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.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
+background_top.png filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,26 @@
+FROM python:3.10-slim
+
+ENV DEBIAN_FRONTEND=noninteractive
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+ENV GRADIO_SERVER_NAME=0.0.0.0
+ENV GRADIO_SERVER_PORT=7860
+
+WORKDIR /app
+COPY . /app
+
+# Python deps (from requirements.txt)
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Notebook execution deps
+RUN pip install --no-cache-dir notebook ipykernel papermill
+
+# Pre-install packages the notebooks use via !pip install
+RUN pip install --no-cache-dir textblob faker vaderSentiment transformers
+
+RUN python -m ipykernel install --user --name python3 --display-name "Python 3"
+
+EXPOSE 7860
+
+CMD ["python", "app.py"]

README.md

@@ -1,10 +1,11 @@
 ---
-title: AS1 19 Group Project
-emoji: 💻
-colorFrom: gray
-colorTo: pink
+title: SE21 App Template
+emoji: 📊
+colorFrom: blue
+colorTo: purple
 sdk: docker
 pinned: false
+short_description: AI-enhanced analytics dashboard template for SE21 students
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,758 @@
+import os
+import re
+import json
+import time
+import traceback
+from pathlib import Path
+from typing import Dict, Any, List, Tuple
+
+import pandas as pd
+import gradio as gr
+import papermill as pm
+import plotly.graph_objects as go
+
+# Optional LLM (HuggingFace Inference API)
+try:
+    from huggingface_hub import InferenceClient
+except Exception:
+    InferenceClient = None
+
+# =========================================================
+# CONFIG
+# =========================================================
+
+BASE_DIR = Path(__file__).resolve().parent
+
+NB1 = os.environ.get("NB1", "datacreation.ipynb").strip()
+NB2 = os.environ.get("NB2", "pythonanalysis.ipynb").strip()
+
+RUNS_DIR = BASE_DIR / "runs"
+ART_DIR = BASE_DIR / "artifacts"
+PY_FIG_DIR = ART_DIR / "py" / "figures"
+PY_TAB_DIR = ART_DIR / "py" / "tables"
+
+PAPERMILL_TIMEOUT = int(os.environ.get("PAPERMILL_TIMEOUT", "1800"))
+MAX_PREVIEW_ROWS = int(os.environ.get("MAX_FILE_PREVIEW_ROWS", "50"))
+MAX_LOG_CHARS = int(os.environ.get("MAX_LOG_CHARS", "8000"))
+
+HF_API_KEY = os.environ.get("HF_API_KEY", "").strip()
+MODEL_NAME = os.environ.get("MODEL_NAME", "deepseek-ai/DeepSeek-R1").strip()
+HF_PROVIDER = os.environ.get("HF_PROVIDER", "novita").strip()
+N8N_WEBHOOK_URL = os.environ.get("N8N_WEBHOOK_URL", "").strip()
+
+LLM_ENABLED = bool(HF_API_KEY) and InferenceClient is not None
+llm_client = (
+    InferenceClient(provider=HF_PROVIDER, api_key=HF_API_KEY)
+    if LLM_ENABLED
+    else None
+)
+
+# =========================================================
+# HELPERS
+# =========================================================
+
+def ensure_dirs():
+    for p in [RUNS_DIR, ART_DIR, PY_FIG_DIR, PY_TAB_DIR]:
+        p.mkdir(parents=True, exist_ok=True)
+
+def stamp():
+    return time.strftime("%Y%m%d-%H%M%S")
+
+def tail(text: str, n: int = MAX_LOG_CHARS) -> str:
+    return (text or "")[-n:]
+
+def _ls(dir_path: Path, exts: Tuple[str, ...]) -> List[str]:
+    if not dir_path.is_dir():
+        return []
+    return sorted(p.name for p in dir_path.iterdir() if p.is_file() and p.suffix.lower() in exts)
+
+def _read_csv(path: Path) -> pd.DataFrame:
+    return pd.read_csv(path, nrows=MAX_PREVIEW_ROWS)
+
+def _read_json(path: Path):
+    with path.open(encoding="utf-8") as f:
+        return json.load(f)
+
+def artifacts_index() -> Dict[str, Any]:
+    return {
+        "python": {
+            "figures": _ls(PY_FIG_DIR, (".png", ".jpg", ".jpeg")),
+            "tables": _ls(PY_TAB_DIR, (".csv", ".json")),
+        },
+    }
+
+# =========================================================
+# PIPELINE RUNNERS
+# =========================================================
+
+def run_notebook(nb_name: str) -> str:
+    ensure_dirs()
+    nb_in = BASE_DIR / nb_name
+    if not nb_in.exists():
+        return f"ERROR: {nb_name} not found."
+    nb_out = RUNS_DIR / f"run_{stamp()}_{nb_name}"
+    pm.execute_notebook(
+        input_path=str(nb_in),
+        output_path=str(nb_out),
+        cwd=str(BASE_DIR),
+        log_output=True,
+        progress_bar=False,
+        request_save_on_cell_execute=True,
+        execution_timeout=PAPERMILL_TIMEOUT,
+    )
+    return f"Executed {nb_name}"
+
+
+def run_datacreation() -> str:
+    try:
+        log = run_notebook(NB1)
+        csvs = [f.name for f in BASE_DIR.glob("*.csv")]
+        return f"OK  {log}\n\nCSVs now in /app:\n" + "\n".join(f"  - {c}" for c in sorted(csvs))
+    except Exception as e:
+        return f"FAILED  {e}\n\n{traceback.format_exc()[-2000:]}"
+
+
+def run_pythonanalysis() -> str:
+    try:
+        log = run_notebook(NB2)
+        idx = artifacts_index()
+        figs = idx["python"]["figures"]
+        tabs = idx["python"]["tables"]
+        return (
+            f"OK  {log}\n\n"
+            f"Figures: {', '.join(figs) or '(none)'}\n"
+            f"Tables:  {', '.join(tabs) or '(none)'}"
+        )
+    except Exception as e:
+        return f"FAILED  {e}\n\n{traceback.format_exc()[-2000:]}"
+
+
+def run_full_pipeline() -> str:
+    logs = []
+    logs.append("=" * 50)
+    logs.append("STEP 1/2: Data Creation (web scraping + synthetic data)")
+    logs.append("=" * 50)
+    logs.append(run_datacreation())
+    logs.append("")
+    logs.append("=" * 50)
+    logs.append("STEP 2/2: Python Analysis (sentiment, ARIMA, dashboard)")
+    logs.append("=" * 50)
+    logs.append(run_pythonanalysis())
+    return "\n".join(logs)
+
+
+# =========================================================
+# GALLERY LOADERS
+# =========================================================
+
+def _load_all_figures() -> List[Tuple[str, str]]:
+    """Return list of (filepath, caption) for Gallery."""
+    items = []
+    for p in sorted(PY_FIG_DIR.glob("*.png")):
+        items.append((str(p), p.stem.replace('_', ' ').title()))
+    return items
+
+
+def _load_table_safe(path: Path) -> pd.DataFrame:
+    try:
+        if path.suffix == ".json":
+            obj = _read_json(path)
+            if isinstance(obj, dict):
+                return pd.DataFrame([obj])
+            return pd.DataFrame(obj)
+        return _read_csv(path)
+    except Exception as e:
+        return pd.DataFrame([{"error": str(e)}])
+
+
+def refresh_gallery():
+    """Called when user clicks Refresh on Gallery tab."""
+    figures = _load_all_figures()
+    idx = artifacts_index()
+
+    table_choices = list(idx["python"]["tables"])
+
+    default_df = pd.DataFrame()
+    if table_choices:
+        default_df = _load_table_safe(PY_TAB_DIR / table_choices[0])
+
+    return (
+        figures if figures else [],
+        gr.update(choices=table_choices, value=table_choices[0] if table_choices else None),
+        default_df,
+    )
+
+
+def on_table_select(choice: str):
+    if not choice:
+        return pd.DataFrame([{"hint": "Select a table above."}])
+    path = PY_TAB_DIR / choice
+    if not path.exists():
+        return pd.DataFrame([{"error": f"File not found: {choice}"}])
+    return _load_table_safe(path)
+
+
+# =========================================================
+# KPI LOADER
+# =========================================================
+
+def load_kpis() -> Dict[str, Any]:
+    for candidate in [PY_TAB_DIR / "kpis.json", PY_FIG_DIR / "kpis.json"]:
+        if candidate.exists():
+            try:
+                return _read_json(candidate)
+            except Exception:
+                pass
+    return {}
+
+
+# =========================================================
+# AI DASHBOARD -- LLM picks what to display
+# =========================================================
+
+DASHBOARD_SYSTEM = """You are an AI dashboard assistant for a book-sales analytics app.
+The user asks questions or requests about their data. You have access to pre-computed
+artifacts from a Python analysis pipeline.
+
+AVAILABLE ARTIFACTS (only reference ones that exist):
+{artifacts_json}
+
+KPI SUMMARY: {kpis_json}
+
+YOUR JOB:
+1. Answer the user's question conversationally using the KPIs and your knowledge of the artifacts.
+2. At the END of your response, output a JSON block (fenced with ```json ... ```) that tells
+   the dashboard which artifact to display. The JSON must have this shape:
+   {{"show": "figure"|"table"|"none", "scope": "python", "filename": "..."}}
+
+   - Use "show": "figure" to display a chart image.
