Initial import after prompting claude

Dockerfile

@@ -0,0 +1,12 @@
+FROM python:3.12-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 5000
+
+CMD ["python", "-m", "gunicorn", "--bind", "0.0.0.0:5000", "--workers", "2", "app:app"]

analyzer/charts.py

@@ -0,0 +1,82 @@
+import io
+import base64
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+import pandas as pd
+
+
+def render_group_charts(groups: list, y_min: float, y_max: float) -> list:
+    padding_fraction = 0.05
+    y_range = y_max - y_min
+    if y_range == 0:
+        y_pad = 1.0
+    else:
+        y_pad = y_range * padding_fraction
+
+    charts = []
+    for i, g in enumerate(groups):
+        fig, ax = plt.subplots(figsize=(9, 4))
+
+        x = g["time"]
+        y = g["speed"]
+
+        ax.plot(x, y, marker="o", linewidth=1.5, markersize=5, color="#1f77b4")
+
+        ax.set_ylim(y_min - y_pad, y_max + y_pad)
+
+        ax.xaxis.set_major_formatter(mdates.DateFormatter("%H:%M:%S"))
+        fig.autofmt_xdate(rotation=30)
+
+        ax.set_title(f"Group {i + 1}  —  {len(g)} shot(s)")
+        ax.set_xlabel("Time of Day")
+        ax.set_ylabel("Speed")
+        ax.grid(True, alpha=0.3)
+        fig.tight_layout()
+
+        buf = io.BytesIO()
+        fig.savefig(buf, format="png", dpi=100)
+        plt.close(fig)
+        buf.seek(0)
+        charts.append(base64.b64encode(buf.read()).decode("utf-8"))
+
+    return charts
+
+
+def render_overview_chart(group_stats: list) -> str:
+    """Dual-axis line chart: avg speed and avg std dev per group."""
+    indices = [s["group_index"] for s in group_stats]
+    speeds  = [s["mean_speed"] for s in group_stats]
+    stds    = [s["std_speed"] if s["std_speed"] is not None else 0.0 for s in group_stats]
+
+    fig, ax1 = plt.subplots(figsize=(7, 3))
+
+    color_speed = "#1f77b4"
+    color_std   = "#d62728"
+
+    ax1.plot(indices, speeds, marker="o", linewidth=1.8, markersize=5,
+             color=color_speed, label="Avg speed")
+    ax1.set_xlabel("Group")
+    ax1.set_ylabel("Avg speed", color=color_speed)
+    ax1.tick_params(axis="y", labelcolor=color_speed)
+    ax1.set_xticks(indices)
+
+    ax2 = ax1.twinx()
+    ax2.plot(indices, stds, marker="s", linewidth=1.8, markersize=5,
+             color=color_std, linestyle="--", label="Avg std dev")
+    ax2.set_ylabel("Avg std dev", color=color_std)
+    ax2.tick_params(axis="y", labelcolor=color_std)
+
+    lines1, labels1 = ax1.get_legend_handles_labels()
+    lines2, labels2 = ax2.get_legend_handles_labels()
+    ax1.legend(lines1 + lines2, labels1 + labels2, fontsize=8, loc="upper right")
+
+    ax1.grid(True, alpha=0.3)
+    fig.tight_layout()
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png", dpi=100)
+    plt.close(fig)
+    buf.seek(0)
+    return base64.b64encode(buf.read()).decode("utf-8")

analyzer/grouper.py

@@ -0,0 +1,44 @@
+from datetime import timedelta
+import pandas as pd
+
+OUTLIER_FACTOR = 5
+
+
+def detect_groups(df: pd.DataFrame) -> list:
+    if len(df) <= 1:
+        return [df]
+
+    times = df["time"]
+    diffs = times.diff().dropna()
+
+    if diffs.empty:
+        return [df]
+
+    median_gap = diffs.median()
+
+    if median_gap == timedelta(0):
+        return [df]
+
+    threshold = OUTLIER_FACTOR * median_gap
+
+    split_positions = []
+    for idx, gap in diffs.items():
+        if gap > threshold:
+            pos = df.index.get_loc(idx)
+            split_positions.append(pos)
+
+    if not split_positions:
+        return [df]
+
+    groups = []
+    prev = 0
+    for pos in split_positions:
+        group = df.iloc[prev:pos]
+        if len(group) > 0:
+            groups.append(group.reset_index(drop=True))
+        prev = pos
+    last = df.iloc[prev:]
+    if len(last) > 0:
+        groups.append(last.reset_index(drop=True))
+
+    return groups

