| import gradio as gr | |
| import os | |
| os.environ["KERAS_BACKEND"] = "tensorflow" | |
| import keras | |
| import keras_nlp | |
| 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") | |
| iface.launch() | |