add plotly examples to doc

app/src/content/embeds/plotly/bar.py

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+import plotly.graph_objects as go
+import plotly.io as pio
+import numpy as np
+
+"""
+Stacked bar chart: GPU memory breakdown vs sequence length, with menus for Model Size and Recomputation.
+Responsive, no zoom/pan, clean hover; styled to match the minimal theme.
+"""
+
+# Axes
+seq_labels = ["1024", "2048", "4096", "8192"]
+seq_scale = np.array([1, 2, 4, 8], dtype=float)
+
+# Components and colors (aligned with the provided example)
+components = [
+    ("parameters", "rgb(78, 165, 183)"),
+    ("gradients",  "rgb(227, 138, 66)"),
+    ("optimizer",  "rgb(232, 137, 171)"),
+    ("activations", "rgb(206, 192, 250)"),
+]
+
+# Model sizes and base memory (GB) for params/grad/opt (constant vs seq), by size
+model_sizes = ["1B", "3B", "8B", "70B", "405B"]
+params_mem = {
+    "1B": 4.0,
+    "3B": 13.3,
+    "8B": 26.0,
+    "70B": 244.0,
+    "405B": 1520.0,
+}
+# Optimizer ~= 2x params; gradients ~= params (illustrative)
+
+# Activations base coefficient per size (growth ~ coeff * (seq/1024)^2)
+act_coeff = {
+    "1B": 3.6,
+    "3B": 9.3,
+    "8B": 46.2,
+    "70B": 145.7,
+    "405B": 1519.9,
+}
+
+def activations_curve(size_key: str, recompute: str) -> np.ndarray:
+    base = act_coeff[size_key] * (seq_scale ** 2)
+    if recompute == "selective":
+        return base * 0.25
+    if recompute == "full":
+        return base * (1.0/16.0)
+    return base
+
+def stack_for(size_key: str, recompute: str):
+    p = np.full_like(seq_scale, params_mem[size_key], dtype=float)
+    g = np.full_like(seq_scale, params_mem[size_key], dtype=float)
+    o = np.full_like(seq_scale, 2.0 * params_mem[size_key], dtype=float)
+    a = activations_curve(size_key, recompute)
+    return {
+        "parameters": p,
+        "gradients": g,
+        "optimizer": o,
+        "activations": a,
+    }
+
+# Precompute all combinations
+recomp_modes = ["none", "selective", "full"]
+Y = {mode: {size: stack_for(size, mode) for size in model_sizes} for mode in recomp_modes}
+
+# Build traces: 4 traces per size (20 total). Start with size index 0 visible
+fig = go.Figure()
+for size in model_sizes:
+    for comp_name, color in components:
+        fig.add_bar(
+            x=seq_labels,
+            y=Y["none"][size][comp_name],
+            name=comp_name,
+            marker=dict(color=color),
+            hovertemplate="Seq len=%{x}<br>Mem=%{y:.1f}GB<br>%{data.name}<extra></extra>",
+            showlegend=True,
+            visible=(size == model_sizes[0]),
+        )
+
+# Compute y-axis ranges per size and recomputation
+def max_total(size: str, mode: str) -> float:
+    stacks = Y[mode][size]
+    totals = stacks["parameters"] + stacks["gradients"] + stacks["optimizer"] + stacks["activations"]
+    return float(np.max(totals))
+
+layout_y_ranges = {mode: {size: 1.05 * max_total(size, mode) for size in model_sizes} for mode in recomp_modes}
+
+# Layout
+fig.update_layout(
+    barmode="stack",
+    autosize=True,
+    paper_bgcolor="rgba(0,0,0,0)",
+    plot_bgcolor="rgba(0,0,0,0)",
+    margin=dict(l=40, r=28, t=20, b=40),
+    hovermode="x unified",
+    legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="left", x=0),
+    xaxis=dict(title=dict(text="Sequence Length"), fixedrange=True),
+    yaxis=dict(title=dict(text="Memory (GB)"), fixedrange=True),
+)
+
+# Updatemenus: Model Size (toggle visibility)
+buttons_sizes = []
+for i, size in enumerate(model_sizes):
+    visible = [False] * (len(model_sizes) * len(components))
+    start = i * len(components)
+    for j in range(len(components)):
+        visible[start + j] = True
+    buttons_sizes.append(dict(
+        label=size,
+        method="update",
+        args=[
+            {"visible": visible},
