update colors

app.py

@@ -99,6 +99,7 @@ if not st.session_state.filtered_df.empty:
     st.session_state.events = [
         {
             "title": row["DESCRIPTION"],
+            "color": "#3a6ad6",
             "start": row["START"].strftime("%Y-%m-%d"),
             "end": row["START"].strftime("%Y-%m-%d"),
         }
@@ -166,10 +167,10 @@ with st.sidebar:
             background-color: #45a049;
         }
         .fc-button-primary {
-            background-color: #008CBA;
+            background-color: #3a6ad6;
         }
         .fc-button-primary:hover {
-            background-color: #007bb5;
+            background-color: #3a6ad6;
         }
         .fc-button-secondary {
             background-color: #e7e7e7;
@@ -190,10 +191,28 @@ if st.session_state.state.get("eventsSet") is not None:
 col1, col2 = st.columns([1, 2])
 
 with col1:
-    # training on lung data
-    # add slider to select number of clusters
+    # clustering
+    st.markdown(
+        """ 
+    <style>
+    div.stSlider > div[data-baseweb="slider"] > div > div > div[role="slider"] {
+        background-color: #3a6ad6; 
+        box-shadow: rgba(58, 106, 214, 0.2) 0px 0px 0px 0.2rem;
+    }
+    div.stSlider > div[data-baseweb="slider"] > div > div > div > div {
+        color: #3a6ad6;
+    }
+    div.stSlider > div[data-baseweb = "slider"] > div > div {{
+    background: linear-gradient(to right, #3a6ad6 0%, 
+                                #3a6ad6 {NB}%, 
+                                #3a6ad6 {NB}%, 
+                                #3a6ad6 100%); }}
+    </style>
+    """,
+        unsafe_allow_html=True,
+    )
     st.session_state.n_clusters = st.slider("Select number of clusters", 2, 5, 5)
-    if st.button("Train model"):
+    if st.button("Show cluster"):
         df = df[["ID", "START", "STOP", "DESCRIPTION"]]
         st.session_state.df = df.groupby("ID").agg({"DESCRIPTION": list}).reset_index()
         st.session_state.df["DESCRIPTION"] = st.session_state.df["DESCRIPTION"].apply(
@@ -221,13 +240,13 @@ with col1:
         st.session_state.cluster_labels = st.session_state.kmeans.fit_predict(
             padded_data_array
         )
-        st.write("Model trained successfully!")
-    # clustering
-    if st.button("Show cluster"):
         st.session_state.idx = st.session_state.df.index[
             st.session_state.df["ID"] == st.session_state.id
         ]
-        st.write("Cluster:", st.session_state.cluster_labels[st.session_state.idx])
+        st.write(
+            "This patient belonngs to cluster:",
+            st.session_state.cluster_labels[st.session_state.idx][0],
+        )
 
     try:
         st.session_state.label_counts = (