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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: Hospitality
-emoji: 👀
-colorFrom: red
-colorTo: blue
+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_hospitality_gradio.py

@@ -0,0 +1,638 @@
+import os
+import re
+import json
+import time
+import traceback
+from pathlib import Path
+from typing import Dict, Any, List, Tuple, Optional
+
+import pandas as pd
+import gradio as gr
+import plotly.graph_objects as go
+
+try:
+    import papermill as pm
+except Exception:
+    pm = None
+
+try:
+    from huggingface_hub import InferenceClient
+except Exception:
+    InferenceClient = None
+
+# =========================================================
+# CONFIG — ITALY HOSPITALITY MARKET INSIGHT ASSISTANT
+# =========================================================
+
+BASE_DIR = Path(__file__).resolve().parent
+
+NB1 = os.environ.get("NB1", "1_Data_Creation_Italy_Hospitality.ipynb").strip()
+NB2 = os.environ.get("NB2", "2a_Python_Analysis_Italy_Hospitality.ipynb").strip()
+
+CLEANED_CSV = os.environ.get("CLEANED_CSV", "italy_hospitality_market_cleaned.csv").strip()
+ENRICHED_CSV = os.environ.get("ENRICHED_CSV", "italy_hospitality_market_enriched_synthetic.csv").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", "80"))
+MAX_LOG_CHARS = int(os.environ.get("MAX_LOG_CHARS", "8000"))
+
+# Hugging Face Inference API
+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()
+
+# Optional n8n automation webhook. Expected JSON response:
+# {"answer": "...", "chart": "risk|investment|city|region|overview|none"}
+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
+
+CHART_PALETTE = ["#7c5cbf", "#2ec4a0", "#e8537a", "#e8a230", "#5e8fef", "#c45ea8"]
+
+# =========================================================
+# 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() -> str:
+    return time.strftime("%Y%m%d-%H%M%S")
+
+def tail(text: str, n: int = MAX_LOG_CHARS) -> str:
+    return (text or "")[-n:]
+
+def csv_path(prefer_enriched: bool = True) -> Path:
+    enriched = BASE_DIR / ENRICHED_CSV
+    cleaned = BASE_DIR / CLEANED_CSV
+    if prefer_enriched and enriched.exists():
+        return enriched
+    if cleaned.exists():
+        return cleaned
+    return enriched
+
+def read_market_data(prefer_enriched: bool = True) -> pd.DataFrame:
+    path = csv_path(prefer_enriched)
+    if not path.exists():
+        return pd.DataFrame(columns=["section", "location", "entity", "metric_name", "value", "unit", "period", "source_page", "note"])
+    df = pd.read_csv(path)
+    df["value"] = pd.to_numeric(df.get("value"), errors="coerce")
+    return df
+
+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 {
+        "core_files": {
+            "cleaned_dataset": CLEANED_CSV if (BASE_DIR / CLEANED_CSV).exists() else None,
+            "enriched_synthetic_dataset": ENRICHED_CSV if (BASE_DIR / ENRICHED_CSV).exists() else None,
+        },
+        "python": {
+            "figures": _ls(PY_FIG_DIR, (".png", ".jpg", ".jpeg")),
+            "tables": _ls(PY_TAB_DIR, (".csv", ".json")),
+        },
+    }
+
+def pivot_metrics(df: pd.DataFrame, section: Optional[str] = None, entity: Optional[str] = None) -> pd.DataFrame:
+    d = df.copy()
+    if section:
+        d = d[d["section"].eq(section)]
+    if entity:
+        d = d[d["entity"].eq(entity)]
+    if d.empty:
+        return pd.DataFrame()
+    wide = d.pivot_table(index=["location", "entity"], columns="metric_name", values="value", aggfunc="first").reset_index()
+    wide.columns.name = None
+    return wide
+
+# =========================================================
+# PIPELINE RUNNERS
+# =========================================================
+
+def run_notebook(nb_name: str) -> str:
+    ensure_dirs()
+    if pm is None:
+        return "ERROR: papermill is not installed. Add it to requirements.txt."
+    nb_in = BASE_DIR / nb_name
+    if not nb_in.exists():
+        return f"ERROR: {nb_name} not found in {BASE_DIR}."