analyzer/parser.py

@@ -0,0 +1,107 @@
+import csv
+import io
+import pandas as pd
+from datetime import datetime, timedelta
+
+CANONICAL_COLS = ["idx", "speed", "std_dev", "energy", "power_factor", "time"]
+TIME_FORMATS = ["%H:%M:%S.%f", "%H:%M:%S", "%H:%M:%S,%f"]
+
+
+def parse_csv(stream) -> pd.DataFrame:
+    raw = stream.read()
+    if isinstance(raw, bytes):
+        raw = raw.decode("utf-8-sig")
+    # Strip BOM characters that may appear anywhere in the file
+    raw = raw.replace("\ufeff", "")
+
+    data_rows = []
+    for line in raw.splitlines():
+        fields = _split_line(line)
+        if len(fields) >= 6 and _is_index(fields[0]) and _is_time(fields[5]):
+            data_rows.append(fields[:6])
+
+    if len(data_rows) < 2:
+        raise ValueError(
+            "Could not find valid data rows in the CSV. "
+            "Expected rows with: integer index, 4 numeric values, and a time (HH:MM:SS)."
+        )
+
+    df = pd.DataFrame(data_rows, columns=CANONICAL_COLS)
+
+    for col in ("speed", "std_dev", "energy", "power_factor"):
+        df[col] = _parse_numeric(df[col])
+
+    df["time"] = _parse_time_column(df["time"])
+    df = df.sort_values("time").reset_index(drop=True)
+    return df[["speed", "std_dev", "energy", "power_factor", "time"]]
+
+
+def _split_line(line: str) -> list:
+    """Parse one CSV line, respecting quoted fields."""
+    for row in csv.reader([line], quotechar='"', doublequote=True, skipinitialspace=True):
+        return [f.strip() for f in row]
+    return []
+
+
+def _is_index(val: str) -> bool:
+    """True if the value is a non-negative integer (auto-increment row index)."""
+    try:
+        return int(val.strip()) >= 0
+    except (ValueError, AttributeError):
+        return False
+
+
+def _is_time(val: str) -> bool:
+    """True if the value parses as HH:MM:SS or HH:MM:SS.fff."""
+    cleaned = val.strip()
+    for fmt in TIME_FORMATS:
+        try:
+            datetime.strptime(cleaned, fmt)
+            return True
+        except ValueError:
+            continue
+    return False
+
+
+def _parse_numeric(col: pd.Series) -> pd.Series:
+    """Parse a numeric column, accepting both '.' and ',' as decimal separator."""
+    result = pd.to_numeric(col, errors="coerce")
+    if result.isna().any():
+        result = pd.to_numeric(
+            col.astype(str).str.replace(",", ".", regex=False),
+            errors="coerce",
+        )
+    if result.isna().any():
+        bad = col[result.isna()].tolist()
+        raise ValueError(f"Non-numeric values in column: {bad}")
+    return result
+
+
+def _parse_time_column(col: pd.Series) -> pd.Series:
+    today = datetime.today().date()
+    cleaned = col.astype(str).str.strip()
+
+    parsed = None
+    for fmt in TIME_FORMATS:
+        candidate = pd.to_datetime(cleaned, format=fmt, errors="coerce")
+        if candidate.notna().all():
+            parsed = candidate
+            break
+
+    if parsed is None:
+        candidate = pd.to_datetime(cleaned, errors="coerce")
+        if candidate.notna().all():
+            parsed = candidate
+
+    if parsed is None:
+        raise ValueError(
+            "Could not parse time column. Expected format: HH:MM:SS or HH:MM:SS.fff"
+        )
+
+    parsed = parsed.apply(lambda t: datetime.combine(today, t.time()))
+
+    times = parsed.tolist()
+    for i in range(1, len(times)):
+        if times[i] < times[i - 1]:
+            times[i] += timedelta(days=1)
+    return pd.Series(times, index=col.index)

