| import gradio as gr |
| import os |
| os.environ["KERAS_BACKEND"] = "tensorflow" |
| import keras |
| import keras_nlp |
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| css = "body {background-image: url('https://www.universetoday.com/wp-content/uploads/2023/11/Sagittarius_C_NIRCam_Image_pillars-2000x1080.jpg');}" |
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| gemma_lm = keras_nlp.models.CausalLM.from_preset("hf://sultan-hassan/CosmoGemma_2b_en") |
|
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| 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) |
|
|
| iface.launch(share=True) |
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|
