Update src/streamlit_app.py

src/streamlit_app.py

@@ -12,29 +12,32 @@ forums](https://discuss.streamlit.io).
 
 In the meantime, below is an example of what you can do with just a few lines of code:
 """
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+@st.cache_resource
+def init_robot_system():
+    model_name='h64_32_z2_isoTrue_actvTanh_isoreg1e-07_nepoch1000_bs10'
+    ae, config = load_saved_model(model_name=model_name, device="cpu")
+    robot_chars = RobotCharacteristics()
+    label_data = np.load('./data/label.npy')
+    njnt_data = np.load('.data/njnt.npy')
+    indices_data = np.load('.data/indices.npy')
+    robot_chars.max_joints_per_group=njnt_data
+    return ae, robot_chars
+motion_level = 'easy'
+x_1 = np.load(f'./{motion_level}_motion/X1.npy')
+x_2 = np.load(f'./{motion_level}_motion/X2.npy')
+z_1 = np.load(f'./{motion_level}_motion/Z1.npy')
+
+
+
+st.title("LEGO Latent Space Explorer")
+z1 = st.slider("X", -10.0, 10.0)
+z2 = st.slider("Y", -10.0, 10.0)
+
+if st.button("Generate Design"):
+    ae, robot_chars = init_robot_system()
+    decode = ae.decode(th.tensor(easy_optimal,device=next(ae.parameters()).device,dtype=th.float32)).detach().numpy()
+    full_decode=robot_chars.build_full_interp_from_symmetric(interp_data_sym=decode,threshold=0.5)
+    data_to_visualize = robot_chars.convert_flat_interp_to_dict(full_decode)
+    clip = robot_chars.visualize_data_to_classify(data_to_visualize, save_video=True,vis=True, test = True, xml=True)
+    st.success("디자인 생성 완료!")
+    st.image("result.png") # 생성된 이미지 보여주기