+   - Use "show": "table" to display a CSV/JSON table.
+   - Use "show": "none" if no artifact is relevant.
+
+RULES:
+- If the user asks about sales trends or forecasting by title, show sales_trends or arima figures.
+- If the user asks about sentiment, show sentiment figure or sentiment_counts table.
+- If the user asks about forecast accuracy or ARIMA, show arima figures.
+- If the user asks about top sellers, show top_titles_by_units_sold.csv.
+- If the user asks a general data question, pick the most relevant artifact.
+- Keep your answer concise (2-4 sentences), then the JSON block.
+"""
+
+JSON_BLOCK_RE = re.compile(r"```json\s*(\{.*?\})\s*```", re.DOTALL)
+FALLBACK_JSON_RE = re.compile(r"\{[^{}]*\"show\"[^{}]*\}", re.DOTALL)
+
+
+def _parse_display_directive(text: str) -> Dict[str, str]:
+    m = JSON_BLOCK_RE.search(text)
+    if m:
+        try:
+            return json.loads(m.group(1))
+        except json.JSONDecodeError:
+            pass
+    m = FALLBACK_JSON_RE.search(text)
+    if m:
+        try:
+            return json.loads(m.group(0))
+        except json.JSONDecodeError:
+            pass
+    return {"show": "none"}
+
+
+def _clean_response(text: str) -> str:
+    """Strip the JSON directive block from the displayed response."""
+    return JSON_BLOCK_RE.sub("", text).strip()
+
+
+def _n8n_call(msg: str) -> Tuple[str, Dict]:
+    """Call the student's n8n webhook and return (reply, directive)."""
+    import requests as req
+    try:
+        resp = req.post(N8N_WEBHOOK_URL, json={"question": msg}, timeout=20)
+        data = resp.json()
+        answer = data.get("answer", "No response from n8n workflow.")
+        chart = data.get("chart", "none")
+        if chart and chart != "none":
+            return answer, {"show": "figure", "chart": chart}
+        return answer, {"show": "none"}
+    except Exception as e:
+        return f"n8n error: {e}. Falling back to keyword matching.", None
+
+
+def ai_chat(user_msg: str, history: list):
+    """Chat function for the AI Dashboard tab."""
+    if not user_msg or not user_msg.strip():
+        return history, "", None, None
+
+    idx = artifacts_index()
+    kpis = load_kpis()
+
+    # Priority: n8n webhook > HF LLM > keyword fallback
+    if N8N_WEBHOOK_URL:
+        reply, directive = _n8n_call(user_msg)
+        if directive is None:
+            reply_fb, directive = _keyword_fallback(user_msg, idx, kpis)
+            reply += "\n\n" + reply_fb
+    elif not LLM_ENABLED:
+        reply, directive = _keyword_fallback(user_msg, idx, kpis)
+    else:
+        system = DASHBOARD_SYSTEM.format(
+            artifacts_json=json.dumps(idx, indent=2),
+            kpis_json=json.dumps(kpis, indent=2) if kpis else "(no KPIs yet, run the pipeline first)",
+        )
+        msgs = [{"role": "system", "content": system}]
+        for entry in (history or [])[-6:]:
+            msgs.append(entry)
+        msgs.append({"role": "user", "content": user_msg})
+
+        try:
+            r = llm_client.chat_completion(
+                model=MODEL_NAME,
+                messages=msgs,
+                temperature=0.3,
+                max_tokens=600,
+                stream=False,
+            )
+            raw = (
+                r["choices"][0]["message"]["content"]
+                if isinstance(r, dict)
+                else r.choices[0].message.content
+            )
+            directive = _parse_display_directive(raw)
+            reply = _clean_response(raw)
+        except Exception as e:
+            reply = f"LLM error: {e}. Falling back to keyword matching."
+            reply_fb, directive = _keyword_fallback(user_msg, idx, kpis)
+            reply += "\n\n" + reply_fb
+
+    # Resolve artifacts — build interactive Plotly charts when possible
+    chart_out = None
+    tab_out = None
+    show = directive.get("show", "none")
+    fname = directive.get("filename", "")
+    chart_name = directive.get("chart", "")
+
+    # Interactive chart builders keyed by name
+    chart_builders = {
+        "sales": build_sales_chart,
+        "sentiment": build_sentiment_chart,
+        "top_sellers": build_top_sellers_chart,
+    }
+
+    if chart_name and chart_name in chart_builders:
+        chart_out = chart_builders[chart_name]()
+    elif show == "figure" and fname:
+        # Fallback: try to match filename to a chart builder
+        if "sales_trend" in fname:
+            chart_out = build_sales_chart()
+        elif "sentiment" in fname:
+            chart_out = build_sentiment_chart()
+        elif "arima" in fname or "forecast" in fname:
+            chart_out = build_sales_chart()  # closest interactive equivalent
+        else:
+            chart_out = _empty_chart(f"No interactive chart for {fname}")
+
+    if show == "table" and fname:
+        fp = PY_TAB_DIR / fname
+        if fp.exists():
+            tab_out = _load_table_safe(fp)
+        else:
+            reply += f"\n\n*(Could not find table: {fname})*"
+
+    new_history = (history or []) + [
+        {"role": "user", "content": user_msg},
+        {"role": "assistant", "content": reply},
+    ]
+
+    return new_history, "", chart_out, tab_out
+
+
+def _keyword_fallback(msg: str, idx: Dict, kpis: Dict) -> Tuple[str, Dict]:
+    """Simple keyword matcher when LLM is unavailable."""
+    msg_lower = msg.lower()
+
+    if not idx["python"]["figures"] and not idx["python"]["tables"]:
+        return (
+            "No artifacts found yet. Please run the pipeline first (Tab 1), "
+            "then come back here to explore the results.",
+            {"show": "none"},
+        )
+
+    kpi_text = ""
+    if kpis:
+        total = kpis.get("total_units_sold", 0)
+        kpi_text = (
+            f"Quick summary: **{kpis.get('n_titles', '?')}** book titles across "
+            f"**{kpis.get('n_months', '?')}** months, with **{total:,.0f}** total units sold."
+        )
+
+    if any(w in msg_lower for w in ["trend", "sales trend", "monthly sale"]):
+        return (
+            f"Here are the sales trends. {kpi_text}",
+            {"show": "figure", "chart": "sales"},
+        )
+
+    if any(w in msg_lower for w in ["sentiment", "review", "positive", "negative"]):
+        return (
+            f"Here is the sentiment distribution across sampled book titles. {kpi_text}",
+            {"show": "figure", "chart": "sentiment"},
+        )
+
+    if any(w in msg_lower for w in ["arima", "forecast", "predict"]):
+        return (
+            f"Here are the sales trends and forecasts. {kpi_text}",
+            {"show": "figure", "chart": "sales"},
+        )
+
+    if any(w in msg_lower for w in ["top", "best sell", "popular", "rank"]):
+        return (
+            f"Here are the top-selling titles by units sold. {kpi_text}",
+            {"show": "table", "scope": "python", "filename": "top_titles_by_units_sold.csv"},
+        )
+
+    if any(w in msg_lower for w in ["price", "pricing", "decision"]):
+        return (
+            f"Here are the pricing decisions. {kpi_text}",
+            {"show": "table", "scope": "python", "filename": "pricing_decisions.csv"},
+        )
+
+    if any(w in msg_lower for w in ["dashboard", "overview", "summary", "kpi"]):
+        return (
+            f"Dashboard overview: {kpi_text}\n\nAsk me about sales trends, sentiment, forecasts, "
+            "pricing, or top sellers to see specific visualizations.",
+            {"show": "table", "scope": "python", "filename": "df_dashboard.csv"},
+        )
+
+    # Default
+    return (
+        f"I can show you various analyses. {kpi_text}\n\n"
+        "Try asking about: **sales trends**, **sentiment**, **ARIMA forecasts**, "
+        "**pricing decisions**, **top sellers**, or **dashboard overview**.",
+        {"show": "none"},
+    )
+
+
+# =========================================================
+# KPI CARDS (BubbleBusters style)
+# =========================================================
+
+def render_kpi_cards() -> str:
+    kpis = load_kpis()
+    if not kpis:
+        return (
+            '<div style="background:rgba(255,255,255,.65);backdrop-filter:blur(16px);'
+            'border-radius:20px;padding:28px;text-align:center;'
+            'border:1.5px solid rgba(255,255,255,.7);'
+            'box-shadow:0 8px 32px rgba(124,92,191,.08);">'
+            '<div style="font-size:36px;margin-bottom:10px;">📊</div>'
+            '<div style="color:#a48de8;font-size:14px;'
+            'font-weight:800;margin-bottom:6px;">No data yet</div>'
+            '<div style="color:#9d8fc4;font-size:12px;">'
+            'Run the pipeline to populate these cards.</div>'
+            '</div>'
+        )
+
+    def card(icon, label, value, colour):
+        return f"""
+        <div style="background:rgba(255,255,255,.72);backdrop-filter:blur(16px);
+                    border-radius:20px;padding:18px 14px 16px;text-align:center;
+                    border:1.5px solid rgba(255,255,255,.8);
+                    box-shadow:0 4px 16px rgba(124,92,191,.08);
+                    border-top:3px solid {colour};">
+            <div style="font-size:26px;margin-bottom:7px;line-height:1;">{icon}</div>
+            <div style="color:#9d8fc4;font-size:9.5px;text-transform:uppercase;
+                        letter-spacing:1.8px;margin-bottom:7px;font-weight:800;">{label}</div>
+            <div style="color:#2d1f4e;font-size:16px;font-weight:800;">{value}</div>
+        </div>"""
+
+    kpi_config = [
+        ("n_titles",         "📚", "Book Titles",  "#a48de8"),