+            {"yaxis": {"range": [0, layout_y_ranges["none"][size]]}},
+        ],
+    ))
+
+# Updatemenus: Recomputation (restyle y across all traces)
+def y_for_mode(mode: str):
+    ys = []
+    for size in model_sizes:
+        stacks = Y[mode][size]
+        for comp_name, _ in components:
+            ys.append(stacks[comp_name])
+    return ys
+
+buttons_recomp = []
+for mode, label in [("none", "None"), ("selective", "selective"), ("full", "full")]:
+    ys = y_for_mode(mode)
+    # Flatten into the format expected by Plotly for multiple traces
+    buttons_recomp.append(dict(
+        label=label,
+        method="update",
+        args=[
+            {"y": ys},
+            {"yaxis": {"range": [0, max(layout_y_ranges[mode].values())]}},
+        ],
+    ))
+
+fig.update_layout(
+    updatemenus=[
+        dict(
+            type="dropdown",
+            x=1.03, xanchor="left",
+            y=0.60, yanchor="top",
+            showactive=True,
+            active=0,
+            buttons=buttons_sizes,
+        ),
+        dict(
+            type="dropdown",
+            x=1.03, xanchor="left",
+            y=0.40, yanchor="top",
+            showactive=True,
+            active=0,
+            buttons=buttons_recomp,
+        ),
+    ],
+    annotations=[
+        dict(text="Model Size:", x=1.03, xanchor="left", xref="paper", y=0.60, yanchor="bottom", yref="paper", showarrow=False),
+        dict(text="Recomputation:", x=1.03, xanchor="left", xref="paper", y=0.40, yanchor="bottom", yref="paper", showarrow=False),
+    ],
+)
+
+# Write fragment
+fig.write_html("./plotly-bar.html",
+               include_plotlyjs=False,
+               full_html=False,
+               config={
+                   'displayModeBar': False,
+                   'responsive': True,
+                   'scrollZoom': False,
+               })
+

app/src/content/embeds/plotly/heatmap.py

@@ -0,0 +1,125 @@
+import plotly.graph_objects as go
+import plotly.io as pio
+import numpy as np
+import datetime as dt
+import os
+
+"""
+Calendar-like heatmap (GitHub-style) over the last 52 weeks.
+Minimal, responsive, transparent background; suitable for Distill.
+"""
+
+# Parameters
+NUM_WEEKS = 52
+DAYS = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
+
+# Build dates matrix (7 rows x NUM_WEEKS columns)
+today = dt.date.today()
+# Align to start of current week (Monday)
+start = today - dt.timedelta(days=(today.weekday()))  # Monday of current week
+weeks = [start - dt.timedelta(weeks=w) for w in range(NUM_WEEKS-1, -1, -1)]
+dates = [[weeks[c] + dt.timedelta(days=r) for c in range(NUM_WEEKS)] for r in range(7)]
+
+# Generate values (synthetic) — smooth seasonal pattern + noise
+def gen_value(d: dt.date) -> float:
+    day_of_year = d.timetuple().tm_yday
+    base = 0.5 + 0.45 * np.sin(2 * np.pi * (day_of_year / 365.0))
+    noise = np.random.default_rng(hash(d) % 2**32).uniform(-0.15, 0.15)
+    return max(0.0, min(1.0, base + noise))
+
+z = [[gen_value(d) for d in row] for row in dates]
+custom = [[d.isoformat() for d in row] for row in dates]
+
+# Colors aligned with other charts (slate / blue / gray)
+colorscale = [
+    [0.00, "#e5e7eb"],   # light gray background for low
+    [0.40, "#64748b"],   # slate-500
+    [0.75, "#2563eb"],   # blue-600
+    [1.00, "#4b5563"],   # gray-600 (high end accent)
+]
+
+fig = go.Figure(
+    data=go.Heatmap(
+        z=z,
+        x=[w.isoformat() for w in weeks],
+        y=DAYS,
+        colorscale=colorscale,
+        showscale=False,
+        hovertemplate="Date: %{customdata}<br>Value: %{z:.2f}<extra></extra>",
+        customdata=custom,
+        xgap=2,
+        ygap=2,
+    )
+)
+
+fig.update_layout(
+    autosize=True,
+    paper_bgcolor="rgba(0,0,0,0)",
+    plot_bgcolor="rgba(0,0,0,0)",
+    margin=dict(l=28, r=12, t=8, b=28),
+    xaxis=dict(
+        showgrid=False,
