Update app.py

app.py

@@ -1,758 +1,257 @@
-import os
-import re
-import json
-import time
-import traceback
-from pathlib import Path
-from typing import Dict, Any, List, Tuple
+"""
+Restaurant Health Grade Predictor
+----------------------------------
+A Gradio app that predicts health inspection grades (A/B/C)
+using a placeholder Random Forest model trained on synthetic data.
+
+Requirements:
+    pip install gradio scikit-learn matplotlib numpy pandas
+"""
 
-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,
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder
+import warnings
+
+warnings.filterwarnings("ignore")
+
+# ──────────────────────────────────────────────────────────────────────────────
+# 1. Build a placeholder Random Forest model on synthetic data
+# ──────────────────────────────────────────────────────────────────────────────
+
+CUISINE_TYPES = [
+    "American", "Chinese", "Italian", "Mexican", "Japanese",
+    "Indian", "Thai", "Mediterranean", "French", "Korean",
+]
+
+VIOLATION_CODES = [
+    "No Violation",
+    "02A - No food safety certificate",
+    "04L - Evidence of mice or rats",
+    "06C - Food not protected",
+    "08A - Facility not sanitized",
+    "10B - Plumbing not properly installed",
+    "15L - Workers not using proper hygiene",
+]
+
+GRADE_LABELS = ["A", "B", "C"]
+
+# Encode categorical features
+cuisine_enc = LabelEncoder().fit(CUISINE_TYPES)
+violation_enc = LabelEncoder().fit(VIOLATION_CODES)
+
+def encode_inputs(cuisine: str, violation: str, score: float) -> np.ndarray:
+    c = cuisine_enc.transform([cuisine])[0]
+    v = violation_enc.transform([violation])[0]
+    return np.array([[c, v, score]])
+
+
+def generate_synthetic_data(n: int = 2000, seed: int = 42) -> tuple:
+    rng = np.random.default_rng(seed)
+    cuisines  = rng.integers(0, len(CUISINE_TYPES), n)
+    violations = rng.integers(0, len(VIOLATION_CODES), n)
+    scores    = rng.uniform(0, 100, n)
+
+    # Grade logic: score drives grade; violations add noise
+    grades = []
+    for i in range(n):
+        base = scores[i]
+        penalty = violations[i] * 3          # higher code → worse grade
+        effective = base - penalty
+        if effective >= 60:
+            grades.append(0)                 # A
+        elif effective >= 40:
+            grades.append(1)                 # B
+        else:
+            grades.append(2)                 # C
+
+    X = np.column_stack([cuisines, violations, scores])
+    y = np.array(grades)
+    return X, y
+
+
+print("Training placeholder Random Forest model …")
+X_train, y_train = generate_synthetic_data()
+model = RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1)
+model.fit(X_train, y_train)
+print("Model ready ✓")
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# 2. Prediction + chart function
+# ──────────────────────────────────────────────────────────────────────────────
+
+GRADE_COLORS = {
+    "A": "#2ECC71",   # green
+    "B": "#F39C12",   # amber
+    "C": "#E74C3C",   # red
+}
+
+def predict_grade(cuisine: str, violation: str, score: float):
+    """Run inference and return a grade label and a probability bar chart."""
+    X = encode_inputs(cuisine, violation, score)
+    proba = model.predict_proba(X)[0]               # shape (3,)
+    pred_idx = int(np.argmax(proba))
+    grade = GRADE_LABELS[pred_idx]
+    confidence = proba[pred_idx] * 100
+
+    # ── build the bar chart ──────────────────────────────────────────────────
+    fig, ax = plt.subplots(figsize=(6, 3.5))
+    fig.patch.set_facecolor("#1A1A2E")
+    ax.set_facecolor("#16213E")
+
+    bar_colors = [GRADE_COLORS[g] for g in GRADE_LABELS]
+    bars = ax.bar(
+        GRADE_LABELS,
+        proba * 100,
+        color=bar_colors,
+        width=0.5,
+        edgecolor="none",
+        zorder=3,
     )
 
+    # highlight the predicted grade with a glow border
+    pred_bar = bars[pred_idx]
+    pred_bar.set_linewidth(2.5)
+    pred_bar.set_edgecolor("white")
+
+    # value labels on bars
+    for bar, p in zip(bars, proba * 100):
+        ax.text(
+            bar.get_x() + bar.get_width() / 2,
+            bar.get_height() + 1.5,
+            f"{p:.1f}%",
+            ha="center", va="bottom",
+            color="white", fontsize=11, fontweight="bold",
+        )
 