+    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}. Output saved to {nb_out.name}"
+
+def run_datacreation() -> str:
+    try:
+        log = run_notebook(NB1)
+        csvs = sorted(p.name for p in BASE_DIR.glob("*.csv"))
+        return f"OK — {log}\n\nCSVs available:\n" + "\n".join(f"  - {c}" for c in 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\nFigures: {', '.join(figs) or '(none)'}\nTables: {', '.join(tabs) or '(none)'}"
+    except Exception as e:
+        return f"FAILED — {e}\n\n{traceback.format_exc()[-2000:]}"
+
+def run_full_pipeline() -> str:
+    return "\n".join([
+        "=" * 58,
+        "STEP 1/2: Data Creation — real-world PwC hospitality indicators",
+        "=" * 58,
+        run_datacreation(),
+        "",
+        "=" * 58,
+        "STEP 2/2: Python Analysis — synthetic scores + dashboard artifacts",
+        "=" * 58,
+        run_pythonanalysis(),
+    ])
+
+# =========================================================
+# DATA / TABLE LOADERS
+# =========================================================
+
+def load_table_safe(path: Path) -> pd.DataFrame:
+    try:
+        if path.suffix.lower() == ".json":
+            obj = _read_json(path)
+            return pd.DataFrame([obj]) if isinstance(obj, dict) else pd.DataFrame(obj)
+        return _read_csv(path)
+    except Exception as e:
+        return pd.DataFrame([{"error": str(e)}])
+
+def refresh_gallery():
+    figures = [(str(p), p.stem.replace("_", " ").title()) for p in sorted(PY_FIG_DIR.glob("*.png"))]
+    idx = artifacts_index()
+    table_choices = list(idx["python"]["tables"])
+
+    # Always include core datasets in table dropdown
+    for core in [CLEANED_CSV, ENRICHED_CSV]:
+        if (BASE_DIR / core).exists() and core not in table_choices:
+            table_choices.insert(0, core)
+
+    default_df = pd.DataFrame()
+    if table_choices:
+        chosen = table_choices[0]
+        path = BASE_DIR / chosen if (BASE_DIR / chosen).exists() else PY_TAB_DIR / chosen
+        default_df = load_table_safe(path)
+
+    return figures, 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 = BASE_DIR / choice if (BASE_DIR / choice).exists() else PY_TAB_DIR / choice
+    if not path.exists():
+        return pd.DataFrame([{"error": f"File not found: {choice}"}])
+    return load_table_safe(path)
+
+# =========================================================
+# KPIs
+# =========================================================
+
+def load_kpis() -> Dict[str, Any]:
+    df = read_market_data(prefer_enriched=True)
+    if df.empty:
+        return {}
+    syn = df[df["section"].eq("synthetic_features")]
+    risk = syn[syn["metric_name"].eq("risk_score")]
+    investment = syn[syn["metric_name"].eq("investment_potential_score")]
+    attractiveness = syn[syn["metric_name"].eq("market_attractiveness_score")]
+
+    return {
+        "locations": int(df["location"].nunique()),
+        "metrics": int(df["metric_name"].nunique()),
+        "real_rows": int((df["section"] != "synthetic_features").sum()),
+        "synthetic_rows": int((df["section"] == "synthetic_features").sum()),
+        "avg_risk_score": round(float(risk["value"].mean()), 1) if not risk.empty else None,
+        "avg_investment_potential": round(float(investment["value"].mean()), 1) if not investment.empty else None,
+        "avg_market_attractiveness": round(float(attractiveness["value"].mean()), 1) if not attractiveness.empty else None,
+    }
+
+def render_kpi_cards() -> str:
+    kpis = load_kpis()
+    if not kpis:
+        return """
+        <div style='background:rgba(255,255,255,.72);border-radius:20px;padding:28px;text-align:center;border:1px solid #ddd;'>
+          <div style='font-size:34px;'>🏨</div>
+          <b>No hospitality data found yet</b><br>
+          <span>Run the pipeline or place the CSV files in the app folder.</span>
+        </div>"""
+
+    cards = [
+        ("📍", "Locations", kpis.get("locations"), "#a48de8"),
+        ("📊", "Metrics", kpis.get("metrics"), "#7aa6f8"),
+        ("🧪", "Synthetic Rows", kpis.get("synthetic_rows"), "#6ee7c7"),
+        ("⚠️", "Avg Risk Score", kpis.get("avg_risk_score"), "#e8537a"),