+        zeroline=False,
+        showline=False,
+        ticks="",
+        showticklabels=False,
+        fixedrange=True,
+    ),
+    yaxis=dict(
+        showgrid=False,
+        zeroline=False,
+        showline=False,
+        ticks="",
+        tickfont=dict(size=12, color="rgba(0,0,0,0.65)"),
+        fixedrange=True,
+    ),
+)
+
+post_script = """
+(function(){
+  var plots = document.querySelectorAll('.js-plotly-plot');
+  plots.forEach(function(gd){
+    function round(){
+      try {
+        var root = gd && gd.parentNode ? gd.parentNode : document;
+        var rects = root.querySelectorAll('.hoverlayer .hovertext rect');
+        rects.forEach(function(r){ r.setAttribute('rx', 8); r.setAttribute('ry', 8); });
+      } catch(e) {}
+    }
+    if (gd && gd.on){
+      gd.on('plotly_hover', round);
+      gd.on('plotly_unhover', round);
+      gd.on('plotly_relayout', round);
+    }
+    setTimeout(round, 0);
+  });
+})();
+"""
+
+html = pio.to_html(
+    fig,
+    include_plotlyjs=False,
+    full_html=False,
+    post_script=post_script,
+    config={
+        "displayModeBar": False,
+        "responsive": True,
+        "scrollZoom": False,
+        "doubleClick": False,
+        "modeBarButtonsToRemove": [
+            "zoom2d", "pan2d", "select2d", "lasso2d",
+            "zoomIn2d", "zoomOut2d", "autoScale2d", "resetScale2d",
+            "toggleSpikelines"
+        ],
+    },
+)
+
+fig.write_html("./plotly-heatmap.html", 
+               include_plotlyjs=False, 
+               full_html=False, 
+               config={
+                   'displayModeBar': False,
+                   'responsive': True, 
+                   'scrollZoom': False,
+               })
+

app/src/content/embeds/plotly/line.py

@@ -0,0 +1,276 @@
+import plotly.graph_objects as go
+import plotly.io as pio
+import numpy as np
+import os
+import uuid
+
+"""
+Interactive line chart example (Baseline / Improved / Target) with a live slider.
+
+Context: research-style training curves for multiple datasets (CIFAR-10, CIFAR-100, ImageNet-1K).
+The slider "Augmentation α" blends the Improved curve between the Baseline (α=0)
+and an augmented counterpart (α=1) via a simple mixing equation.
+Export remains responsive, with no zoom and no mode bar.
+"""
+
+# Grid (x) and parameterization
+N = 240
+x = np.linspace(0, 1, N)
+
+# Logistic helper for smooth learning curves
+def logistic(xv: np.ndarray, ymin: float, ymax: float, k: float, x0: float) -> np.ndarray:
+    return ymin + (ymax - ymin) / (1.0 + np.exp(-k * (xv - x0)))
+
+# Plausible dataset params (baseline vs augmented) + a constant target line
+datasets_params = [
+    {
+        "name": "CIFAR-10",
+        "base": {"ymin": 0.10, "ymax": 0.90, "k": 10.0, "x0": 0.55},
+        "aug":  {"ymin": 0.15, "ymax": 0.96, "k": 12.0, "x0": 0.40},
+        "target": 0.97,
+    },
+    {
+        "name": "CIFAR-100",
+        "base": {"ymin": 0.05, "ymax": 0.70, "k": 9.5, "x0": 0.60},
+        "aug":  {"ymin": 0.08, "ymax": 0.80, "k": 11.0, "x0": 0.45},
+        "target": 0.85,
+    },
+    {
+        "name": "ImageNet-1K",
+        "base": {"ymin": 0.02, "ymax": 0.68, "k": 8.5, "x0": 0.65},
+        "aug":  {"ymin": 0.04, "ymax": 0.75, "k": 9.5, "x0": 0.50},
+        "target": 0.82,
+    },
+]
+
+# Initial dataset index and alpha
+alpha0 = 0.7
+ds0 = datasets_params[0]
+base0 = logistic(x, **ds0["base"])
+aug0 = logistic(x, **ds0["aug"])
+target0 = np.full_like(x, ds0["target"], dtype=float)
+
+# Traces: Baseline (fixed), Improved (blended by α), Target (constant goal)
+blend = lambda l, e, a: (1 - a) * l + a * e
+y1 = base0
+y2 = blend(base0, aug0, alpha0)
+y3 = target0
+
+color_base = "#64748b"     # slate-500
+color_improved = "#F981D4" # pink
+color_target = "#4b5563"   # gray-600 (dash)
+
+fig = go.Figure()
+fig.add_trace(
