Create app.py

app.py

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+import io
+import textwrap
+
+import numpy as np
+import pandas as pd
+import streamlit as st
+from sklearn.manifold import TSNE
+import plotly.express as px
+
+# --------------  Helper functions -------------------------------------------
+EXAMPLE_SHAPES = {
+    "Cube (3-D, 8 vertices)": np.array([
+        [0, 0, 0], [0, 0, 1],
+        [0, 1, 0], [0, 1, 1],
+        [1, 0, 0], [1, 0, 1],
+        [1, 1, 0], [1, 1, 1]
+    ]),
+    "Square pyramid (3-D, 5 vertices)": np.array([
+        [-1, -1,  0],
+        [ 1, -1,  0],
+        [ 1,  1,  0],
+        [-1,  1,  0],
+        [ 0,  0,  1]
+    ])
+}
+
+
+def parse_text_points(text: str) -> np.ndarray:
+    """
+    Parse a multiline string of comma- or whitespace-separated numbers
+    into an (n_points, n_dims) array.
+    """
+    cleaned = textwrap.dedent(text.strip())
+    rows = [row for row in cleaned.splitlines() if row.strip()]
+    data = [list(map(float, row.replace(",", " ").split())) for row in rows]
+    return np.array(data, dtype=float)
+
+
+def run_tsne(data: np.ndarray, perplexity: float, seed: int) -> np.ndarray:
+    tsne = TSNE(
+        n_components=2,
+        perplexity=perplexity,
+        random_state=seed,
+        init="pca"
+    )
+    return tsne.fit_transform(data)
+# ---------------------------------------------------------------------------
+
+
+st.title("🌀 t-SNE Explorer for n-D Point Clouds")
+st.markdown(
+    """
+    Upload or paste your points, choose parameters, and see how
+    **t-SNE** flattens them into 2-D.  
+    *Example shapes* are provided for quick experimentation.
+    """
+)
+
+# --- Sidebar controls -------------------------------------------------------
+with st.sidebar:
+    st.header("1️⃣  Choose data source")
+    source = st.radio(
+        "Data input method",
+        ["Example shape", "Upload CSV/TXT", "Paste raw text"]
+    )
+
+    if source == "Example shape":
+        shape_key = st.selectbox("Pick a shape", list(EXAMPLE_SHAPES.keys()))
+        data_raw = EXAMPLE_SHAPES[shape_key]
+
+    elif source == "Upload CSV/TXT":
+        file = st.file_uploader("Upload coordinates file (*.csv / *.txt)")
+        if file:
+            text = io.StringIO(file.getvalue().decode("utf-8")).read()
+            data_raw = parse_text_points(text)
+        else:
+            st.stop()
+
+    else:  # Paste text
+        placeholder = "e.g.\n0,0,0\n0,0,1\n0,1,0\n..."
+        text = st.text_area("Paste coordinates (one point per line)", height=200, placeholder=placeholder)
+        if not text.strip():
+            st.stop()
+        data_raw = parse_text_points(text)
+
+    st.divider()
+    st.header("2️⃣  t-SNE parameters")
+    perplexity = st.slider("Perplexity", 5.0, 50.0, 30.0, 1.0)
+    seed = st.number_input("Random seed", value=42, step=1)
+    run_button = st.button("Run t-SNE 🚀")
+
+# --- Main area --------------------------------------------------------------
+if run_button:
+    if data_raw.ndim != 2 or data_raw.shape[0] < 2:
+        st.error("Need at least two points; check your input.")
+        st.stop()
+
+    if perplexity >= data_raw.shape[0]:
+        st.error("Perplexity must be less than the number of points.")
+        st.stop()
+
+    embedding = run_tsne(data_raw, perplexity, seed)
+    df_plot = pd.DataFrame(embedding, columns=["x", "y"])
+
+    st.subheader("2-D embedding")
+    fig = px.scatter(df_plot, x="x", y="y", width=700, height=500)
+    fig.update_traces(marker=dict(size=10))
+    fig.update_layout(margin=dict(l=20, r=20, t=30, b=20))
+    st.plotly_chart(fig, use_container_width=True)
+
+    with st.expander("Show raw data"):
+        st.write(pd.DataFrame(data_raw))