+        ("n_months",         "📅", "Time Periods", "#7aa6f8"),
+        ("total_units_sold", "📦", "Units Sold",   "#6ee7c7"),
+        ("total_revenue",    "💰", "Revenue",      "#3dcba8"),
+    ]
+
+    html = (
+        '<div style="display:grid;grid-template-columns:repeat(auto-fit,minmax(140px,1fr));'
+        'gap:12px;margin-bottom:24px;">'
+    )
+    for key, icon, label, colour in kpi_config:
+        val = kpis.get(key)
+        if val is None:
+            continue
+        if isinstance(val, (int, float)) and val > 100:
+            val = f"{val:,.0f}"
+        html += card(icon, label, str(val), colour)
+    # Extra KPIs not in config
+    known = {k for k, *_ in kpi_config}
+    for key, val in kpis.items():
+        if key not in known:
+            label = key.replace("_", " ").title()
+            if isinstance(val, (int, float)) and val > 100:
+                val = f"{val:,.0f}"
+            html += card("📈", label, str(val), "#8fa8f8")
+    html += "</div>"
+    return html
+
+
+# =========================================================
+# INTERACTIVE PLOTLY CHARTS (BubbleBusters style)
+# =========================================================
+
+CHART_PALETTE = ["#7c5cbf", "#2ec4a0", "#e8537a", "#e8a230", "#5e8fef",
+                 "#c45ea8", "#3dbacc", "#a0522d", "#6aaa3a", "#d46060"]
+
+def _styled_layout(**kwargs) -> dict:
+    defaults = dict(
+        template="plotly_white",
+        paper_bgcolor="rgba(255,255,255,0.95)",
+        plot_bgcolor="rgba(255,255,255,0.98)",
+        font=dict(family="system-ui, sans-serif", color="#2d1f4e", size=12),
+        margin=dict(l=60, r=20, t=70, b=70),
+        legend=dict(
+            orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1,
+            bgcolor="rgba(255,255,255,0.92)",
+            bordercolor="rgba(124,92,191,0.35)", borderwidth=1,
+        ),
+        title=dict(font=dict(size=15, color="#4b2d8a")),
+    )
+    defaults.update(kwargs)
+    return defaults
+
+
+def _empty_chart(title: str) -> go.Figure:
+    fig = go.Figure()
+    fig.update_layout(
+        title=title, height=420, template="plotly_white",
+        paper_bgcolor="rgba(255,255,255,0.95)",
+        annotations=[dict(text="Run the pipeline to generate data",
+            x=0.5, y=0.5, xref="paper", yref="paper", showarrow=False,
+            font=dict(size=14, color="rgba(124,92,191,0.5)"))],
+    )
+    return fig
+
+
+def build_sales_chart() -> go.Figure:
+    path = PY_TAB_DIR / "df_dashboard.csv"
+    if not path.exists():
+        return _empty_chart("Sales Trends — run the pipeline first")
+    df = pd.read_csv(path)
+    date_col = next((c for c in df.columns if "month" in c.lower() or "date" in c.lower()), None)
+    val_cols = [c for c in df.columns if c != date_col and df[c].dtype in ("float64", "int64")]
+    if not date_col or not val_cols:
+        return _empty_chart("Could not auto-detect columns in df_dashboard.csv")
+    df[date_col] = pd.to_datetime(df[date_col], errors="coerce")
+    fig = go.Figure()
+    for i, col in enumerate(val_cols):
+        fig.add_trace(go.Scatter(
+            x=df[date_col], y=df[col], name=col.replace("_", " ").title(),
+            mode="lines+markers", line=dict(color=CHART_PALETTE[i % len(CHART_PALETTE)], width=2),
+            marker=dict(size=4),
+            hovertemplate=f"<b>{col.replace('_',' ').title()}</b><br>%{{x|%b %Y}}: %{{y:,.0f}}<extra></extra>",
+        ))
+    fig.update_layout(**_styled_layout(height=450, hovermode="x unified",
+                                        title=dict(text="Monthly Overview")))
+    fig.update_xaxes(gridcolor="rgba(124,92,191,0.15)", showgrid=True)
+    fig.update_yaxes(gridcolor="rgba(124,92,191,0.15)", showgrid=True)
+    return fig
+
+
+def build_sentiment_chart() -> go.Figure:
+    path = PY_TAB_DIR / "sentiment_counts_sampled.csv"
+    if not path.exists():
+        return _empty_chart("Sentiment Distribution — run the pipeline first")
+    df = pd.read_csv(path)
+    title_col = df.columns[0]
+    sent_cols = [c for c in ["negative", "neutral", "positive"] if c in df.columns]
+    if not sent_cols:
+        return _empty_chart("No sentiment columns found in CSV")
+    colors = {"negative": "#e8537a", "neutral": "#5e8fef", "positive": "#2ec4a0"}
+    fig = go.Figure()
+    for col in sent_cols:
+        fig.add_trace(go.Bar(
+            name=col.title(), y=df[title_col], x=df[col],
+            orientation="h", marker_color=colors.get(col, "#888"),
+            hovertemplate=f"<b>{col.title()}</b>: %{{x}}<extra></extra>",
+        ))
+    fig.update_layout(**_styled_layout(
+        height=max(400, len(df) * 28), barmode="stack",
+        title=dict(text="Sentiment Distribution by Book"),
+    ))
+    fig.update_xaxes(title="Number of Reviews")
+    fig.update_yaxes(autorange="reversed")
+    return fig
+
+
+def build_top_sellers_chart() -> go.Figure:
+    path = PY_TAB_DIR / "top_titles_by_units_sold.csv"
+    if not path.exists():
+        return _empty_chart("Top Sellers — run the pipeline first")
+    df = pd.read_csv(path).head(15)
+    title_col = next((c for c in df.columns if "title" in c.lower()), df.columns[0])
+    val_col = next((c for c in df.columns if "unit" in c.lower() or "sold" in c.lower()), df.columns[-1])
+    fig = go.Figure(go.Bar(
+        y=df[title_col], x=df[val_col], orientation="h",
+        marker=dict(color=df[val_col], colorscale=[[0, "#c5b4f0"], [1, "#7c5cbf"]]),
+        hovertemplate="<b>%{y}</b><br>Units: %{x:,.0f}<extra></extra>",
+    ))
+    fig.update_layout(**_styled_layout(
+        height=max(400, len(df) * 30),
+        title=dict(text="Top Selling Titles"), showlegend=False,
+    ))
+    fig.update_yaxes(autorange="reversed")
+    fig.update_xaxes(title="Total Units Sold")
+    return fig
+
+
+def refresh_dashboard():
+    return render_kpi_cards(), build_sales_chart(), build_sentiment_chart(), build_top_sellers_chart()
+
+
+# =========================================================
+# UI
+# =========================================================
+
+ensure_dirs()
+
+def load_css() -> str:
+    css_path = BASE_DIR / "style.css"
+    return css_path.read_text(encoding="utf-8") if css_path.exists() else ""
+
+
+with gr.Blocks(title="AIBDM 2026 Workshop App") as demo:
+
+    gr.Markdown(
+        "# SE21 App Template\n"
+        "*This is an app template for SE21 students*",
+        elem_id="escp_title",
+    )
+
+    # ===========================================================
+    # TAB 1 -- Pipeline Runner
+    # ===========================================================
+    with gr.Tab("Pipeline Runner"):
+        gr.Markdown()
+
+        with gr.Row():
+            with gr.Column(scale=1):
+                btn_nb1 = gr.Button("Step 1: Data Creation", variant="secondary")
+            with gr.Column(scale=1):
+                btn_nb2 = gr.Button("Step 2: Python Analysis", variant="secondary")
+
+        with gr.Row():
+            btn_all = gr.Button("Run Full Pipeline (Both Steps)", variant="primary")
+
+        run_log = gr.Textbox(
+            label="Execution Log",
+            lines=18,
+            max_lines=30,
+            interactive=False,
+        )
+
+        btn_nb1.click(run_datacreation, outputs=[run_log])
+        btn_nb2.click(run_pythonanalysis, outputs=[run_log])
+        btn_all.click(run_full_pipeline, outputs=[run_log])
+
+    # ===========================================================
+    # TAB 2 -- Dashboard (KPIs + Interactive Charts + Gallery)
+    # ===========================================================
+    with gr.Tab("Dashboard"):
+        kpi_html = gr.HTML(value=render_kpi_cards)
+
+        refresh_btn = gr.Button("Refresh Dashboard", variant="primary")
+
+        gr.Markdown("#### Interactive Charts")
+        chart_sales = gr.Plot(label="Monthly Overview")
+        chart_sentiment = gr.Plot(label="Sentiment Distribution")
+        chart_top = gr.Plot(label="Top Sellers")
+
+        gr.Markdown("#### Static Figures (from notebooks)")
+        gallery = gr.Gallery(
+            label="Generated Figures",
+            columns=2,
+            height=480,
+            object_fit="contain",
+        )
+
+        gr.Markdown("#### Data Tables")
+        table_dropdown = gr.Dropdown(
+            label="Select a table to view",
+            choices=[],
+            interactive=True,
+        )
+        table_display = gr.Dataframe(
+            label="Table Preview",
+            interactive=False,
+        )
+
+        def _on_refresh():
+            kpi, c1, c2, c3 = refresh_dashboard()
+            figs, dd, df = refresh_gallery()
+            return kpi, c1, c2, c3, figs, dd, df
+
+        refresh_btn.click(
+            _on_refresh,
+            outputs=[kpi_html, chart_sales, chart_sentiment, chart_top,
+                     gallery, table_dropdown, table_display],
+        )
+        table_dropdown.change(
+            on_table_select,
+            inputs=[table_dropdown],
+            outputs=[table_display],
+        )
+
+    # ===========================================================
+    # TAB 3 -- AI Dashboard
+    # ===========================================================
+    with gr.Tab('"AI" Dashboard'):
+        _ai_status = (
+            "Connected to your **n8n workflow**." if N8N_WEBHOOK_URL
+            else "**LLM active.**" if LLM_ENABLED
+            else "Using **keyword matching**. Upgrade options: "
+                 "set `N8N_WEBHOOK_URL` to connect your n8n workflow, "
+                 "or set `HF_API_KEY` for direct LLM access."
