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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=1024)
ind = out.index('Response') + len('Response')+2
return out[ind:]
chat_interface = gr.Interface(
respond,
inputs="text",
outputs="text",
title="Gemma instruct 2b_en finetuned on medical transcripts",
description="Gemma instruct 2b_en finetuned on medical transcripts",
css=css
)
chat_interface.launch() |