-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.
+    ax.set_ylim(0, 110)
+    ax.set_xlabel("Predicted Grade", color="#AAAACC", fontsize=11, labelpad=8)
+    ax.set_ylabel("Probability (%)", color="#AAAACC", fontsize=11, labelpad=8)
+    ax.set_title(
+        f"Model Confidence — Predicted Grade: {grade}  ({confidence:.1f}%)",
+        color="white", fontsize=13, fontweight="bold", pad=12,
+    )
+    ax.tick_params(colors="white", labelsize=12)
+    for spine in ax.spines.values():
+        spine.set_visible(False)
+    ax.yaxis.grid(True, color="#2A2A4A", linewidth=0.8, zorder=0)
+    ax.set_axisbelow(True)
+
+    plt.tight_layout()
+
+    # ── compose the text output ───────────────────────────────────────────────
+    emoji = {"A": "🟢", "B": "🟡", "C": "🔴"}[grade]
+    summary = (
+        f"{emoji}  Predicted Health Grade: **{grade}**\n\n"
+        f"Confidence: {confidence:.1f}%\n\n"
+        f"---\n"
+        f"| Input | Value |\n"
+        f"|---|---|\n"
+        f"| Cuisine | {cuisine} |\n"
+        f"| Violation | {violation} |\n"
+        f"| Inspection Score | {score:.1f} |\n\n"
+        f"*Note: This uses a placeholder Random Forest model trained on "
+        f"synthetic data. Replace `generate_synthetic_data()` and re-train "
+        f"with real inspection records for production use.*"
+    )
 
-AVAILABLE ARTIFACTS (only reference ones that exist):
-{artifacts_json}
+    return summary, fig
 
-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": "..."}}
+# ──────────────────────────────────────────────────────────────────────────────
+# 3. Gradio UI
+# ──────────────────────────────────────────────────────────────────────────────
 
-   - 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.
+DESCRIPTION = """
+## 🍽️ Restaurant Health Grade Predictor
 
-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.
+Enter inspection details below to get a predicted **A / B / C** health grade
+and a probability breakdown from the Random Forest model.
 """
 
-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,
+with gr.Blocks(
+    title="Health Grade Predictor",
+    theme=gr.themes.Soft(
+        primary_hue="violet",
+        secondary_hue="slate",
+        neutral_hue="slate",
+    ),
+    css="""
+        .predict-btn { font-size: 1.1rem !important; padding: 0.7rem !important; }
+        #grade-output .prose { font-size: 1.05rem !important; }
+    """,
+) as demo:
+
+    gr.Markdown(DESCRIPTION)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            cuisine_input = gr.Dropdown(
+                choices=CUISINE_TYPES,
+                value="American",
+                label="🍜 Cuisine Type",
             )
-            raw = (
-                r["choices"][0]["message"]["content"]
-                if isinstance(r, dict)
-                else r.choices[0].message.content
+            violation_input = gr.Dropdown(
+                choices=VIOLATION_CODES,
+                value="No Violation",
+                label="⚠️ Violation Code",
+            )
+            score_input = gr.Slider(
+                minimum=0,
+                maximum=100,
+                value=85,
+                step=0.5,
+                label="📊 Inspection Score  (0 = worst, 100 = best)",
+            )
+            predict_btn = gr.Button(
+                "🔍  Predict Grade",
+                variant="primary",
+                elem_classes="predict-btn",
             )
-            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"},
-        )
+        with gr.Column(scale=2):
+            grade_output = gr.Markdown(
+                value="*Fill in the inputs and click **Predict Grade**.*",
+                elem_id="grade-output",
+            )
+            chart_output = gr.Plot(label="Grade Probability Distribution")
 
-    # 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"},
+    predict_btn.click(
+        fn=predict_grade,
+        inputs=[cuisine_input, violation_input, score_input],
+        outputs=[grade_output, chart_output],
     )
 
-
-# =========================================================
-# 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")),
+    gr.Examples(
+        examples=[
+            ["Italian",      "No Violation",                   95],
+            ["Chinese",      "04L - Evidence of mice or rats", 55],
+            ["Mexican",      "08A - Facility not sanitized",   40],
+            ["Japanese",     "02A - No food safety certificate",72],
+            ["Mediterranean","15L - Workers not using proper hygiene", 30],
+        ],
+        inputs=[cuisine_input, violation_input, score_input],
+        outputs=[grade_output, chart_output],
+        fn=predict_grade,
+        cache_examples=True,
+        label="📌 Quick Examples",
     )
-    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",
+        """
+        ---
+        **How grades work (synthetic rules used for training)**  
+        `Effective Score = Inspection Score − (Violation Code Index × 3)`  
+        • **A** → Effective ≥ 60 &nbsp;|&nbsp; **B** → 40–59 &nbsp;|&nbsp; **C** → < 40  
+
+        Replace `generate_synthetic_data()` with a real labelled dataset to make this production-ready.
+        """
     )
 
-    # ===========================================================
-    # 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)])
+if __name__ == "__main__":
+    demo.launch(share=False)