+        ("💼", "Avg Investment Potential", kpis.get("avg_investment_potential"), "#2ec4a0"),
+        ("⭐", "Avg Market Attractiveness", kpis.get("avg_market_attractiveness"), "#e8a230"),
+    ]
+
+    html = "<div style='display:grid;grid-template-columns:repeat(auto-fit,minmax(150px,1fr));gap:12px;margin-bottom:24px;'>"
+    for icon, label, value, colour in cards:
+        value = "—" if value is None else value
+        html += f"""
+        <div style='background:rgba(255,255,255,.78);border-radius:20px;padding:18px;text-align:center;
+                    border:1px solid rgba(124,92,191,.18);box-shadow:0 4px 16px rgba(124,92,191,.08);
+                    border-top:3px solid {colour};'>
+          <div style='font-size:26px;margin-bottom:7px;'>{icon}</div>
+          <div style='color:#8b78c6;font-size:10px;text-transform:uppercase;letter-spacing:1.4px;font-weight:800;'>{label}</div>
+          <div style='color:#2d1f4e;font-size:18px;font-weight:800;margin-top:6px;'>{value}</div>
+        </div>"""
+    html += "</div>"
+    return html
+
+# =========================================================
+# INTERACTIVE CHARTS
+# =========================================================
+
+def styled_layout(**kwargs) -> dict:
+    defaults = dict(
+        template="plotly_white",
+        paper_bgcolor="rgba(255,255,255,0.96)",
+        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),
+        title=dict(font=dict(size=16, color="#4b2d8a")),
+        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
+    )
+    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",
+        annotations=[dict(text="No data available yet", x=0.5, y=0.5, xref="paper", yref="paper", showarrow=False)],
+    )
+    return fig
+
+def build_city_performance_chart() -> go.Figure:
+    df = read_market_data(True)
+    wide = pivot_metrics(df, section="city_performance", entity="city")
+    needed = [c for c in ["occupancy_yoy", "adr_yoy", "revpar_yoy"] if c in wide.columns]
+    if wide.empty or not needed:
+        return empty_chart("City Performance — Occupancy, ADR, RevPAR")
+    fig = go.Figure()
+    for i, col in enumerate(needed):
+        fig.add_trace(go.Bar(x=wide["location"], y=wide[col], name=col.replace("_", " ").upper(), marker_color=CHART_PALETTE[i]))
+    fig.update_layout(**styled_layout(title=dict(text="City Performance YoY — Occupancy, ADR, RevPAR"), barmode="group", height=430))
+    fig.update_yaxes(title="YoY change (%)")
+    return fig
+
+def build_region_demand_chart() -> go.Figure:
+    df = read_market_data(True)
+    wide = pivot_metrics(df, section="regional_demand", entity="region")
+    needed = [c for c in ["domestic_demand_growth", "international_demand_growth"] if c in wide.columns]
+    if wide.empty or not needed:
+        return empty_chart("Regional Demand Growth")
+    fig = go.Figure()
+    for i, col in enumerate(needed):
+        fig.add_trace(go.Bar(y=wide["location"], x=wide[col], orientation="h", name=col.replace("_", " ").title(), marker_color=CHART_PALETTE[i]))
+    fig.update_layout(**styled_layout(title=dict(text="Regional Demand Growth — Domestic vs International"), barmode="group", height=max(420, len(wide)*42)))
+    fig.update_xaxes(title="Growth (%)")
+    fig.update_yaxes(autorange="reversed")
+    return fig
+
+def build_synthetic_scores_chart() -> go.Figure:
+    df = read_market_data(True)
+    wide = pivot_metrics(df, section="synthetic_features")
+    score_cols = [c for c in ["growth_score", "market_attractiveness_score", "investment_potential_score", "risk_score"] if c in wide.columns]
+    if wide.empty or not score_cols:
+        return empty_chart("Synthetic Scores")
+    fig = go.Figure()
+    for i, col in enumerate(score_cols):
+        fig.add_trace(go.Bar(x=wide["location"], y=wide[col], name=col.replace("_", " ").title(), marker_color=CHART_PALETTE[i % len(CHART_PALETTE)]))
+    fig.update_layout(**styled_layout(title=dict(text="Synthetic Market Scores by Location"), barmode="group", height=470))
+    fig.update_yaxes(title="Score (0–100)", range=[0, 105])
+    return fig
+
+def build_risk_chart() -> go.Figure:
+    df = read_market_data(True)
+    wide = pivot_metrics(df, section="synthetic_features")
