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import gradio as gr |
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import os |
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os.environ["KERAS_BACKEND"] = "tensorflow" |
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import keras |
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import keras_nlp |
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import utils |
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css = """ |
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html, body { |
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margin: 0; |
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padding: 0; |
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height: 100%; |
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overflow: hidden; |
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} |
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body::before { |
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content: ''; |
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position: fixed; |
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top: 0; |
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left: 0; |
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width: 100vw; |
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height: 100vh; |
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background-image: url('https://stsci-opo.org/STScI-01J5E849R5W27ZZ2C3QAE9ET75.png'); |
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background-size: cover; |
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background-repeat: no-repeat; |
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opacity: 0.35; /* Faint background image */ |
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background-position: center; |
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z-index: -1; /* Keep the background behind text */ |
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} |
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.gradio-container { |
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display: flex; |
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justify-content: center; |
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align-items: center; |
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height: 100vh; /* Ensure the content is vertically centered */ |
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} |
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""" |
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gemma_lm = keras_nlp.models.CausalLM.from_preset("hf://sultan-hassan/CosmoGemma_2b_en") |
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chat = utils.ChatState(gemma_lm) |
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def launch(message): |
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return chat.send_message(message) |
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iface = gr.Interface(launch, |
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inputs="text", |
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outputs="text", |
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css=css, |
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title="Hey I am CosmoGemma 👋 I can answer cosmology questions from astroph.CO research articles. Try me :)", |
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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.") |
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iface.launch() |
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