analyzer/pdf_report.py

@@ -0,0 +1,95 @@
+import base64
+import io
+from datetime import datetime
+
+from fpdf import FPDF
+
+_COL_LABEL = 80
+_COL_VALUE = 50
+_ROW_H = 7
+
+
+def generate_pdf(overall: dict, group_stats: list, charts: list, overview_chart: str) -> bytes:
+    pdf = FPDF()
+    pdf.set_auto_page_break(auto=True, margin=15)
+
+    pdf.add_page()
+    _title_block(pdf)
+    _overall_section(pdf, overall, overview_chart)
+
+    for stat, chart_b64 in zip(group_stats, charts):
+        _group_section(pdf, stat, chart_b64)
+
+    return bytes(pdf.output())
+
+
+# ---------------------------------------------------------------------------
+
+def _title_block(pdf: FPDF):
+    pdf.set_font("Helvetica", "B", 18)
+    pdf.cell(0, 12, "Ballistic Analysis Report", new_x="LMARGIN", new_y="NEXT", align="C")
+    pdf.set_font("Helvetica", "", 9)
+    pdf.cell(
+        0, 5,
+        f"Generated: {datetime.now().strftime('%Y-%m-%d  %H:%M')}",
+        new_x="LMARGIN", new_y="NEXT", align="C",
+    )
+    pdf.ln(8)
+
+
+def _overall_section(pdf: FPDF, overall: dict, overview_chart: str):
+    _section_heading(pdf, "Overall Statistics")
+    rows = [
+        ("Total shots",    str(overall["count"])),
+        ("Min speed",      f"{overall['min_speed']:.4f}"),
+        ("Max speed",      f"{overall['max_speed']:.4f}"),
+        ("Mean speed",     f"{overall['mean_speed']:.4f}"),
+        ("Std dev (speed)", f"{overall['std_speed']:.4f}" if overall["std_speed"] is not None else "n/a"),
+    ]
+    _table(pdf, rows)
+    img_bytes = base64.b64decode(overview_chart)
+    pdf.image(io.BytesIO(img_bytes), x=pdf.l_margin, w=min(140, pdf.epw))
+    pdf.ln(4)
+
+
+def _group_section(pdf: FPDF, stat: dict, chart_b64: str):
+    pdf.ln(4)
+    heading = (
+        f"Group {stat['group_index']}  -  "
+        f"{stat['time_start']} to {stat['time_end']}  "
+        f"({stat['count']} shot(s))"
+    )
+    _section_heading(pdf, heading)
+
+    rows = [
+        ("Min speed",      f"{stat['min_speed']:.4f}"),
+        ("Max speed",      f"{stat['max_speed']:.4f}"),
+        ("Mean speed",     f"{stat['mean_speed']:.4f}"),
+        ("Std dev (speed)", f"{stat['std_speed']:.4f}" if stat["std_speed"] is not None else "n/a"),
+    ]
+    _table(pdf, rows)
+
+    img_bytes = base64.b64decode(chart_b64)
+    # Check remaining page space; add new page if chart won't fit
+    if pdf.get_y() + 75 > pdf.page_break_trigger:
+        pdf.add_page()
+    pdf.image(io.BytesIO(img_bytes), x=pdf.l_margin, w=pdf.epw)
+    pdf.ln(4)
+
+
+def _section_heading(pdf: FPDF, text: str):
+    pdf.set_font("Helvetica", "B", 12)
+    pdf.set_fill_color(230, 236, 255)
+    pdf.cell(0, 8, text, new_x="LMARGIN", new_y="NEXT", fill=True)
+    pdf.ln(2)
+
+
+def _table(pdf: FPDF, rows: list):
+    for i, (label, value) in enumerate(rows):
+        fill = i % 2 == 0
+        pdf.set_fill_color(248, 249, 252) if fill else pdf.set_fill_color(255, 255, 255)
+        pdf.set_font("Helvetica", "", 10)
+        pdf.cell(_COL_LABEL, _ROW_H, label, border=0, fill=fill)
+        pdf.set_font("Helvetica", "B", 10)
+        pdf.cell(_COL_VALUE, _ROW_H, value, border=0, fill=fill, new_x="LMARGIN", new_y="NEXT")
+    pdf.ln(3)

