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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() | |