+    go.Scatter(
+        x=x,
+        y=y1,
+        name="Baseline",
+        mode="lines",
+        line=dict(color=color_base, width=2, shape="spline", smoothing=0.6),
+        hovertemplate="<b>%{fullData.name}</b><br>x=%{x:.2f}<br>y=%{y:.3f}<extra></extra>",
+        showlegend=True,
+    )
+)
+fig.add_trace(
+    go.Scatter(
+        x=x,
+        y=y2,
+        name="Improved",
+        mode="lines",
+        line=dict(color=color_improved, width=2, shape="spline", smoothing=0.6),
+        hovertemplate="<b>%{fullData.name}</b><br>x=%{x:.2f}<br>y=%{y:.3f}<extra></extra>",
+        showlegend=True,
+    )
+)
+fig.add_trace(
+    go.Scatter(
+        x=x,
+        y=y3,
+        name="Target",
+        mode="lines",
+        line=dict(color=color_target, width=2, dash="dash"),
+        hovertemplate="<b>%{fullData.name}</b><br>x=%{x:.2f}<br>y=%{y:.3f}<extra></extra>",
+        showlegend=True,
+    )
+)
+
+fig.update_layout(
+    autosize=True,
+    paper_bgcolor="rgba(0,0,0,0)",
+    plot_bgcolor="rgba(0,0,0,0)",
+    margin=dict(l=40, r=28, t=20, b=40),
+    hovermode="x unified",
+    legend=dict(
+        orientation="v",
+        x=1,
+        y=0,
+        xanchor="right",
+        yanchor="bottom",
+        bgcolor="rgba(255,255,255,0)",
+        borderwidth=0,
+    ),
+    hoverlabel=dict(
+        bgcolor="white",
+        font=dict(color="#111827", size=12),
+        bordercolor="rgba(0,0,0,0.15)",
+        align="left",
+        namelength=-1,
+    ),
+    xaxis=dict(
+        showgrid=False,
+        zeroline=False,
+        showline=True,
+        linecolor="rgba(0,0,0,0.25)",
+        linewidth=1,
+        ticks="outside",
+        ticklen=6,
+        tickcolor="rgba(0,0,0,0.25)",
+        tickfont=dict(size=12, color="rgba(0,0,0,0.55)"),
+        title=None,
+        automargin=True,
+        fixedrange=True,
+    ),
+    yaxis=dict(
+        showgrid=False,
+        zeroline=False,
+        showline=True,
+        linecolor="rgba(0,0,0,0.25)",
+        linewidth=1,
+        ticks="outside",
+        ticklen=6,
+        tickcolor="rgba(0,0,0,0.25)",
+        tickfont=dict(size=12, color="rgba(0,0,0,0.55)"),
+        title=None,
+        tickformat=".2f",
+        rangemode="tozero",
+        automargin=True,
+        fixedrange=True,
+    ),
+)
+
+# Write the fragment next to this file into src/fragments/line.html (robust path)
+output_path = os.path.join(os.path.dirname(__file__), "fragments", "line.html")
+os.makedirs(os.path.dirname(output_path), exist_ok=True)
+
+# Inject a small post-render script to round the hover box corners
+post_script = """
+(function(){
+  function attach(gd){
+    function round(){
+      try {
+        var root = gd && gd.parentNode ? gd.parentNode : document;
+        var rects = root.querySelectorAll('.hoverlayer .hovertext rect');
+        rects.forEach(function(r){ r.setAttribute('rx', 8); r.setAttribute('ry', 8); });
+      } catch(e) {}
+    }
+    if (gd && gd.on) {
+      gd.on('plotly_hover', round);
+      gd.on('plotly_unhover', round);
+      gd.on('plotly_relayout', round);
+    }
+    setTimeout(round, 0);
+  }
+  var plots = document.querySelectorAll('.js-plotly-plot');
+  plots.forEach(attach);
+})();
+"""
+
+html_plot = pio.to_html(
+    fig,
+    include_plotlyjs=False,
+    full_html=False,
+    post_script=post_script,
+    config={
+        "displayModeBar": False,
+        "responsive": True,
+        "scrollZoom": False,
+        "doubleClick": False,
+        "modeBarButtonsToRemove": [
+            "zoom2d", "pan2d", "select2d", "lasso2d",
+            "zoomIn2d", "zoomOut2d", "autoScale2d", "resetScale2d",
+            "toggleSpikelines"
+        ],
+    },
+)
+
+# Build a self-contained fragment with a live slider (no mouseup required)
+uid = uuid.uuid4().hex[:8]
+slider_id = f"line-ex-alpha-{uid}"
