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import streamlit as st | |
from huggingface_hub import HfFolder, snapshot_download | |
HfFolder().save_token(st.secrets['etoken']) | |
snapshot_download("OpenShape/openshape-demo-support", local_dir='.') | |
import numpy | |
import openshape | |
from openshape.demo import misc_utils, classification | |
def load_openshape(name): | |
return openshape.load_pc_encoder(name) | |
f32 = numpy.float32 | |
# clip_model, clip_prep = load_openclip() | |
model_g14 = openshape.load_pc_encoder('openshape-pointbert-vitg14-rgb') | |
st.title("OpenShape Demo") | |
load_data = misc_utils.input_3d_shape() | |
prog = st.progress(0.0, "Idle") | |
try: | |
if st.button("Run Classification on LVIS Categories"): | |
pc = load_data(prog) | |
col2 = misc_utils.render_pc(pc) | |
prog.progress(0.5, "Running Classification") | |
pred = classification.pred_lvis_sims(model_g14, pc) | |
with col2: | |
for i, (cat, sim) in zip(range(5), pred.items()): | |
st.text(cat) | |
st.caption("Similarity %.4f" % sim) | |
prog.progress(1.0, "Idle") | |
except Exception as exc: | |
st.error(repr(exc)) | |