analyzer/stats.py

@@ -0,0 +1,30 @@
+import pandas as pd
+
+
+def compute_overall_stats(df: pd.DataFrame) -> dict:
+    s = df["speed"]
+    return {
+        "min_speed": s.min(),
+        "max_speed": s.max(),
+        "mean_speed": s.mean(),
+        "std_speed": s.std(ddof=1),
+        "count": len(df),
+    }
+
+
+def compute_group_stats(groups: list) -> list:
+    result = []
+    for i, g in enumerate(groups):
+        s = g["speed"]
+        std = s.std(ddof=1) if len(g) > 1 else None
+        result.append({
+            "group_index": i + 1,
+            "count": len(g),
+            "min_speed": s.min(),
+            "max_speed": s.max(),
+            "mean_speed": s.mean(),
+            "std_speed": std,
+            "time_start": g["time"].min().strftime("%H:%M:%S"),
+            "time_end": g["time"].max().strftime("%H:%M:%S"),
+        })
+    return result

app.py

@@ -0,0 +1,55 @@
+import base64
+
+from flask import Flask, request, render_template
+
+from analyzer.parser import parse_csv
+from analyzer.grouper import detect_groups
+from analyzer.stats import compute_overall_stats, compute_group_stats
+from analyzer.charts import render_group_charts, render_overview_chart
+from analyzer.pdf_report import generate_pdf
+
+app = Flask(__name__)
+app.config["MAX_CONTENT_LENGTH"] = 16 * 1024 * 1024
+
+
+@app.route("/")
+def index():
+    return render_template("upload.html")
+
+
+@app.route("/analyze", methods=["POST"])
+def analyze():
+    if "csv_file" not in request.files or request.files["csv_file"].filename == "":
+        return render_template("upload.html", error="No file selected.")
+
+    file = request.files["csv_file"]
+
+    try:
+        df = parse_csv(file.stream)
+        groups = detect_groups(df)
+        overall = compute_overall_stats(df)
+        group_stats = compute_group_stats(groups)
+        charts = render_group_charts(
+            groups,
+            y_min=overall["min_speed"],
+            y_max=overall["max_speed"],
+        )
+        overview_chart = render_overview_chart(group_stats)
+    except ValueError as e:
+        return render_template("upload.html", error=str(e))
+
+    pdf_bytes = generate_pdf(overall, group_stats, charts, overview_chart)
+    pdf_b64 = base64.b64encode(pdf_bytes).decode("utf-8")
+
+    groups_display = list(zip(group_stats, charts))
+    return render_template(
+        "results.html",
+        overall=overall,
+        groups_display=groups_display,
+        overview_chart=overview_chart,
+        pdf_b64=pdf_b64,
+    )
+
+
+if __name__ == "__main__":
+    app.run(debug=True)

docker-compose.yaml

@@ -0,0 +1,6 @@
+services:
+  web:
+    build: .
+    ports:
+      - "5000:5000"
+    restart: unless-stopped

memory/feedback_no_host_install.md

@@ -0,0 +1,11 @@
+---
+name: no_host_pip_install
+description: Do not run pip install on the host machine; user is on Debian with externally-managed Python. All dependency testing must happen inside Docker containers.
+type: feedback
+---
+
+Do not run `pip install` on the host machine.
+
+**Why:** The host is Debian with an externally-managed Python environment; pip installs are blocked system-wide.
+
+**How to apply:** Any testing that requires libraries not already on the host must be done inside a Docker container (`docker compose run` or `docker build`). Skip host-level install tests entirely and note to the user that they should verify inside the container.