+container_id = f"line-ex-container-{uid}"
+
+slider_tpl = '''
+<div id="__CID__">
+  __PLOT__
+  <div class="plotly_controls" style="margin-top:12px; display:flex; gap:16px; align-items:center;">
+    <label style="font-size:12px;color:rgba(0,0,0,.65); display:flex; align-items:center; gap:6px; white-space:nowrap; padding:6px 10px;">
+      Dataset
+      <select id="__DSID__" style="font-size:12px; padding:2px 6px;">
+        <option value="0">CIFAR-10</option>
+        <option value="1">CIFAR-100</option>
+        <option value="2">ImageNet-1K</option>
+      </select>
+    </label>
+    <label style="font-size:12px;color:rgba(0,0,0,.65);display:flex;align-items:center;gap:10px; flex:1; padding:6px 10px;">
+      Augmentation α
+      <input id="__SID__" type="range" min="0" max="1" step="0.01" value="__A0__" style="flex:1;">
+      <span class="alpha-value">__A0__</span>
+    </label>
+  </div>
+</div>
+<script>
+(function(){
+  var container = document.getElementById('__CID__');
+  if(!container) return;
+  var gd = container.querySelector('.js-plotly-plot');
+  var slider = document.getElementById('__SID__');
+  var dsSelect = document.getElementById('__DSID__');
+  var valueEl = container.querySelector('.alpha-value');
+  var N = __N__;
+  var xs = Array.from({length: N}, function(_,i){ return i/(N-1); });
+  function logistic(x, ymin, ymax, k, x0){ return ymin + (ymax - ymin) / (1 + Math.exp(-k*(x - x0))); }
+  function blend(l,e,a){ return (1-a)*l + a*e; }
+  var datasets = [
+    { name:'CIFAR-10',  base:{ymin:0.10,ymax:0.90,k:10.0,x0:0.55}, aug:{ymin:0.15,ymax:0.96,k:12.0,x0:0.40}, target:0.97 },
+    { name:'CIFAR-100', base:{ymin:0.05,ymax:0.70,k:9.5,x0:0.60},  aug:{ymin:0.08,ymax:0.80,k:11.0,x0:0.45},  target:0.85 },
+    { name:'ImageNet-1K', base:{ymin:0.02,ymax:0.68,k:8.5,x0:0.65}, aug:{ymin:0.04,ymax:0.75,k:9.5,x0:0.50},  target:0.82 }
+  ];
+  var dsi = 0;
+  var yb = xs.map(function(x){ return logistic(x, datasets[dsi].base.ymin, datasets[dsi].base.ymax, datasets[dsi].base.k, datasets[dsi].base.x0); });
+  var ya = xs.map(function(x){ return logistic(x, datasets[dsi].aug.ymin, datasets[dsi].aug.ymax, datasets[dsi].aug.k, datasets[dsi].aug.x0); });
+  var yt = xs.map(function(){ return datasets[dsi].target; });
+  function applyAlpha(a){
+    var yi = yb.map(function(v,i){ return blend(v, ya[i], a); });
+    Plotly.restyle(gd, {y:[yi]}, [1]); // only Improved changes with α
+    if(valueEl) valueEl.textContent = a.toFixed(2);
+  }
+  function applyDataset(){
+    var d = datasets[dsi];
+    yb = xs.map(function(x){ return logistic(x, d.base.ymin, d.base.ymax, d.base.k, d.base.x0); });
+    ya = xs.map(function(x){ return logistic(x, d.aug.ymin, d.aug.ymax, d.aug.k, d.aug.x0); });
+    yt = xs.map(function(){ return d.target; });
+    var a = parseFloat(slider.value)||0;
+    var yi = yb.map(function(v,i){ return blend(v, ya[i], a); });
+    Plotly.restyle(gd, {y:[yb]}, [0]); // Baseline
+    Plotly.restyle(gd, {y:[yi]}, [1]); // Improved (blended)
+    Plotly.restyle(gd, {y:[yt]}, [2]); // Target
+  }
+  var initA = parseFloat(slider.value)||0;
+  slider.addEventListener('input', function(e){ applyAlpha(parseFloat(e.target.value)||0); });
+  dsSelect.addEventListener('change', function(e){ dsi = parseInt(e.target.value)||0; applyDataset(); });
+  setTimeout(function(){ applyDataset(); applyAlpha(initA); }, 0);
+})();
+</script>
+'''
+
+slider_html = (slider_tpl
+    .replace('__CID__', container_id)
+    .replace('__SID__', slider_id)
+    .replace('__A0__', f"{alpha0:.2f}")
+    .replace('__N__', str(N))
+    .replace('__PLOT__', html_plot)
+)
+
+with open("./plotly-line.html", "w", encoding="utf-8") as f:
+    f.write(slider_html)
+

app/src/content/embeds/plotly/poetry.lock

@@ -0,0 +1,511 @@
+# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand.