+    if wide.empty or "risk_score" not in wide.columns:
+        return empty_chart("Risk Score")
+    wide = wide.sort_values("risk_score", ascending=True)
+    fig = go.Figure(go.Bar(
+        y=wide["location"],
+        x=wide["risk_score"],
+        orientation="h",
+        text=[f"{v:.1f}" for v in wide["risk_score"]],
+        marker=dict(color=wide["risk_score"], colorscale=[[0, "#2ec4a0"], [0.5, "#e8a230"], [1, "#e8537a"]]),
+    ))
+    fig.update_layout(**styled_layout(title=dict(text="Risk Score by Location"), showlegend=False, height=max(420, len(wide)*35)))
+    fig.update_xaxes(title="Risk score (0–100)", range=[0, 105])
+    return fig
+
+def build_investment_chart() -> go.Figure:
+    df = read_market_data(True)
+    wide = pivot_metrics(df, section="synthetic_features")
+    if wide.empty or "investment_potential_score" not in wide.columns:
+        return empty_chart("Investment Potential")
+    wide = wide.sort_values("investment_potential_score", ascending=True)
+    fig = go.Figure(go.Bar(
+        y=wide["location"],
+        x=wide["investment_potential_score"],
+        orientation="h",
+        text=[f"{v:.1f}" for v in wide["investment_potential_score"]],
+        marker=dict(color=wide["investment_potential_score"], colorscale=[[0, "#c5b4f0"], [1, "#7c5cbf"]]),
+    ))
+    fig.update_layout(**styled_layout(title=dict(text="Investment Potential Score by Location"), showlegend=False, height=max(420, len(wide)*35)))
+    fig.update_xaxes(title="Investment potential score (0–100)", range=[0, 105])
+    return fig
+
+def build_opportunity_table() -> pd.DataFrame:
+    df = read_market_data(True)
+    wide = pivot_metrics(df, section="synthetic_features")
+    if wide.empty:
+        return pd.DataFrame([{"hint": "No synthetic features found yet."}])
+    keep = [c for c in ["location", "entity", "growth_score", "market_attractiveness_score", "investment_potential_score", "risk_score", "risk_level", "opportunity_category"] if c in wide.columns]
+    out = wide[keep].copy()
+    for col in ["growth_score", "market_attractiveness_score", "investment_potential_score", "risk_score"]:
+        if col in out:
+            out[col] = out[col].round(1)
+    return out.sort_values(["entity", "investment_potential_score"], ascending=[True, False], na_position="last") if "investment_potential_score" in out else out
+
+def refresh_dashboard():
+    figs, dd, df = refresh_gallery()
+    return (
+        render_kpi_cards(),
+        build_city_performance_chart(),
+        build_region_demand_chart(),
+        build_synthetic_scores_chart(),
+        build_risk_chart(),
+        build_investment_chart(),
+        build_opportunity_table(),
+        figs,
+        dd,
+        df,
+    )
+
+# =========================================================
+# AI DASHBOARD — N8N / HUGGING FACE / KEYWORD FALLBACK
+# =========================================================
+
+DASHBOARD_SYSTEM = """You are the AI assistant for an Italy Hospitality Market Insight Assistant.
+The app uses a real-world PwC Italy Hospitality Market Snapshot dataset and an enriched synthetic dataset.
+The dataset has long-format columns: section, location, entity, metric_name, value, unit, period, source_page, note.
+
+Available artifacts:
+{artifacts_json}
+
+KPI summary:
+{kpis_json}
+
+Key concepts:
+- city performance: occupancy_yoy, adr_yoy, revpar_yoy for Milan, Rome, Florence, Venice.
+- regional demand: domestic_demand_growth and international_demand_growth.
+- synthetic features: growth_score, market_attractiveness_score, investment_potential_score, risk_score, risk_level, opportunity_category.
+
+Answer briefly and practically. At the END, output a JSON block exactly like:
+```json
+{{"show": "chart"|"table"|"none", "chart": "city|region|scores|risk|investment|none", "table": "opportunities|raw|none"}}
+```
+Choose:
+- city for occupancy / ADR / RevPAR / city performance questions.
+- region for domestic/international regional demand questions.
+- scores for comparing synthetic scores.
+- risk for risk score or risk level.
+- investment for investment potential / attractiveness / opportunity.
+- opportunities table for opportunity categories or strategic recommendations.
