app and assets

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+data/colon.csv filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,286 @@
+import pandas as pd
+import streamlit as st
+from streamlit_calendar import calendar
+from streamlit_timeline import st_timeline
+import numpy as np
+from sklearn.cluster import KMeans
+import altair as alt
+
+st.set_page_config(layout="wide")
+
+# load data
+df = pd.read_csv("data/colon.csv")
+df = df.dropna(subset=["DESCRIPTION", "START"])
+df["BIRTHDATE"] = pd.to_datetime(df["BIRTHDATE"], errors="coerce").dt.date
+df["START"] = pd.to_datetime(df["START"], errors="coerce").dt.date
+df["STOP"] = pd.to_datetime(df["STOP"], errors="coerce").dt.date
+df = df.sort_values(by=["ID", "START", "DESCRIPTION"], ascending=[True, False, True])
+unique_ids = df["ID"].unique()
+
+# inject custom CSS to set the width of the sidebar
+st.markdown(
+    """
+    <style>
+        section[data-testid="stSidebar"] {
+            width: 600px !important; # Set the width to your desired value
+        }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+# pick id
+st.sidebar.title("Patient information")
+st.session_state.id = st.sidebar.selectbox(
+    "Select patient ID:",
+    unique_ids,
+    index=0,
+    placeholder="Type or select ID...",
+)
+
+# sidebar
+name = (
+    df.loc[df["ID"] == st.session_state.id, "NAME"].iloc[0]
+    if not df.loc[df["ID"] == st.session_state.id, "NAME"].empty
+    else None
+)
+
+gender = (
+    df.loc[df["ID"] == st.session_state.id, "GENDER"].iloc[0]
+    if not df.loc[df["ID"] == st.session_state.id, "GENDER"].empty
+    else None
+)
+st.sidebar.write("Name:", name, f" ({gender})")
+
+bd = (
+    df.loc[df["ID"] == st.session_state.id, "BIRTHDATE"].iloc[0]
+    if not df.loc[df["ID"] == st.session_state.id, "BIRTHDATE"].empty
+    else None
+)
+st.sidebar.write("Birthdate:", bd)
+
+race = (
+    df.loc[df["ID"] == st.session_state.id, "RACE"].iloc[0]
+    if not df.loc[df["ID"] == st.session_state.id, "RACE"].empty
+    else None
+)
+
+etn = (
+    df.loc[df["ID"] == st.session_state.id, "ETHNICITY"].iloc[0]
+    if not df.loc[df["ID"] == st.session_state.id, "ETHNICITY"].empty
+    else None
+)
+st.sidebar.write("Race/Ethnicity:", race, " /", etn)
+
+mar = (
+    df.loc[df["ID"] == st.session_state.id, "MARITAL"].iloc[0]
+    if not df.loc[df["ID"] == st.session_state.id, "MARITAL"].empty
+    else None
+)
+st.sidebar.write("Marital status:", mar)
+
+adr = (
+    df.loc[df["ID"] == st.session_state.id, "ADDRESS"].iloc[0]
+    if not df.loc[df["ID"] == st.session_state.id, "ADDRESS"].empty
+    else None
+)
+st.sidebar.write("Address:", adr)
+
+# filter data
+st.session_state.filtered_df = df[df["ID"] == st.session_state.id]
+try:
+    st.session_state.initial_date = (
+        st.session_state.filtered_df["START"].max().strftime("%Y-%m-%d")
+    )
+except:
+    pass
+
+if not st.session_state.filtered_df.empty:
+    st.session_state.events = [
+        {
+            "title": row["DESCRIPTION"],
+            "start": row["START"].strftime("%Y-%m-%d"),
+            "end": row["START"].strftime("%Y-%m-%d"),
+        }
+        for _, row in st.session_state.filtered_df.iterrows()
+    ]
+
+# calendar
+mode = st.sidebar.selectbox(
+    "Calendar Mode:",
+    (
+        "daygrid",
+        "list",
+    ),
+)
+
+calendar_options = {
+    "editable": "true",
+    "navLinks": "true",
+    "selectable": "true",
+}
+
+if mode == "daygrid":
+    calendar_options = {
+        **calendar_options,
+        "headerToolbar": {
+            "left": "today prev,next",
+            "center": "title",
+            "right": "dayGridDay,dayGridWeek,dayGridMonth",
+        },
+        "initialDate": st.session_state.initial_date,
+        "initialView": "dayGridMonth",
+    }
+
+elif mode == "list":
+    calendar_options = {
+        **calendar_options,
+        "initialDate": st.session_state.initial_date,
+        "initialView": "listMonth",
+    }
+
+with st.sidebar:
+    st.session_state.state = calendar(
+        events=st.session_state.get("events", st.session_state.events),
+        options=calendar_options,
+        custom_css="""
+        .fc-event-past {
+            opacity: 0.8;
+        }
+        .fc-event-time {
+            font-style: italic;
+        }
+        .fc-event-title {
+            font-weight: 700;
