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import gradio as gr | |
from huggingface_hub import InferenceClient | |
import keras | |
import keras_nlp | |
import os | |
os.environ["KERAS_BACKEND"] = "jax" | |
""" | |
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference | |
""" | |
css = """ | |
html, body { | |
margin: 0; | |
padding: 0; | |
height: 100%; | |
overflow: hidden; | |
} | |
body::before { | |
content: ''; | |
position: fixed; | |
top: 0; | |
left: 0; | |
width: 100vw; | |
height: 100vh; | |
background-image: url('https://png.pngtree.com/background/20230413/original/pngtree-medical-color-cartoon-blank-background-picture-image_2422159.jpg'); | |
background-size: cover; | |
background-repeat: no-repeat; | |
opacity: 0.60; | |
background-position: center; | |
z-index: -1; | |
} | |
.gradio-container { | |
display: flex; | |
flex-direction: column; | |
justify-content: center; | |
align-items: center; | |
height: 100vh; | |
} | |
""" | |
gemma_model = keras_nlp.models.GemmaCausalLM.from_preset("hf://harishnair04/gemma_instruct_medtr_2b") | |
def respond(input): | |
template = "Instruction:\n{instruction}\n\nResponse:\n{response}" | |
prompt = template.format( | |
instruction=input, | |
response="", | |
) | |
out = gemma_model.generate(prompt, max_length=128) | |
ind = out.index('Response') + len('Response')+2 | |
return out[ind:] | |
chat_interface = gr.Interface( | |
respond, | |
inputs="text", | |
outputs="text", | |
title="MedTr Chat", | |
description="Gemma 2B instruct fine-tuned for medical transcripts", | |
css=css | |
) | |
chat_interface.launch() |