+"""
+
+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]:
+    for regex in [JSON_BLOCK_RE, FALLBACK_JSON_RE]:
+        m = regex.search(text)
+        if m:
+            try:
+                return json.loads(m.group(1) if regex is JSON_BLOCK_RE else m.group(0))
+            except Exception:
+                continue
+    return {"show": "none", "chart": "none", "table": "none"}
+
+def clean_response(text: str) -> str:
+    return JSON_BLOCK_RE.sub("", text).strip()
+
+def n8n_call(msg: str) -> Tuple[str, Optional[Dict[str, str]]]:
+    import requests
+    try:
+        resp = requests.post(N8N_WEBHOOK_URL, json={"question": msg, "project": "italy_hospitality_market"}, timeout=25)
+        resp.raise_for_status()
+        data = resp.json()
+        answer = data.get("answer") or data.get("reply") or "No answer returned by n8n."
+        chart = data.get("chart", "none")
+        table = data.get("table", "none")
+        return answer, {"show": "chart" if chart != "none" else ("table" if table != "none" else "none"), "chart": chart, "table": table}
+    except Exception as e:
+        return f"n8n error: {e}. Falling back to local logic.", None
+
+def keyword_fallback(msg: str, kpis: Dict[str, Any]) -> Tuple[str, Dict[str, str]]:
+    m = msg.lower()
+    kpi_text = ""
+    if kpis:
+        kpi_text = f"The dataset covers {kpis.get('locations', '?')} locations and {kpis.get('metrics', '?')} metrics."
+
+    if any(w in m for w in ["occupancy", "adr", "revpar", "city", "milan", "rome", "florence", "venice"]):
+        return f"Here is the city performance view for occupancy, ADR, and RevPAR. {kpi_text}", {"show": "chart", "chart": "city", "table": "none"}
+    if any(w in m for w in ["region", "regional", "domestic", "international", "demand", "lazio", "puglia", "sicilia"]):
+        return f"Here is the regional demand comparison between domestic and international growth. {kpi_text}", {"show": "chart", "chart": "region", "table": "none"}
+    if any(w in m for w in ["risk", "risky", "safe", "low risk", "high risk"]):
+        return f"Here is the risk score view. Lower scores indicate safer or more stable opportunities. {kpi_text}", {"show": "chart", "chart": "risk", "table": "opportunities"}
+    if any(w in m for w in ["investment", "potential", "attractive", "attractiveness", "opportunity", "recommend"]):
+        return f"Here is the investment potential view, supported by the opportunity-category table. {kpi_text}", {"show": "chart", "chart": "investment", "table": "opportunities"}
+    if any(w in m for w in ["score", "scores", "growth", "synthetic", "compare"]):
+        return f"Here is the synthetic score comparison across locations. {kpi_text}", {"show": "chart", "chart": "scores", "table": "opportunities"}
+    if any(w in m for w in ["table", "data", "raw", "dataset"]):
+        return f"Here is the enriched hospitality dataset table preview. {kpi_text}", {"show": "table", "chart": "none", "table": "raw"}
+
+    return (
+        f"You can ask about city performance, regional demand, risk, investment potential, synthetic scores, or opportunity categories. {kpi_text}",
+        {"show": "none", "chart": "none", "table": "none"},
+    )
+
+def resolve_chart(name: str):
+    return {
+        "city": build_city_performance_chart,
+        "region": build_region_demand_chart,
+        "scores": build_synthetic_scores_chart,
+        "risk": build_risk_chart,
+        "investment": build_investment_chart,
+    }.get(name, lambda: None)()
+
+def resolve_table(name: str):
+    if name == "opportunities":
+        return build_opportunity_table()
+    if name == "raw":
+        return read_market_data(True).head(MAX_PREVIEW_ROWS)
+    return None
+
+def ai_chat(user_msg: str, history: list):
+    if not user_msg or not user_msg.strip():
+        return history, "", None, None
+
+    idx = artifacts_index()
+    kpis = load_kpis()
+
+    if N8N_WEBHOOK_URL:
+        reply, directive = n8n_call(user_msg)
+        if directive is None:
+            reply_fb, directive = keyword_fallback(user_msg, kpis)
+            reply = reply + "\n\n" + reply_fb
+    elif LLM_ENABLED:
+        system = DASHBOARD_SYSTEM.format(
+            artifacts_json=json.dumps(idx, indent=2),
+            kpis_json=json.dumps(kpis, indent=2) if kpis else "No KPIs available yet.",
+        )
+        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=0.25,
+                max_tokens=650,
+                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_fb, directive = keyword_fallback(user_msg, kpis)
+            reply = f"Hugging Face error: {e}. Falling back to local logic.\n\n{reply_fb}"
+    else:
+        reply, directive = keyword_fallback(user_msg, kpis)
+
+    chart_out = resolve_chart(directive.get("chart", "none")) if directive.get("show") in ["chart", "figure"] or directive.get("chart") != "none" else None
+    table_out = resolve_table(directive.get("table", "none")) if directive.get("table") != "none" else None
+
+    new_history = (history or []) + [
+        {"role": "user", "content": user_msg},
+        {"role": "assistant", "content": reply},
+    ]
+    return new_history, "", chart_out, table_out
+
+# =========================================================
+# 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="Italy Hospitality Market Insight Assistant") as demo:
+    gr.Markdown(
+        "# Italy Hospitality Market Insight Assistant\n"
+        "*A Gradio app for PwC-based hospitality indicators, synthetic market scores, n8n automation and Hugging Face AI Q&A.*",
+        elem_id="escp_title",
+    )
+
+    with gr.Tab("Pipeline Runner"):
+        gr.Markdown("Run the project notebooks. Step 1 creates/cleans the dataset; Step 2 creates analysis outputs and synthetic insights.")