+        )
+        gr.Markdown(
+            "### Ask questions, get interactive visualisations\n\n"
+            f"Type a question and the system will pick the right interactive chart or table. {_ai_status}"
+        )
+
+        with gr.Row(equal_height=True):
+            with gr.Column(scale=1):
+                chatbot = gr.Chatbot(
+                    label="Conversation",
+                    height=380,
+                )
+                user_input = gr.Textbox(
+                    label="Ask about your data",
+                    placeholder="e.g. Show me sales trends / What are the top sellers? / Sentiment analysis",
+                    lines=1,
+                )
+                gr.Examples(
+                    examples=[
+                        "Show me the sales trends",
+                        "What does the sentiment look like?",
+                        "Which titles sell the most?",
+                        "Show the ARIMA forecasts",
+                        "What are the pricing decisions?",
+                        "Give me a dashboard overview",
+                    ],
+                    inputs=user_input,
+                )
+
+            with gr.Column(scale=1):
+                ai_figure = gr.Plot(
+                    label="Interactive Chart",
+                )
+                ai_table = gr.Dataframe(
+                    label="Data Table",
+                    interactive=False,
+                )
+
+        user_input.submit(
+            ai_chat,
+            inputs=[user_input, chatbot],
+            outputs=[chatbot, user_input, ai_figure, ai_table],
+        )
+
+
+demo.launch(css=load_css(), allowed_paths=[str(BASE_DIR)])

datacreation.ipynb

@@ -0,0 +1,1300 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XQZJSSopDet9",
+        "outputId": "61ea737f-4653-41ba-964f-f4c88e7a0738"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (2.2.2)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (2.0.2)\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)\n",
+            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.12/dist-packages (1.6.1)\n",
+            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.12/dist-packages (0.14.6)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas) (2.9.0.post0)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas) (2026.1)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (4.62.1)\n",
+            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.5.0)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (26.1)\n",
+            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (11.3.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (3.3.2)\n",
+            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (1.16.3)\n",
+            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (1.5.3)\n",
+            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (3.6.0)\n",
+            "Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.12/dist-packages (from statsmodels) (1.0.2)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install pandas numpy matplotlib scikit-learn statsmodels"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import random\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "random.seed(42)\n",
+        "np.random.seed(42)"
+      ],
+      "metadata": {
+        "id": "yzXlo3jYDi0f"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"=\" * 50)\n",
+        "print(\"STEP 1/2: Data Creation (real data + synthetic enrichment)\")\n",
+        "print(\"=\" * 50)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EL_CQaWsDkGw",
+        "outputId": "793b948a-a315-43d5-d2bc-6b8589dcb4b4"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "==================================================\n",
+            "STEP 1/2: Data Creation (real data + synthetic enrichment)\n",
+            "==================================================\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "jobs_df = pd.read_csv(\"CSV-JOBS.csv\")\n",
+        "\n",
+        "print(\"Shape:\", jobs_df.shape)\n",
+        "print(\"Columns:\")\n",
+        "print(jobs_df.columns.tolist())\n",
+        "\n",
+        "display(jobs_df.head())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 505
+        },
+        "id": "jyIW1KljDlXP",
+        "outputId": "ea7ebc0a-bf5f-4923-b855-2d35cbd83968"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Shape: (103, 9)\n",
+            "Columns:\n",
+            "['ID', 'title', 'Domain(s)', 'Grade', 'Type of contract', 'Institution(s)    ', 'Location(s)', 'Deadline ', 'Link to Content']\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "      ID                                              title  \\\n",
+              "0  19702                                  Project Assistant   \n",
+              "1  19703                                     Policy Officer   \n",
+              "2  19664  Senior Military Advisor to the Executive Director   \n",
+              "3  19665                                  Structures Expert   \n",
+              "4  19666  Certification Expert - Hydromechanical and Fli...   \n",
+              "\n",
+              "                  Domain(s)   Grade Type of contract  \\\n",
+              "0  Justice and human rights  FG III   Contract staff   \n",
+              "1  Justice and human rights   FG IV   Contract staff   \n",
+              "2                 Transport   AD 10  Temporary staff   \n",
+              "3                 Transport    AD 7  Temporary staff   \n",
+              "4                 Transport    AD 7  Temporary staff   \n",
+              "\n",
+              "                                  Institution(s)            Location(s)  \\\n",
+              "0  (FRA) European Union Agency for Fundamental Ri...   Vienna (Austria)   \n",
+              "1  (FRA) European Union Agency for Fundamental Ri...   Vienna (Austria)   \n",
+              "2       (EASA) European Union Aviation Safety Agency  Cologne (Germany)   \n",
+              "3       (EASA) European Union Aviation Safety Agency  Cologne (Germany)   \n",
+              "4       (EASA) European Union Aviation Safety Agency  Cologne (Germany)   \n",
+              "\n",
+              "            Deadline                                     Link to Content  \n",
+              "0  30/04/2026 - 13:00  https://eu-careers.europa.eu/en/job-opportunit...  \n",
+              "1  30/04/2026 - 13:00  https://eu-careers.europa.eu/en/job-opportunit...  \n",
+              "2  30/04/2026 - 23:59  https://eu-careers.europa.eu/en/job-opportunit...  \n",
+              "3  30/04/2026 - 23:59  https://eu-careers.europa.eu/en/job-opportunit...  \n",
+              "4  30/04/2026 - 23:59  https://eu-careers.europa.eu/en/job-opportunit...  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-94849428-3ad1-4840-b983-058c86036565\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ID</th>\n",
+              "      <th>title</th>\n",
+              "      <th>Domain(s)</th>\n",
+              "      <th>Grade</th>\n",
+              "      <th>Type of contract</th>\n",
+              "      <th>Institution(s)</th>\n",
+              "      <th>Location(s)</th>\n",
+              "      <th>Deadline</th>\n",
+              "      <th>Link to Content</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>19702</td>\n",
+              "      <td>Project Assistant</td>\n",
+              "      <td>Justice and human rights</td>\n",
+              "      <td>FG III</td>\n",
+              "      <td>Contract staff</td>\n",
+              "      <td>(FRA) European Union Agency for Fundamental Ri...</td>\n",
+              "      <td>Vienna (Austria)</td>\n",
+              "      <td>30/04/2026 - 13:00</td>\n",
+              "      <td>https://eu-careers.europa.eu/en/job-opportunit...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>19703</td>\n",
+              "      <td>Policy Officer</td>\n",
+              "      <td>Justice and human rights</td>\n",
+              "      <td>FG IV</td>\n",
+              "      <td>Contract staff</td>\n",
+              "      <td>(FRA) European Union Agency for Fundamental Ri...</td>\n",
+              "      <td>Vienna (Austria)</td>\n",
+              "      <td>30/04/2026 - 13:00</td>\n",
+              "      <td>https://eu-careers.europa.eu/en/job-opportunit...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>19664</td>\n",
+              "      <td>Senior Military Advisor to the Executive Director</td>\n",
+              "      <td>Transport</td>\n",
+              "      <td>AD 10</td>\n",
+              "      <td>Temporary staff</td>\n",
+              "      <td>(EASA) European Union Aviation Safety Agency</td>\n",
+              "      <td>Cologne (Germany)</td>\n",
+              "      <td>30/04/2026 - 23:59</td>\n",
+              "      <td>https://eu-careers.europa.eu/en/job-opportunit...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>19665</td>\n",
+              "      <td>Structures Expert</td>\n",
+              "      <td>Transport</td>\n",
+              "      <td>AD 7</td>\n",
+              "      <td>Temporary staff</td>\n",
+              "      <td>(EASA) European Union Aviation Safety Agency</td>\n",
+              "      <td>Cologne (Germany)</td>\n",
+              "      <td>30/04/2026 - 23:59</td>\n",
+              "      <td>https://eu-careers.europa.eu/en/job-opportunit...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>19666</td>\n",
+              "      <td>Certification Expert - Hydromechanical and Fli...</td>\n",
+              "      <td>Transport</td>\n",
+              "      <td>AD 7</td>\n",
+              "      <td>Temporary staff</td>\n",
+              "      <td>(EASA) European Union Aviation Safety Agency</td>\n",
+              "      <td>Cologne (Germany)</td>\n",
+              "      <td>30/04/2026 - 23:59</td>\n",