requirements.txt

@@ -0,0 +1,6 @@
+Flask>=3.0
+pandas>=1.5
+matplotlib>=3.6
+numpy>=1.24
+gunicorn>=21.0
+fpdf2>=2.7

sample.csv

@@ -0,0 +1,63 @@
+Balle de fusil 169,0 gr
+#,Vitesse (MPS),Δ Moyenne (MPS),ÉK (J),Facteur de puissance (N s),Temps,Âme nettoyée,Âme froide,Notes de tir
+1,"807,4","6,1","3569,4","8,8",09:30:37,,,
+2,"801,1","-0,2","3514,0","8,8",09:31:06,,,
+3,"799,3","-2,0","3498,4","8,8",09:31:28,,,
+4,"800,1","-1,3","3505,0","8,8",09:32:17,,,
+5,"800,3","-1,0","3507,1","8,8",09:32:36,,,
+6,"789,5","-11,8","3412,9","8,6",09:49:40,,,
+7,"800,7","-0,7","3510,2","8,8",09:49:54,,,
+8,"795,1","-6,2","3461,6","8,7",09:50:10,,,
+9,"799,8","-1,5","3502,6","8,8",09:50:27,,,
+10,"798,9","-2,4","3494,8","8,7",09:50:57,,,
+11,"795,4","-5,9","3464,0","8,7",09:53:04,,,
+12,"798,6","-2,7","3492,0","8,7",09:53:15,,,
+13,"798,1","-3,2","3487,8","8,7",09:53:28,,,
+14,"798,3","-3,0","3489,3","8,7",09:53:44,,,
+15,"797,6","-3,7","3483,7","8,7",09:54:02,,,
+16,"800,3","-1,0","3506,9","8,8",10:08:55,,,
+17,"800,6","-0,7","3509,9","8,8",10:09:24,,,
+18,"804,6","3,2","3544,5","8,8",10:09:38,,,
+19,"799,8","-1,6","3502,4","8,8",10:09:55,,,
+20,"802,1","0,8","3523,0","8,8",10:10:15,,,
+21,"802,0","0,6","3521,7","8,8",10:26:11,,,
+22,"804,0","2,7","3539,7","8,8",10:26:32,,,
+23,"805,1","3,8","3549,4","8,8",10:27:23,,,
+24,"802,8","1,5","3529,3","8,8",10:27:50,,,
+25,"805,8","4,4","3555,1","8,8",10:28:15,,,
+26,"808,1","6,8","3575,9","8,8",10:43:45,,,
+27,"798,5","-2,9","3490,8","8,7",10:44:04,,,
+28,"798,4","-3,0","3489,9","8,7",10:44:20,,,
+29,"801,6","0,3","3518,7","8,8",10:44:48,,,
+30,"800,1","-1,2","3505,5","8,8",10:45:10,,,
+31,"802,4","1,1","3525,3","8,8",10:57:50,,,
+32,"796,9","-4,5","3477,1","8,7",10:58:08,,,
+33,"800,9","-0,5","3511,8","8,8",10:58:27,,,
+34,"800,7","-0,7","3510,2","8,8",10:58:43,,,
+35,"797,3","-4,1","3480,3","8,7",10:59:02,,,
+36,"796,2","-5,1","3471,0","8,7",11:14:15,,,
+37,"809,7","8,4","3589,9","8,9",11:14:37,,,
+38,"802,9","1,6","3529,6","8,8",11:15:02,,,
+39,"810,3","8,9","3594,7","8,9",11:15:27,,,
+40,"804,2","2,9","3541,5","8,8",11:15:57,,,
+41,"807,1","5,8","3567,2","8,8",11:28:00,,,
+42,"805,4","4,1","3552,0","8,8",11:28:20,,,
+43,"802,0","0,7","3522,2","8,8",11:28:50,,,
+44,"804,6","3,3","3544,6","8,8",11:29:11,,,
+45,"807,6","6,3","3571,4","8,8",11:29:32,,,
+46,"807,3","6,0","3568,5","8,8",11:30:05,,,
+47,"795,8","-5,5","3467,8","8,7",11:30:19,,,
+48,"798,2","-3,1","3488,9","8,7",11:30:36,,,
+49,"801,9","0,5","3520,6","8,8",11:30:49,,,
+50,"801,2","-0,2","3514,5","8,8",11:31:10,,,
+-,,,,,,
+VITESSE MOYENNE,"801,3",,,,,
+FACTEUR DE PUISSANCE MOYEN,"8,8",,,,,
+ÉCART-TYPE,"4,1",,,,,
+SPREAD,"20,8",,,,,
+Poids du projectile (GRAINS),"169,0",,,,,
+ÉNERGIE CINÉTIQUE MOY.,"3516,1",,,,,
+Note sur la session,,,,,,
+-,,,,,,
+Date,"MARS 16, 2026 09:13",,,,,
+Tous les tirs inclus dans les calculs,,,,,,