+        with gr.Row():
+            btn_nb1 = gr.Button("Step 1: Data Creation", variant="secondary")
+            btn_nb2 = gr.Button("Step 2: Python Analysis", variant="secondary")
+        btn_all = gr.Button("Run Full Pipeline", 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])
+
+    with gr.Tab("Dashboard"):
+        kpi_html = gr.HTML(value=render_kpi_cards)
+        refresh_btn = gr.Button("Refresh Dashboard", variant="primary")
+
+        gr.Markdown("#### Interactive Hospitality Charts")
+        with gr.Row():
+            chart_city = gr.Plot(label="City Performance")
+            chart_region = gr.Plot(label="Regional Demand")
+        with gr.Row():
+            chart_scores = gr.Plot(label="Synthetic Scores")
+            chart_risk = gr.Plot(label="Risk Score")
+        chart_investment = gr.Plot(label="Investment Potential")
+
+        gr.Markdown("#### Opportunity Categories")
+        opportunity_table = gr.Dataframe(label="Synthetic Strategy Table", interactive=False)
+
+        gr.Markdown("#### Static Figures and Data Tables")
+        gallery = gr.Gallery(label="Generated Figures", columns=2, height=430, object_fit="contain")
+        table_dropdown = gr.Dropdown(label="Select a table to view", choices=[], interactive=True)
+        table_display = gr.Dataframe(label="Table Preview", interactive=False)
+
+        refresh_btn.click(
+            refresh_dashboard,
+            outputs=[kpi_html, chart_city, chart_region, chart_scores, chart_risk, chart_investment, opportunity_table, gallery, table_dropdown, table_display],
+        )
+        table_dropdown.change(on_table_select, inputs=[table_dropdown], outputs=[table_display])
+
+    with gr.Tab('"AI" Dashboard'):
+        ai_status = (
+            "Connected to your **n8n workflow**." if N8N_WEBHOOK_URL else
+            "**Hugging Face LLM active.**" if LLM_ENABLED else
+            "Using **local keyword matching**. To activate AI, set `HF_API_KEY`; to activate automations, set `N8N_WEBHOOK_URL`."
+        )
+        gr.Markdown(
+            "### Ask questions about the Italy hospitality market\n\n"
+            f"{ai_status}\n\n"
+            "Examples: *Which city has the strongest RevPAR?*, *Show risk scores*, *Which regions are investment opportunities?*"
+        )
+        with gr.Row(equal_height=True):
+            with gr.Column(scale=1):
+                chatbot = gr.Chatbot(label="Conversation", height=400, type="messages")
+                user_input = gr.Textbox(label="Ask about your data", placeholder="e.g. Show me investment potential by location", lines=1)
+                gr.Examples(
+                    examples=[
+                        "Show me city performance for occupancy, ADR and RevPAR",
+                        "Which locations have the highest risk?",
+                        "Show investment potential by location",
+                        "Compare synthetic scores",
+                        "What is regional domestic vs international demand?",
+                        "Give me strategic recommendations",
+                    ],
+                    inputs=user_input,
+                )
+            with gr.Column(scale=1):
+                ai_figure = gr.Plot(label="Interactive Chart")
+                ai_table = gr.Dataframe(label="Relevant Table", interactive=False)
+
+        user_input.submit(ai_chat, inputs=[user_input, chatbot], outputs=[chatbot, user_input, ai_figure, ai_table])
+
+if __name__ == "__main__":
+    demo.launch(css=load_css(), allowed_paths=[str(BASE_DIR)])

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

gitattributes (1)

@@ -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;
+}