+              "      <td>https://eu-careers.europa.eu/en/job-opportunit...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-94849428-3ad1-4840-b983-058c86036565')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-94849428-3ad1-4840-b983-058c86036565 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-94849428-3ad1-4840-b983-058c86036565');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(jobs_df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 19664,\n        \"max\": 19703,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          19703,\n          19666,\n          19664\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Policy Officer\",\n          \"Certification Expert - Hydromechanical and Flight Control Systems\",\n          \"Senior Military Advisor to the Executive Director\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Domain(s)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Transport\",\n          \"Justice and human rights\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"FG IV\",\n          \"AD 7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type of contract\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Temporary staff\",\n          \"Contract staff\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Institution(s)    \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"(EASA) European Union Aviation Safety Agency\",\n          \"(FRA) European Union Agency for Fundamental Rights\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Location(s)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Cologne (Germany)\",\n          \"Vienna (Austria)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Deadline \",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"30/04/2026 - 23:59\",\n          \"30/04/2026 - 13:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Link to Content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"https://eu-careers.europa.eu/en/job-opportunities/policy-officer/fra-ca-polof-fgiv-2026\",\n          \"https://eu-careers.europa.eu/en/job-opportunities/certification-expert-hydromechanical-and-flight-control-systems/easa-ad-2026-997\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "jobs_clean = jobs_df.copy()\n",
+        "\n",
+        "jobs_clean.columns = jobs_clean.columns.str.strip()\n",
+        "jobs_clean[\"Deadline\"] = pd.to_datetime(jobs_clean[\"Deadline\"], errors=\"coerce\")\n",
+        "\n",
+        "jobs_clean = jobs_clean.dropna(subset=[\"title\", \"Domain(s)\", \"Type of contract\", \"Deadline\"])\n",
+        "\n",
+        "print(\"Cleaned shape:\", jobs_clean.shape)\n",
+        "display(jobs_clean.head())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 505
+        },
+        "id": "UgMfW50wDm1g",
+        "outputId": "4b88a153-aa33-44db-87ae-28013ca4ac56"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Cleaned shape: (99, 9)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_25014/2873856445.py:4: UserWarning: Parsing dates in %d/%m/%Y - %H:%M format when dayfirst=False (the default) was specified. Pass `dayfirst=True` or specify a format to silence this warning.\n",
+            "  jobs_clean[\"Deadline\"] = pd.to_datetime(jobs_clean[\"Deadline\"], errors=\"coerce\")\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "      ID                                              title  \\\n",
+              "0  19702                                  Project Assistant   \n",
+              "1  19703                                     Policy Officer   \n",
+              "2  19664  Senior Military Advisor to the Executive Director   \n",
+              "3  19665                                  Structures Expert   \n",
+              "4  19666  Certification Expert - Hydromechanical and Fli...   \n",
+              "\n",
+              "                  Domain(s)   Grade Type of contract  \\\n",
+              "0  Justice and human rights  FG III   Contract staff   \n",
+              "1  Justice and human rights   FG IV   Contract staff   \n",
+              "2                 Transport   AD 10  Temporary staff   \n",
+              "3                 Transport    AD 7  Temporary staff   \n",
+              "4                 Transport    AD 7  Temporary staff   \n",
+              "\n",
+              "                                      Institution(s)        Location(s)  \\\n",
+              "0  (FRA) European Union Agency for Fundamental Ri...   Vienna (Austria)   \n",
+              "1  (FRA) European Union Agency for Fundamental Ri...   Vienna (Austria)   \n",
+              "2       (EASA) European Union Aviation Safety Agency  Cologne (Germany)   \n",
+              "3       (EASA) European Union Aviation Safety Agency  Cologne (Germany)   \n",
+              "4       (EASA) European Union Aviation Safety Agency  Cologne (Germany)   \n",
+              "\n",
+              "             Deadline                                    Link to Content  \n",
+              "0 2026-04-30 13:00:00  https://eu-careers.europa.eu/en/job-opportunit...  \n",
+              "1 2026-04-30 13:00:00  https://eu-careers.europa.eu/en/job-opportunit...  \n",
+              "2 2026-04-30 23:59:00  https://eu-careers.europa.eu/en/job-opportunit...  \n",
+              "3 2026-04-30 23:59:00  https://eu-careers.europa.eu/en/job-opportunit...  \n",
+              "4 2026-04-30 23:59:00  https://eu-careers.europa.eu/en/job-opportunit...  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f0935762-8470-4529-94a0-87eee82320e7\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ID</th>\n",
+              "      <th>title</th>\n",
+              "      <th>Domain(s)</th>\n",
+              "      <th>Grade</th>\n",
+              "      <th>Type of contract</th>\n",
+              "      <th>Institution(s)</th>\n",
+              "      <th>Location(s)</th>\n",
+              "      <th>Deadline</th>\n",
+              "      <th>Link to Content</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>19702</td>\n",
+              "      <td>Project Assistant</td>\n",
+              "      <td>Justice and human rights</td>\n",
+              "      <td>FG III</td>\n",
+              "      <td>Contract staff</td>\n",
+              "      <td>(FRA) European Union Agency for Fundamental Ri...</td>\n",
+              "      <td>Vienna (Austria)</td>\n",
+              "      <td>2026-04-30 13:00:00</td>\n",
+              "      <td>https://eu-careers.europa.eu/en/job-opportunit...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>19703</td>\n",
+              "      <td>Policy Officer</td>\n",
+              "      <td>Justice and human rights</td>\n",
+              "      <td>FG IV</td>\n",
+              "      <td>Contract staff</td>\n",
+              "      <td>(FRA) European Union Agency for Fundamental Ri...</td>\n",
+              "      <td>Vienna (Austria)</td>\n",
+              "      <td>2026-04-30 13:00:00</td>\n",
+              "      <td>https://eu-careers.europa.eu/en/job-opportunit...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>19664</td>\n",
+              "      <td>Senior Military Advisor to the Executive Director</td>\n",
+              "      <td>Transport</td>\n",
+              "      <td>AD 10</td>\n",
+              "      <td>Temporary staff</td>\n",
+              "      <td>(EASA) European Union Aviation Safety Agency</td>\n",
+              "      <td>Cologne (Germany)</td>\n",
+              "      <td>2026-04-30 23:59:00</td>\n",
+              "      <td>https://eu-careers.europa.eu/en/job-opportunit...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>19665</td>\n",
+              "      <td>Structures Expert</td>\n",
+              "      <td>Transport</td>\n",
+              "      <td>AD 7</td>\n",
+              "      <td>Temporary staff</td>\n",
+              "      <td>(EASA) European Union Aviation Safety Agency</td>\n",
+              "      <td>Cologne (Germany)</td>\n",
+              "      <td>2026-04-30 23:59:00</td>\n",
+              "      <td>https://eu-careers.europa.eu/en/job-opportunit...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>19666</td>\n",
+              "      <td>Certification Expert - Hydromechanical and Fli...</td>\n",
+              "      <td>Transport</td>\n",
+              "      <td>AD 7</td>\n",
+              "      <td>Temporary staff</td>\n",
+              "      <td>(EASA) European Union Aviation Safety Agency</td>\n",
+              "      <td>Cologne (Germany)</td>\n",
+              "      <td>2026-04-30 23:59:00</td>\n",
+              "      <td>https://eu-careers.europa.eu/en/job-opportunit...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f0935762-8470-4529-94a0-87eee82320e7')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-f0935762-8470-4529-94a0-87eee82320e7 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-f0935762-8470-4529-94a0-87eee82320e7');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(jobs_clean\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 19664,\n        \"max\": 19703,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          19703,\n          19666,\n          19664\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Policy Officer\",\n          \"Certification Expert - Hydromechanical and Flight Control Systems\",\n          \"Senior Military Advisor to the Executive Director\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Domain(s)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Transport\",\n          \"Justice and human rights\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"FG IV\",\n          \"AD 7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type of contract\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Temporary staff\",\n          \"Contract staff\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Institution(s)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"(EASA) European Union Aviation Safety Agency\",\n          \"(FRA) European Union Agency for Fundamental Rights\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Location(s)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Cologne (Germany)\",\n          \"Vienna (Austria)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Deadline\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2026-04-30 13:00:00\",\n        \"max\": \"2026-04-30 23:59:00\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"2026-04-30 23:59:00\",\n          \"2026-04-30 13:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Link to Content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"https://eu-careers.europa.eu/en/job-opportunities/policy-officer/fra-ca-polof-fgiv-2026\",\n          \"https://eu-careers.europa.eu/en/job-opportunities/certification-expert-hydromechanical-and-flight-control-systems/easa-ad-2026-997\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Unique domains:\", jobs_clean[\"Domain(s)\"].nunique())\n",
+        "print(\"Unique contract types:\", jobs_clean[\"Type of contract\"].nunique())\n",
+        "print(\"Unique institutions:\", jobs_clean[\"Institution(s)\"].nunique())\n",
