import gradio as gr
import os
os.environ["KERAS_BACKEND"] = "tensorflow"                                                                           
import keras
import keras_nlp


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://stsci-opo.org/STScI-01J5E849R5W27ZZ2C3QAE9ET75.png');
    background-size: cover;
    background-repeat: no-repeat;
    opacity: 0.35;             /* Faint background image */
    background-position: center;
    z-index: -1;    /* Keep the background behind text */
}
.gradio-container {
    display: flex;
    justify-content: center;
    align-items: center;
    height: 100vh;  /* Ensure the content is vertically centered */
}
"""

keras.config.set_floatx("bfloat16")
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=1024)
    ind = out.index('Response') + len('Response')+2
    return out[ind:]

iface = gr.Interface(launch,
                     inputs="text",
                     outputs="text",
                     css=css,
                     title="Hey I am CosmoGemma 👋 I can answer cosmology questions from astroph.CO research articles. Try me :)",
                     description="Gemma_2b_en fine-tuned on QA pairs (~3.5k) generated from Cosmology and Nongalactic Astrophysics articles (arXiv astro-ph.CO) from 2018-2022 and tested on QA pairs (~1k) generated from 2023 articles, scoring over 75% accuracy.")

iface.launch()