+
+[[package]]
+name = "certifi"
+version = "2025.8.3"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
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+xml = ["lxml (>=4.9.2)"]
+
+[[package]]
+name = "plotly"
+version = "5.24.1"
+description = "An open-source, interactive data visualization library for Python"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
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+
+[package.dependencies]
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+tenacity = ">=6.2.0"
+
+[[package]]
+name = "python-dateutil"
+version = "2.9.0.post0"
+description = "Extensions to the standard Python datetime module"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["main"]
+files = [
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+]
+
+[package.dependencies]
+six = ">=1.5"
+
+[[package]]
+name = "pytz"
+version = "2025.2"
+description = "World timezone definitions, modern and historical"
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "pytz-2025.2-py2.py3-none-any.whl", hash = "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00"},
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+
+[[package]]
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+description = "Python HTTP for Humans."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
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+charset_normalizer = ">=2,<4"
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+urllib3 = ">=1.21.1,<3"
+
+[package.extras]
+socks = ["PySocks (>=1.5.6,!=1.5.7)"]
+use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
+
+[[package]]
+name = "six"
+version = "1.17.0"
+description = "Python 2 and 3 compatibility utilities"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["main"]
+files = [
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+    {file = "six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"},
+]
+
+[[package]]
+name = "tenacity"
+version = "9.1.2"
+description = "Retry code until it succeeds"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
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+    {file = "tenacity-9.1.2.tar.gz", hash = "sha256:1169d376c297e7de388d18b4481760d478b0e99a777cad3a9c86e556f4b697cb"},
+]
+
+[package.extras]
+doc = ["reno", "sphinx"]
+test = ["pytest", "tornado (>=4.5)", "typeguard"]
+
+[[package]]
+name = "tzdata"
+version = "2025.2"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+groups = ["main"]
+files = [
+    {file = "tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8"},
+    {file = "tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9"},
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+
+[[package]]
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+version = "2.5.0"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
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+]
+
+[package.extras]
+brotli = ["brotli (>=1.0.9) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\""]
+h2 = ["h2 (>=4,<5)"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
+
+[metadata]
+lock-version = "2.1"
+python-versions = ">=3.10,<3.13"
+content-hash = "a7f64c43efcba78952701498a72a8fe503e995841717b2d5de4c9aa20c9a996a"

app/src/content/embeds/plotly/pyproject.toml

@@ -0,0 +1,20 @@
+[tool.poetry]
+name = "blogpost-fine-tasks-python"
+version = "0.1.0"
+description = "Plotly fragment generation scripts and HTML/Markdown conversions for the blogpost."
+package-mode = false
+
+[tool.poetry.dependencies]
+python = ">=3.10,<3.13"
+Markdown = "^3.6"
+requests = "^2.32.3"
+numpy = "^2.0.0"
+pandas = "^2.2.2"
+plotly = "^5.24.0"
+
+[tool.poetry.scripts]
+html-to-md = "convert_to_md:main"
+
+[build-system]
+requires = ["poetry-core>=1.5.0"]
+build-backend = "poetry.core.masonry.api"