+        "print(\"Unique locations:\", jobs_clean[\"Location(s)\"].nunique())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wN8LLnbqDo5H",
+        "outputId": "1230b9e7-4d0b-44da-bf73-61a5a39d0ab9"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Unique domains: 35\n",
+            "Unique contract types: 5\n",
+            "Unique institutions: 38\n",
+            "Unique locations: 31\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "comments_by_demand = {\n",
+        "    \"high\": [\n",
+        "        \"This role appears to be in strong demand across institutions.\",\n",
+        "        \"The labor market for this profile looks highly competitive.\",\n",
+        "        \"This posting suggests strong employment opportunities.\",\n",
+        "        \"Demand for this skill set appears to be growing.\",\n",
+        "        \"This category seems to attract significant hiring activity.\"\n",
+        "    ],\n",
+        "    \"stable\": [\n",
+        "        \"This role appears to have steady demand.\",\n",
+        "        \"The market for this profile looks relatively balanced.\",\n",
+        "        \"This posting suggests moderate but stable opportunities.\",\n",
+        "        \"Demand appears consistent across institutions.\",\n",
+        "        \"This category seems to have regular hiring activity.\"\n",
+        "    ],\n",
+        "    \"low\": [\n",
+        "        \"This role appears to have weaker demand.\",\n",
+        "        \"The market for this profile looks more limited.\",\n",
+        "        \"This posting suggests fewer employment opportunities.\",\n",
+        "        \"Demand for this skill set appears relatively low.\",\n",
+        "        \"This category seems to have less hiring activity.\"\n",
+        "    ]\n",
+        "}"
+      ],
+      "metadata": {
+        "id": "HHqueIqZDqVe"
+      },
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "jobs_enriched = jobs_clean.copy()\n",
+        "\n",
+        "today = pd.Timestamp.today().normalize()\n",
+        "jobs_enriched[\"days_to_deadline\"] = (jobs_enriched[\"Deadline\"] - today).dt.days\n",
+        "\n",
+        "def urgency_from_days(days):\n",
+        "    if pd.isna(days):\n",
+        "        return random.randint(3, 6)\n",
+        "    if days <= 7:\n",
+        "        return random.randint(8, 10)\n",
+        "    elif days <= 21:\n",
+        "        return random.randint(5, 8)\n",
+        "    else:\n",
+        "        return random.randint(2, 6)\n",
+        "\n",
+        "jobs_enriched[\"urgency_score\"] = jobs_enriched[\"days_to_deadline\"].apply(urgency_from_days)\n",
+        "\n",
+        "def demand_from_urgency(score):\n",
+        "    if score >= 8:\n",
+        "        return random.randint(7, 10)\n",
+        "    elif score >= 5:\n",
+        "        return random.randint(4, 7)\n",
+        "    else:\n",
+        "        return random.randint(2, 5)\n",
+        "\n",
+        "jobs_enriched[\"job_demand_score\"] = jobs_enriched[\"urgency_score\"].apply(demand_from_urgency)\n",
+        "\n",
+        "def demand_label(score):\n",
+        "    if score <= 3:\n",
+        "        return \"low\"\n",
+        "    elif score <= 6:\n",
+        "        return \"stable\"\n",
+        "    else:\n",
+        "        return \"high\"\n",
+        "\n",
+        "jobs_enriched[\"demand_label\"] = jobs_enriched[\"job_demand_score\"].apply(demand_label)\n",
+        "\n",
+        "jobs_enriched[\"estimated_salary\"] = np.random.randint(35000, 95000, len(jobs_enriched))\n",
+        "\n",
+        "jobs_enriched[\"automation_risk\"] = np.random.choice(\n",
+        "    [\"low\", \"medium\", \"high\"],\n",
+        "    size=len(jobs_enriched),\n",
+        "    p=[0.35, 0.45, 0.20]\n",
+        ")\n",
+        "\n",
+        "jobs_enriched[\"estimated_applications\"] = np.random.randint(20, 250, len(jobs_enriched))\n",
+        "\n",
+        "jobs_enriched[\"job_comment\"] = jobs_enriched[\"demand_label\"].apply(\n",
+        "    lambda x: random.choice(comments_by_demand[x])\n",
+        ")\n",
+        "\n",
+        "display(jobs_enriched.head())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 539
+        },
+        "id": "l6PBz502Dr3a",
+        "outputId": "1f359396-203b-4d7a-8671-d58ccee2df1a"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "      ID                                              title  \\\n",
+              "0  19702                                  Project Assistant   \n",
+              "1  19703                                     Policy Officer   \n",
+              "2  19664  Senior Military Advisor to the Executive Director   \n",
+              "3  19665                                  Structures Expert   \n",
+              "4  19666  Certification Expert - Hydromechanical and Fli...   \n",
+              "\n",
+              "                  Domain(s)   Grade Type of contract  \\\n",
+              "0  Justice and human rights  FG III   Contract staff   \n",
+              "1  Justice and human rights   FG IV   Contract staff   \n",
+              "2                 Transport   AD 10  Temporary staff   \n",
+              "3                 Transport    AD 7  Temporary staff   \n",
+              "4                 Transport    AD 7  Temporary staff   \n",
+              "\n",
+              "                                      Institution(s)        Location(s)  \\\n",
+              "0  (FRA) European Union Agency for Fundamental Ri...   Vienna (Austria)   \n",
+              "1  (FRA) European Union Agency for Fundamental Ri...   Vienna (Austria)   \n",
+              "2       (EASA) European Union Aviation Safety Agency  Cologne (Germany)   \n",
+              "3       (EASA) European Union Aviation Safety Agency  Cologne (Germany)   \n",
+              "4       (EASA) European Union Aviation Safety Agency  Cologne (Germany)   \n",
+              "\n",
+              "             Deadline                                    Link to Content  \\\n",
+              "0 2026-04-30 13:00:00  https://eu-careers.europa.eu/en/job-opportunit...   \n",
+              "1 2026-04-30 13:00:00  https://eu-careers.europa.eu/en/job-opportunit...   \n",
+              "2 2026-04-30 23:59:00  https://eu-careers.europa.eu/en/job-opportunit...   \n",
+              "3 2026-04-30 23:59:00  https://eu-careers.europa.eu/en/job-opportunit...   \n",
+              "4 2026-04-30 23:59:00  https://eu-careers.europa.eu/en/job-opportunit...   \n",
+              "\n",
+              "   days_to_deadline  urgency_score  job_demand_score demand_label  \\\n",
+              "0                 1             10                 9         high   \n",
+              "1                 1              8                10         high   \n",
+              "2                 1              8                10         high   \n",
+              "3                 1             10                 9         high   \n",
+              "4                 1              9                 8         high   \n",
+              "\n",
+              "   estimated_salary automation_risk  estimated_applications  \\\n",
+              "0             91422             low                      87   \n",
+              "1             50795             low                     202   \n",
+              "2             35860             low                     222   \n",
+              "3             73158          medium                     203   \n",
+              "4             89343             low                     142   \n",
+              "\n",
+              "                                         job_comment  \n",
+              "0   Demand for this skill set appears to be growing.  \n",
+              "1  This posting suggests strong employment opport...  \n",
+              "2  This role appears to be in strong demand acros...  \n",
+              "3  The labor market for this profile looks highly...  \n",
+              "4  The labor market for this profile looks highly...  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a517b793-a4f6-427d-970a-3326feaef8c4\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ID</th>\n",
+              "      <th>title</th>\n",
+              "      <th>Domain(s)</th>\n",
+              "      <th>Grade</th>\n",
+              "      <th>Type of contract</th>\n",
+              "      <th>Institution(s)</th>\n",
+              "      <th>Location(s)</th>\n",
+              "      <th>Deadline</th>\n",
+              "      <th>Link to Content</th>\n",
+              "      <th>days_to_deadline</th>\n",
+              "      <th>urgency_score</th>\n",
+              "      <th>job_demand_score</th>\n",
+              "      <th>demand_label</th>\n",
+              "      <th>estimated_salary</th>\n",
+              "      <th>automation_risk</th>\n",
+              "      <th>estimated_applications</th>\n",
+              "      <th>job_comment</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>19702</td>\n",
+              "      <td>Project Assistant</td>\n",
+              "      <td>Justice and human rights</td>\n",
+              "      <td>FG III</td>\n",
+              "      <td>Contract staff</td>\n",
+              "      <td>(FRA) European Union Agency for Fundamental Ri...</td>\n",
+              "      <td>Vienna (Austria)</td>\n",
+              "      <td>2026-04-30 13:00:00</td>\n",
+              "      <td>https://eu-careers.europa.eu/en/job-opportunit...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>10</td>\n",
+              "      <td>9</td>\n",
+              "      <td>high</td>\n",
+              "      <td>91422</td>\n",
+              "      <td>low</td>\n",
+              "      <td>87</td>\n",
+              "      <td>Demand for this skill set appears to be growing.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>19703</td>\n",
+              "      <td>Policy Officer</td>\n",
+              "      <td>Justice and human rights</td>\n",
+              "      <td>FG IV</td>\n",
+              "      <td>Contract staff</td>\n",
+              "      <td>(FRA) European Union Agency for Fundamental Ri...</td>\n",
+              "      <td>Vienna (Austria)</td>\n",
+              "      <td>2026-04-30 13:00:00</td>\n",
+              "      <td>https://eu-careers.europa.eu/en/job-opportunit...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>8</td>\n",
+              "      <td>10</td>\n",
+              "      <td>high</td>\n",
+              "      <td>50795</td>\n",
