app.py

import gradio as gr
import numpy as np

species = np.load("data/species_70b.npy")
clas = np.load("data/class_70b.npy")
order = np.load("data/order_70b.npy")
#genus = np.load("genus_70b.npy")
#family = np.load("family_70b.npy")

species_list = list(species[()].keys())
class_list = list(clas[()].keys())
order_list = list(order[()].keys())
#genus_list = list(genus[()].keys())
#family_list = list(family[()].keys())

def update_fn(val):
    if val=="Class":
        return gr.Dropdown(label="Name", choices=class_list, interactive=True)
    elif val=="Order":
        return gr.Dropdown(label="Name", choices=order_list, interactive=True)
    elif val=="Family":
        return gr.Dropdown(label="Name", choices=family_list, interactive=True)
    elif val=="Genus":
        return gr.Dropdown(label="Name", choices=genus_list, interactive=True)
    elif val=="Species":
        return gr.Dropdown(label="Name", choices=species_list, interactive=True)

with gr.Blocks() as demo:
    gr.Markdown(
    """
    # Hierarchical Species Distribution Model!
    This model predicts the distribution of species based on geographic, environmental, and natural language features.
    """)
    inp = gr.Dropdown(label="Taxonomic Hierarchy", choices=["Class", "Order", "Family", "Genus", "Species"])
    out = gr.Dropdown(label="Name", interactive=True)
    inp.change(update_fn, inp, out)

demo.launch()