Add Gradio app

app.py

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+from pathlib import Path
+
+import gradio as gr
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+
+
+CSV_PATH = Path("metrics.csv")
+
+
+def load_data() -> pd.DataFrame:
+    df = pd.read_csv(CSV_PATH)
+    # Normalize/parse columns
+    if "date" not in df.columns:
+        raise ValueError("Expected a 'date' column in metrics.csv")
+    df["date"] = pd.to_datetime(df["date"], errors="coerce")
+    df = df.sort_values("date").reset_index(drop=True)
+    # Ensure numeric columns are numeric
+    for c in df.columns:
+        if c == "date":
+            continue
+        df[c] = pd.to_numeric(df[c], errors="coerce")
+    return df
+
+
+def line_figure(df: pd.DataFrame, cols: list[str], title: str, yaxis_title: str = "") -> go.Figure:
+    if not cols:
+        # Empty placeholder so the UI doesn't error
+        fig = go.Figure()
+        fig.update_layout(title=f"{title} (no series selected)")
+        return fig
+    fig = px.line(
+        df,
+        x="date",
+        y=cols,
+        markers=True,
+        title=title,
+    )
+    # Improve layout for time series
+    fig.update_layout(
+        legend_title_text="Series",
+        xaxis_title="Date",
+        yaxis_title=yaxis_title,
+        hovermode="x unified",
+        margin={"l": 50, "r": 20, "t": 50, "b": 40},
+    )
+    return fig
+
+
+TAB_SPEC = {
+    "Docstrings": [
+        "docstring coverage",
+        "docstring missing",
+    ],
+    "Size (Lines/Statements/Expressions/Parameters)": [
+        "lines mean",
+        "lines max",
+        "lines 90th-percentile",
+        "statements mean",
+        "statements max",
+        "statements 90th-percentile",
+        "expressions mean",
+        "expressions max",
+        "expressions 90th-percentile",
+        "parameters mean",
+        "parameters max",
+        "parameters 90th-percentile",
+    ],
+    "Complexity": [
+        "cyclomatic_complexity mean",
+        "cyclomatic_complexity max",
+        "cyclomatic_complexity 90th-percentile",
+    ],
+    "Typing": [
+        "type_coverage mean",
+        "type_coverage min",
+        "type_coverage 50th-percentile",
+    ],
+    "Duplication": [
+        "duplication.score mean",
+        "duplication.score max",
+        "duplication.score 90th-percentile",
+        "duplication.score 50th-percentile",
+        "duplication.duplicated-lines total",
+    ],
+    "TODOs": [
+        "todo_comments total",
+    ],
+    "CLOC (Repository scope)": [
+        "files",
+        "lines blank",
+        "lines comment",
+        "lines code",
+    ],
+}
+
+Y_LABELS = {
+    "Docstrings": "value",
+    "Size (Lines/Statements/Expressions/Parameters)": "count",
+    "Complexity": "complexity",
+    "Typing": "fraction / coverage",
+    "Duplication": "score / lines",
+    "TODOs": "count",
+    "CLOC (Repository scope)": "lines / files",
+}
+
+DF = load_data()
+
+with gr.Blocks(title="Code Metrics – Time Series", fill_height=True) as demo:
+    gr.Markdown(
+        "## Code Metrics Over Time\n"
+        f"Loaded **{CSV_PATH}** with {len(DF)} rows spanning "
+        f"{DF['date'].min().date()} → {DF['date'].max().date()}.\n\n"
+        "Use each tab to pick the series you want to plot."
+    )
+
+    with gr.Tabs():
+        tab_controls = []
+        tab_plots = []
+
+        for tab_name, series in TAB_SPEC.items():
+            available = [s for s in series if s in DF.columns]  # guard against missing cols
+            with gr.Tab(tab_name):
+                with gr.Row():
+                    sel = gr.CheckboxGroup(
+                        choices=available,
+                        value=available,
+                        label="Series",
+                    )
+                plot = gr.Plot(
+                    value=line_figure(DF, available, tab_name, Y_LABELS.get(tab_name, "")),
+                    show_label=False,
+                )
+
+                sel.change(
+                    fn=lambda cols, t=tab_name: line_figure(DF, cols, t, Y_LABELS.get(t, "")),
+                    inputs=sel,
+                    outputs=plot,
+                )
+
+                tab_controls.append(sel)
+                tab_plots.append(plot)
+
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+pandas
+plotly