+              "      <td>low</td>\n",
+              "      <td>202</td>\n",
+              "      <td>This posting suggests strong employment opport...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>19664</td>\n",
+              "      <td>Senior Military Advisor to the Executive Director</td>\n",
+              "      <td>Transport</td>\n",
+              "      <td>AD 10</td>\n",
+              "      <td>Temporary staff</td>\n",
+              "      <td>(EASA) European Union Aviation Safety Agency</td>\n",
+              "      <td>Cologne (Germany)</td>\n",
+              "      <td>2026-04-30 23:59:00</td>\n",
+              "      <td>https://eu-careers.europa.eu/en/job-opportunit...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>8</td>\n",
+              "      <td>10</td>\n",
+              "      <td>high</td>\n",
+              "      <td>35860</td>\n",
+              "      <td>low</td>\n",
+              "      <td>222</td>\n",
+              "      <td>This role appears to be in strong demand acros...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>19665</td>\n",
+              "      <td>Structures Expert</td>\n",
+              "      <td>Transport</td>\n",
+              "      <td>AD 7</td>\n",
+              "      <td>Temporary staff</td>\n",
+              "      <td>(EASA) European Union Aviation Safety Agency</td>\n",
+              "      <td>Cologne (Germany)</td>\n",
+              "      <td>2026-04-30 23:59:00</td>\n",
+              "      <td>https://eu-careers.europa.eu/en/job-opportunit...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>10</td>\n",
+              "      <td>9</td>\n",
+              "      <td>high</td>\n",
+              "      <td>73158</td>\n",
+              "      <td>medium</td>\n",
+              "      <td>203</td>\n",
+              "      <td>The labor market for this profile looks highly...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>19666</td>\n",
+              "      <td>Certification Expert - Hydromechanical and Fli...</td>\n",
+              "      <td>Transport</td>\n",
+              "      <td>AD 7</td>\n",
+              "      <td>Temporary staff</td>\n",
+              "      <td>(EASA) European Union Aviation Safety Agency</td>\n",
+              "      <td>Cologne (Germany)</td>\n",
+              "      <td>2026-04-30 23:59:00</td>\n",
+              "      <td>https://eu-careers.europa.eu/en/job-opportunit...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>9</td>\n",
+              "      <td>8</td>\n",
+              "      <td>high</td>\n",
+              "      <td>89343</td>\n",
+              "      <td>low</td>\n",
+              "      <td>142</td>\n",
+              "      <td>The labor market for this profile looks highly...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a517b793-a4f6-427d-970a-3326feaef8c4')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a517b793-a4f6-427d-970a-3326feaef8c4 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-a517b793-a4f6-427d-970a-3326feaef8c4');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(jobs_enriched\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 19664,\n        \"max\": 19703,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          19703,\n          19666,\n          19664\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Policy Officer\",\n          \"Certification Expert - Hydromechanical and Flight Control Systems\",\n          \"Senior Military Advisor to the Executive Director\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Domain(s)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Transport\",\n          \"Justice and human rights\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Grade\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"FG IV\",\n          \"AD 7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type of contract\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Temporary staff\",\n          \"Contract staff\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Institution(s)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"(EASA) European Union Aviation Safety Agency\",\n          \"(FRA) European Union Agency for Fundamental Rights\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Location(s)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Cologne (Germany)\",\n          \"Vienna (Austria)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Deadline\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2026-04-30 13:00:00\",\n        \"max\": \"2026-04-30 23:59:00\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"2026-04-30 23:59:00\",\n          \"2026-04-30 13:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Link to Content\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"https://eu-careers.europa.eu/en/job-opportunities/policy-officer/fra-ca-polof-fgiv-2026\",\n          \"https://eu-careers.europa.eu/en/job-opportunities/certification-expert-hydromechanical-and-flight-control-systems/easa-ad-2026-997\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"days_to_deadline\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"urgency_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 8,\n        \"max\": 10,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"job_demand_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 8,\n        \"max\": 10,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"demand_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"high\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"estimated_salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24288,\n        \"min\": 35860,\n        \"max\": 91422,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          50795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"automation_risk\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"medium\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"estimated_applications\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55,\n        \"min\": 87,\n        \"max\": 222,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          202\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"job_comment\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"This posting suggests strong employment opportunities.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "jobs_enriched.to_csv(\"jobs_enriched.csv\", index=False)\n",
+        "print(\"Saved: jobs_enriched.csv\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "s00SIZBsDtd-",
+        "outputId": "e5dbe4a5-e76a-48b2-ac18-866a964dfe3d"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saved: jobs_enriched.csv\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "jobs_enriched[\"deadline_month\"] = jobs_enriched[\"Deadline\"].dt.to_period(\"M\").astype(str)\n",
+        "\n",
+        "monthly_openings = (\n",
+        "    jobs_enriched\n",
+        "    .groupby([\"deadline_month\", \"Domain(s)\"], as_index=False)\n",
+        "    .agg(\n",
+        "        postings=(\"title\", \"count\"),\n",
+        "        avg_salary=(\"estimated_salary\", \"mean\"),\n",
+        "        avg_demand_score=(\"job_demand_score\", \"mean\"),\n",
+        "        avg_applications=(\"estimated_applications\", \"mean\")\n",
+        "    )\n",
+        ")\n",
+        "\n",
+        "display(monthly_openings.head())\n",
+        "print(\"Shape:\", monthly_openings.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 241
+        },
+        "id": "3NtBDh8ODvLW",
+        "outputId": "f2fab0c5-a09f-4276-a1c7-f171375ac23b"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  deadline_month                          Domain(s)  postings    avg_salary  \\\n",
+              "0        2026-04                    Data protection         1  79732.000000   \n",
+              "1        2026-04  Economics, Finance and Statistics         1  46284.000000   \n",
+              "2        2026-04                    Human Resources         1  89886.000000   \n",
+              "3        2026-04           Justice and human rights         2  71108.500000   \n",
+              "4        2026-04                          Transport         3  66120.333333   \n",
+              "\n",
+              "   avg_demand_score  avg_applications  \n",
+              "0               8.0             164.0  \n",
+              "1              10.0              57.0  \n",
+              "2               7.0              43.0  \n",
+              "3               9.5             144.5  \n",
+              "4               9.0             189.0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8cae9ab8-ac0a-4995-90c7-0321e6dabc74\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>deadline_month</th>\n",
+              "      <th>Domain(s)</th>\n",
+              "      <th>postings</th>\n",
+              "      <th>avg_salary</th>\n",
+              "      <th>avg_demand_score</th>\n",
+              "      <th>avg_applications</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2026-04</td>\n",
+              "      <td>Data protection</td>\n",
+              "      <td>1</td>\n",
+              "      <td>79732.000000</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>164.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2026-04</td>\n",
+              "      <td>Economics, Finance and Statistics</td>\n",
+              "      <td>1</td>\n",
+              "      <td>46284.000000</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>57.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2026-04</td>\n",
+              "      <td>Human Resources</td>\n",
+              "      <td>1</td>\n",
+              "      <td>89886.000000</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>43.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2026-04</td>\n",
+              "      <td>Justice and human rights</td>\n",
+              "      <td>2</td>\n",
+              "      <td>71108.500000</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>144.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2026-04</td>\n",
+              "      <td>Transport</td>\n",
+              "      <td>3</td>\n",