+        }
+        .fc-toolbar-title {
+            font-size: 2rem;
+        }
+        .fc-button {
+            background-color: #4CAF50;
+            color: #ffffff;
+            border: none;
+            cursor: pointer;
+        }
+        .fc-button:hover {
+            background-color: #45a049;
+        }
+        .fc-button-primary {
+            background-color: #008CBA;
+        }
+        .fc-button-primary:hover {
+            background-color: #007bb5;
+        }
+        .fc-button-secondary {
+            background-color: #e7e7e7;
+            color: black;
+        }
+        .fc-button-secondary:hover {
+            background-color: #ddd;
+        }
+        """,
+        key=mode,
+    )
+
+
+if st.session_state.state.get("eventsSet") is not None:
+    st.session_state["events"] = st.session_state.state["eventsSet"]
+
+# clustering
+col1, col2 = st.columns([1, 2])
+
+with col1:
+    # training on lung data
+    # add slider to select number of clusters
+    st.session_state.n_clusters = st.slider("Select number of clusters", 2, 5, 5)
+    if st.button("Train model"):
+        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(
+            np.array
+        )
+        training_data = st.session_state.df["DESCRIPTION"].tolist()
+
+        transformed_data = []
+        for array in training_data:
+            unique_values = np.unique(array)
+            value_to_int = {value: idx + 1 for idx, value in enumerate(unique_values)}
+            transformed_array = np.vectorize(value_to_int.get)(array)
+            transformed_data.append(transformed_array)
+
+        max_length = max(len(array) for array in transformed_data)
+        padded_data = [
+            np.pad(array, (0, max_length - len(array)), "constant")
+            for array in transformed_data
+        ]
+        padded_data_array = np.vstack(padded_data)
+
+        st.session_state.kmeans = KMeans(
+            n_clusters=st.session_state.n_clusters, random_state=42
+        )
+        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])
+
+    try:
+        st.session_state.label_counts = (
+            pd.Series(st.session_state.cluster_labels).value_counts().sort_index()
+        )
+        st.session_state.cluster_df = pd.DataFrame(
+            {
+                "Cluster Label": st.session_state.label_counts.index,
+                "Count": st.session_state.label_counts.values,
+            }
+        )
+        # st.bar_chart(st.session_state.cluster_df)
+        chart = (
+            alt.Chart(st.session_state.cluster_df)
+            .mark_bar()
+            .encode(x="Cluster Label:O", y="Count:Q")
+            .properties(title="Number of people per cluster")
+            .configure_legend(disable=True)  # Disable the legend
+        )
+        st.altair_chart(chart, use_container_width=True)
+    except:
+        pass
+
+with col2:
+    try:
+        st.session_state.selected_cluster = st.selectbox(
+            "Select cluster to view descriptions",
+            np.unique(st.session_state.cluster_labels),
+            0,
+        )
+        st.session_state.indices = np.where(
+            st.session_state.cluster_labels == st.session_state.selected_cluster
+        )[0]
+        st.session_state.seq_df = st.session_state.df.loc[st.session_state.indices]
+        st.write(f"Descriptions for cluster {st.session_state.selected_cluster}:")
+        st.dataframe(
+            st.session_state.seq_df["DESCRIPTION"],
+            use_container_width=True,
+        )
+    except:
+        pass
+
+# timeline
+if not st.session_state.filtered_df.empty:
+    st.session_state.item = [
+        {
+            "id": id,
+            "content": row["DESCRIPTION"],
+            "start": row["START"].strftime("%Y-%m-%d"),
+        }
+        for id, (_, row) in enumerate(st.session_state.filtered_df.iterrows())
+    ]
+
+st.session_state.timeline = st_timeline(
+    st.session_state.item, groups=[], options={}, height="300px", width="100%"
+)

data/colon.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d82d1155a2a33b6af26913fcc928f7ccf7b38c982618a54abd7084f1b4289400
+size 29236073

requirements.txt

@@ -0,0 +1,7 @@
+altair==5.3.0
+numpy==2.0.1
+pandas==2.2.2
+scikit-learn==1.5.1
+streamlit==1.37.0
+streamlit-calendar==1.2.0
+streamlit-vis-timeline==0.3.0