K6AF.png'); |
| background-size: cover; |
| background-repeat: no-repeat; |
| background-position: center; |
| opacity: 0.02; /* Fainter background image */ |
| z-index: -1; /* Keep the background behind text */ |
| } |
| """ |
|
|
|
|
| gemma_lm = keras_nlp.models.CausalLM.from_preset("hf://sultan-hassan/CosmoGemma_2b_en") |
|
|
| def launch(input): |
| template = "Instruction:\n{instruction}\n\nResponse:\n{response}" |
| prompt = template.format( |
| instruction=input, |
| response="", |
| ) |
| out = gemma_lm.generate(prompt, max_length=256) |
| ind = out.index('Response') + len('Response')+2 |
| return out[ind:] |
|
|
| iface = gr.Interface(launch, |
| inputs="text", |
| outputs="text", |
| css=css, |
| title="Hello, I am an expert in cosmology, try me!", |
| description="Gemma_2b_en fine-tuned on QA pairs (~3.5k) generated from Cosmology and Nongalactic Astrophysics articles (arXiv astro-ph.CO) from 2018-2022 and tested on QA pairs (~1k) generated from 2023 articles, scoring over 75% accuracy.") |
|
|
| iface.launch() |
|
|
|
|