templates/base.html

@@ -0,0 +1,80 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>Ballistic Analyzer</title>
+  <style>
+    *, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
+    body {
+      font-family: system-ui, sans-serif;
+      background: #f4f5f7;
+      color: #222;
+      min-height: 100vh;
+      padding: 2rem 1rem;
+    }
+    .container {
+      max-width: 960px;
+      margin: 0 auto;
+      background: #fff;
+      border-radius: 8px;
+      padding: 2rem 2.5rem;
+      box-shadow: 0 2px 8px rgba(0,0,0,0.08);
+    }
+    h1 { font-size: 1.8rem; margin-bottom: 1.5rem; color: #1a1a2e; }
+    h2 { font-size: 1.3rem; margin: 2rem 0 0.75rem; color: #1a1a2e; border-bottom: 2px solid #e0e0e0; padding-bottom: 0.3rem; }
+    h3 { font-size: 1.1rem; margin: 1.5rem 0 0.5rem; color: #333; }
+    a { color: #1f77b4; text-decoration: none; }
+    a:hover { text-decoration: underline; }
+    .error {
+      background: #fff0f0;
+      border-left: 4px solid #e74c3c;
+      padding: 0.75rem 1rem;
+      border-radius: 4px;
+      margin-bottom: 1.25rem;
+      color: #c0392b;
+    }
+    table {
+      border-collapse: collapse;
+      width: 100%;
+      margin-bottom: 1rem;
+    }
+    th, td {
+      text-align: left;
+      padding: 0.55rem 0.9rem;
+      border-bottom: 1px solid #e8e8e8;
+      font-size: 0.92rem;
+    }
+    th {
+      background: #f0f4ff;
+      font-weight: 600;
+      color: #444;
+    }
+    tr:last-child td { border-bottom: none; }
+    tr:hover td { background: #fafbff; }
+    .group-section {
+      border: 1px solid #e0e0e0;
+      border-radius: 6px;
+      padding: 1.25rem 1.5rem;
+      margin-bottom: 1.5rem;
+    }
+    .group-meta {
+      font-size: 0.88rem;
+      color: #666;
+      margin-bottom: 0.75rem;
+    }
+    .chart-img {
+      width: 100%;
+      max-width: 860px;
+      display: block;
+      margin-top: 1rem;
+      border-radius: 4px;
+    }
+  </style>
+</head>
+<body>
+  <div class="container">
+    {% block content %}{% endblock %}
+  </div>
+</body>
+</html>