+              "      <td>66120.333333</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>189.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8cae9ab8-ac0a-4995-90c7-0321e6dabc74')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8cae9ab8-ac0a-4995-90c7-0321e6dabc74 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8cae9ab8-ac0a-4995-90c7-0321e6dabc74');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"print(\\\"Shape:\\\", monthly_openings\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"deadline_month\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2026-04\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Domain(s)\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Economics, Finance and Statistics\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"postings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16331.975065972749,\n        \"min\": 46284.0,\n        \"max\": 89886.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          46284.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_demand_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2041594578792296,\n        \"min\": 7.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_applications\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.56294685262401,\n        \"min\": 43.0,\n        \"max\": 189.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          57.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Shape: (51, 6)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "monthly_openings.to_csv(\"jobs_monthly_openings.csv\", index=False)\n",
+        "print(\"Saved: jobs_monthly_openings.csv\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_WpGUgWSDxDr",
+        "outputId": "3214fc49-8b8f-4810-eed9-6befe196b7df"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saved: jobs_monthly_openings.csv\n"
+          ]
+        }
+      ]
+    }
+  ]
+}

gitattributes

@@ -0,0 +1,36 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz 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
+background_top.png filter=lfs diff=lfs merge=lfs -text

requirements.txt

@@ -0,0 +1,17 @@
+gradio==6.0.0
+pandas>=2.0.0
+numpy>=1.24.0
+matplotlib>=3.7.0
+seaborn>=0.13.0
+statsmodels>=0.14.0
+scikit-learn>=1.3.0
+papermill>=2.5.0
+nbformat>=5.9.0
+pillow>=10.0.0
+requests>=2.31.0
+beautifulsoup4>=4.12.0
+vaderSentiment>=3.3.2
+huggingface_hub>=0.20.0
+textblob>=0.18.0
+faker>=20.0.0
+plotly>=5.18.0

style.css

@@ -0,0 +1,326 @@
+/* --- Target the Gradio app wrapper for backgrounds --- */
+gradio-app,
+.gradio-app,
+.main,
+#app,
+[data-testid="app"] {
+  background-color: rgb(40,9,109) !important;
+  background-image:
+    url('https://huggingface.co/spaces/atascioglu/SE21AppTemplate/resolve/main/background_top.png'),
+    url('https://huggingface.co/spaces/atascioglu/SE21AppTemplate/resolve/main/background_mid.png'),
+    url('https://huggingface.co/spaces/atascioglu/SE21AppTemplate/resolve/main/background_bottom.png') !important;
+  background-position:
+    top center,
+    0 913px,
+    bottom center !important;
+  background-repeat:
+    no-repeat,
+    repeat-y,
+    no-repeat !important;
+  background-size:
+    100% auto,
+    100% auto,
+    100% auto !important;
+  min-height: 100vh !important;
+}
+
+/* --- Fallback on html/body --- */
+html, body {
+  background-color: rgb(40,9,109) !important;
+  margin: 0 !important;
+  padding: 0 !important;
+  min-height: 100vh !important;
+}
+
+/* Bottom image is now part of the main background layers (positioned at bottom center) */
+
+/* --- Main container --- */
+.gradio-container {
+  max-width: 1400px !important;
+  width: 94vw !important;
+  margin: 0 auto !important;
+  padding-top: 220px !important;
+  padding-bottom: 150px !important;
+  background: transparent !important;
+}
+
+/* --- Title in ESCP gold --- */
+#escp_title h1 {
+  color: rgb(242,198,55) !important;
+  font-size: 3rem !important;
+  font-weight: 800 !important;
+  text-align: center !important;
+  margin: 0 0 12px 0 !important;
+}
+
+/* --- Subtitle --- */
+#escp_title p, #escp_title em {
+  color: rgba(255,255,255,0.85) !important;
+  text-align: center !important;
+}
+
+/* --- Tab bar background --- */
+.tabs > .tab-nav,
+.tab-nav,
+div[role="tablist"],
+.svelte-tabs > .tab-nav {
+  background: rgba(40,9,109,0.6) !important;
+  border-radius: 10px 10px 0 0 !important;
+  padding: 4px !important;
+}
+
+/* --- ALL tab buttons: force white text --- */
+.tabs > .tab-nav button,
+.tab-nav button,
+div[role="tablist"] button,
+button[role="tab"],
+.svelte-tabs button,
+.tab-nav > button,
+.tabs button {
+  color: #ffffff !important;
+  font-weight: 600 !important;
+  border: none !important;
+  background: transparent !important;
+  padding: 10px 20px !important;
+  border-radius: 8px 8px 0 0 !important;
+  opacity: 1 !important;
+}
+
+/* --- Selected tab: ESCP gold --- */
+.tabs > .tab-nav button.selected,
+.tab-nav button.selected,
+button[role="tab"][aria-selected="true"],
+button[role="tab"].selected,
+div[role="tablist"] button[aria-selected="true"],
+.svelte-tabs button.selected {
+  color: rgb(242,198,55) !important;
+  background: rgba(255,255,255,0.12) !important;
+}
+
+/* --- Unselected tabs: ensure visibility --- */
+.tabs > .tab-nav button:not(.selected),
+.tab-nav button:not(.selected),
+button[role="tab"][aria-selected="false"],
+button[role="tab"]:not(.selected),
+div[role="tablist"] button:not([aria-selected="true"]) {
+  color: #ffffff !important;
+  opacity: 1 !important;
+}
+
+/* --- White card panels --- */
+.gradio-container .gr-block,
+.gradio-container .gr-box,
+.gradio-container .gr-panel,
+.gradio-container .gr-group {
+  background: #ffffff !important;
+  border-radius: 10px !important;
+}
+
+/* --- Tab content area --- */
+.tabitem {
+  background: rgba(255,255,255,0.95) !important;
+  border-radius: 0 0 10px 10px !important;
+  padding: 16px !important;
+}
+
+/* --- Inputs --- */
+.gradio-container input,
+.gradio-container textarea,
+.gradio-container select {
+  background: #ffffff !important;
+  border: 1px solid #d1d5db !important;
+  border-radius: 8px !important;
+}
+
+/* --- Buttons: ESCP purple primary --- */
+.gradio-container button:not([role="tab"]) {
+  font-weight: 600 !important;
+  padding: 10px 16px !important;
+  border-radius: 10px !important;
+}
+
+button.primary {
+  background-color: rgb(40,9,109) !important;
+  color: #ffffff !important;
+  border: none !important;
+}
+
+button.primary:hover {
+  background-color: rgb(60,20,140) !important;
+}
+
+button.secondary {
+  background-color: #ffffff !important;
+  color: rgb(40,9,109) !important;
+  border: 2px solid rgb(40,9,109) !important;
+}
+
+button.secondary:hover {
+  background-color: rgb(240,238,250) !important;
+}
+
+/* --- Dataframes --- */
+[data-testid="dataframe"] {
+  background-color: #ffffff !important;
+  border-radius: 10px !important;
+}
+
+table {
+  font-size: 0.85rem !important;
+}
+
+/* --- Chatbot (AI Dashboard tab) --- */
+.gr-chatbot {
+  min-height: 380px !important;
+  background-color: #ffffff !important;
+  border-radius: 12px !important;
+}
+
+.gr-chatbot .message.user {
+  background-color: rgb(232,225,250) !important;
+  border-radius: 12px !important;
+}
+
+.gr-chatbot .message.bot {
+  background-color: #f3f4f6 !important;
+  border-radius: 12px !important;
+}
+
+/* --- Gallery --- */
+.gallery {
+  background: #ffffff !important;
+  border-radius: 10px !important;
+}
+
+/* --- Log textbox --- */
+textarea {
+  font-family: monospace !important;
+  font-size: 0.8rem !important;
+}
+
+/* --- Markdown headings inside tabs --- */
+.tabitem h3 {
+  color: rgb(40,9,109) !important;
+  font-weight: 700 !important;
+}
+
+.tabitem h4 {
+  color: #374151 !important;
+}
+
+/* --- Examples row (AI Dashboard) --- */
+.examples-row button {
+  background: rgb(240,238,250) !important;
+  color: rgb(40,9,109) !important;
+  border: 1px solid rgb(40,9,109) !important;
+  border-radius: 8px !important;
+  font-size: 0.85rem !important;
+}
+
+.examples-row button:hover {
+  background: rgb(232,225,250) !important;
+}
+
+/* --- Header / footer: transparent over banner --- */
+header, header *,
+footer, footer * {
+  background: transparent !important;
+  box-shadow: none !important;
+}
+
+footer a, footer button,
+header a, header button {
+  background: transparent !important;
+  border: none !important;
+  box-shadow: none !important;
+}
+
+#footer, #footer *,
+[class*="footer"], [class*="footer"] *,
+[class*="chip"], [class*="pill"], [class*="chip"] *, [class*="pill"] * {
+  background: transparent !important;
+  border: none !important;
+  box-shadow: none !important;
+}
+
+[data-testid*="api"], [data-testid*="settings"],
+[id*="api"], [id*="settings"],
+[class*="api"], [class*="settings"],
+[class*="bottom"], [class*="toolbar"], [class*="controls"] {
+  background: transparent !important;
+  box-shadow: none !important;
+}
+
+[data-testid*="api"] *, [data-testid*="settings"] *,
+[id*="api"] *, [id*="settings"] *,
+[class*="api"] *, [class*="settings"] * {
+  background: transparent !important;
+  box-shadow: none !important;
+}
+
+section footer {
+  background: transparent !important;
+}
+
+section footer button,
+section footer a {
+  background: transparent !important;
+  background-color: transparent !important;
+  border: none !important;
+  box-shadow: none !important;
+  color: white !important;
+}
+
+section footer button:hover,
+section footer button:focus,
+section footer a:hover,
+section footer a:focus {
+  background: transparent !important;
+  background-color: transparent !important;
+  box-shadow: none !important;
+}
+
+section footer button,
+section footer button * {
+  background: transparent !important;
+  background-color: transparent !important;
+  background-image: none !important;
+  box-shadow: none !important;
+  filter: none !important;
+}
+
+section footer button::before,
+section footer button::after {
+  background: transparent !important;
+  background-color: transparent !important;
+  background-image: none !important;
+  box-shadow: none !important;
+  filter: none !important;
+}
+
+section footer a,
+section footer a * {
+  background: transparent !important;
+  background-color: transparent !important;
+  box-shadow: none !important;
+}
+
+.gradio-container footer button,
+.gradio-container footer button *,
+.gradio-container .footer button,
+.gradio-container .footer button * {
+  background: transparent !important;
+  background-color: transparent !important;
+  background-image: none !important;
+  box-shadow: none !important;
+}
+
+.gradio-container footer button::before,
+.gradio-container footer button::after,
+.gradio-container .footer button::before,
+.gradio-container .footer button::after {
+  background: transparent !important;
+  background-color: transparent !important;
+  background-image: none !important;
+  box-shadow: none !important;
+}