templates/results.html

@@ -0,0 +1,78 @@
+{% extends "base.html" %}
+{% block content %}
+<div style="display:flex;align-items:baseline;justify-content:space-between;flex-wrap:wrap;gap:1rem;margin-bottom:1.5rem;">
+  <h1 style="margin:0;">Analysis Results</h1>
+  <div style="display:flex;gap:0.75rem;align-items:center;">
+    <a href="/">&larr; Upload another file</a>
+    <a href="data:application/pdf;base64,{{ pdf_b64 }}"
+       download="ballistic_report.pdf"
+       style="background:#1f77b4;color:#fff;border-radius:4px;padding:0.5rem 1.1rem;font-size:0.9rem;text-decoration:none;">
+      Download PDF report
+    </a>
+  </div>
+</div>
+
+<h2>Overall Statistics</h2>
+<table>
+  <thead>
+    <tr>
+      <th>Metric</th>
+      <th>Value</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr><td>Total shots</td><td>{{ overall.count }}</td></tr>
+    <tr><td>Min speed</td><td>{{ "%.4f"|format(overall.min_speed) }}</td></tr>
+    <tr><td>Max speed</td><td>{{ "%.4f"|format(overall.max_speed) }}</td></tr>
+    <tr><td>Mean speed</td><td>{{ "%.4f"|format(overall.mean_speed) }}</td></tr>
+    <tr>
+      <td>Std dev (speed)</td>
+      <td>
+        {% if overall.std_speed is not none %}
+          {{ "%.4f"|format(overall.std_speed) }}
+        {% else %}
+          &ndash;
+        {% endif %}
+      </td>
+    </tr>
+  </tbody>
+</table>
+
+<img class="chart-img" src="data:image/png;base64,{{ overview_chart }}"
+     alt="Avg speed and std dev per group" style="max-width:600px;margin:1rem 0 1.5rem;">
+
+<h2>Groups &mdash; {{ groups_display|length }} group(s) detected</h2>
+
+{% for stat, chart_b64 in groups_display %}
+<div class="group-section">
+  <h3>Group {{ stat.group_index }}</h3>
+  <div class="group-meta">
+    {{ stat.time_start }} &ndash; {{ stat.time_end }} &nbsp;|&nbsp; {{ stat.count }} shot(s)
+  </div>
+  <table>
+    <thead>
+      <tr>
+        <th>Metric</th>
+        <th>Value</th>
+      </tr>
+    </thead>
+    <tbody>
+      <tr><td>Min speed</td><td>{{ "%.4f"|format(stat.min_speed) }}</td></tr>
+      <tr><td>Max speed</td><td>{{ "%.4f"|format(stat.max_speed) }}</td></tr>
+      <tr><td>Mean speed</td><td>{{ "%.4f"|format(stat.mean_speed) }}</td></tr>
+      <tr>
+        <td>Std dev (speed)</td>
+        <td>
+          {% if stat.std_speed is not none %}
+            {{ "%.4f"|format(stat.std_speed) }}
+          {% else %}
+            &ndash;
+          {% endif %}
+        </td>
+      </tr>
+    </tbody>
+  </table>
+  <img class="chart-img" src="data:image/png;base64,{{ chart_b64 }}" alt="Speed chart for group {{ stat.group_index }}">
+</div>
+{% endfor %}
+{% endblock %}

templates/upload.html

@@ -0,0 +1,30 @@
+{% extends "base.html" %}
+{% block content %}
+<h1>Ballistic Analyzer</h1>
+
+{% if error %}
+<div class="error">{{ error }}</div>
+{% endif %}
+
+<p style="margin-bottom:1.5rem; color:#555;">
+  Upload a CSV file to analyse shot groups. The file must contain the following columns:
+  <strong>index</strong>, <strong>speed</strong>, <strong>standard deviation</strong>,
+  <strong>energy</strong>, <strong>power factor</strong>, <strong>time of the day</strong>.
+</p>
+
+<form method="POST" action="/analyze" enctype="multipart/form-data" style="display:flex;gap:1rem;align-items:center;flex-wrap:wrap;">
+  <input
+    type="file"
+    name="csv_file"
+    accept=".csv,text/csv"
+    required
+    style="border:1px solid #ccc;border-radius:4px;padding:0.5rem 0.75rem;background:#fafafa;font-size:0.95rem;"
+  >
+  <button
+    type="submit"
+    style="background:#1f77b4;color:#fff;border:none;border-radius:4px;padding:0.55rem 1.4rem;font-size:0.95rem;cursor:pointer;"
+  >
+    Analyze
+  </button>
+</form>
+{% endblock %}