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+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
+
+RUN apt-get update && apt-get install -y --no-install-recommends         r-base r-base-dev build-essential curl git         libcurl4-openssl-dev libssl-dev libxml2-dev         && rm -rf /var/lib/apt/lists/*
+
+# R packages used in ranalysis.ipynb
+RUN R -e "install.packages(c('forecast','ggplot2','jsonlite','readr','dplyr','tidyr','stringr','lubridate','broom'), repos='https://cloud.r-project.org')"
+
+WORKDIR /app
+
+COPY requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Optional R kernel for notebooks
+RUN R -e "install.packages('IRkernel', repos='https://cloud.r-project.org/')"
+RUN R -e "IRkernel::installspec(user = FALSE)"
+
+COPY . /app
+
+EXPOSE 7860
+CMD ["python", "app.py"]

app.py

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+import os
+import re
+import json
+import time
+import traceback
+from pathlib import Path
+from typing import Dict, Any, List, Optional, Tuple
+
+import pandas as pd
+import gradio as gr
+import papermill as pm
+
+# 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()
+NB3 = os.environ.get("NB3", "ranalysis.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"
+R_FIG_DIR = ART_DIR / "r" / "figures"
+R_TAB_DIR = ART_DIR / "r" / "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()
+
+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, R_FIG_DIR, R_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")),
+        },
+        "r": {
+            "figures": _ls(R_FIG_DIR, (".png", ".jpg", ".jpeg")),
+            "tables": _ls(R_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_r() -> str:
+    try:
+        log = run_notebook(NB3)
+        idx = artifacts_index()
+        figs = idx["r"]["figures"]
+        tabs = idx["r"]["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 1/3: Data Creation (web scraping + synthetic data)")
+    logs.append("=" * 50)
+    logs.append(run_datacreation())
+    logs.append("")
+    logs.append("=" * 50)
+    logs.append("STEP 2/3: Python Analysis (sentiment, ARIMA, dashboard)")
+    logs.append("=" * 50)
+    logs.append(run_pythonanalysis())
+    logs.append("")
+    logs.append("=" * 50)
+    logs.append("STEP 3/3: R Analysis (ETS/ARIMA forecasting)")
+    logs.append("=" * 50)
+    logs.append(run_r())
+    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), f"Python | {p.stem.replace('_', ' ').title()}"))
+    for p in sorted(R_FIG_DIR.glob("*.png")):
+        items.append((str(p), f"R | {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 = []
+    for scope in ("python", "r"):
+        for name in idx[scope]["tables"]:
+            table_choices.append(f"{scope}/{name}")
+
+    default_df = pd.DataFrame()
+    if table_choices:
+        scope, name = table_choices[0].split("/", 1)
+        base = PY_TAB_DIR if scope == "python" else R_TAB_DIR
+        default_df = _load_table_safe(base / name)
+
+    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 or "/" not in choice:
+        return pd.DataFrame([{"hint": "Select a table above."}])
+    scope, name = choice.split("/", 1)
+    base = {"python": PY_TAB_DIR, "r": R_TAB_DIR}.get(scope)
+    if not base:
+        return pd.DataFrame([{"error": f"Unknown scope: {scope}"}])
+    path = base / name
+    if not path.exists():
+        return pd.DataFrame([{"error": f"File not found: {path}"}])
+    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 (Tab 3) -- 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 Python and R analysis pipelines.
+
+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"|"r", "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 R regression, the R notebook focuses on forecasting, show accuracy_table.csv.
+- If the user asks about forecast accuracy or model comparison, show accuracy_table.csv or forecast_compare.png.
+- 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 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()
+
+    if 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
+
+    fig_out = None
+    tab_out = None
+    show = directive.get("show", "none")
+    scope = directive.get("scope", "")
+    fname = directive.get("filename", "")
+
+    if show == "figure" and scope and fname:
+        base = {"python": PY_FIG_DIR, "r": R_FIG_DIR}.get(scope)
+        if base and (base / fname).exists():
+            fig_out = str(base / fname)
+        else:
+            reply += f"\n\n*(Could not find figure: {scope}/{fname})*"
+
+    if show == "table" and scope and fname:
+        base = {"python": PY_TAB_DIR, "r": R_TAB_DIR}.get(scope)
+        if base and (base / fname).exists():
+            tab_out = _load_table_safe(base / fname)
+        else:
+            reply += f"\n\n*(Could not find table: {scope}/{fname})*"
+
+    new_history = (history or []) + [
+        {"role": "user", "content": user_msg},
+        {"role": "assistant", "content": reply},
+    ]
+
+    return new_history, "", fig_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 any(idx[s]["figures"] or idx[s]["tables"] for s in ("python", "r")):
+        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 for sampled titles. {kpi_text}",
+            {"show": "figure", "scope": "python", "filename": "sales_trends_sampled_titles.png"},
+        )
+
+    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", "scope": "python", "filename": "sentiment_distribution_sampled_titles.png"},
+        )
+
+    if any(w in msg_lower for w in ["arima", "forecast", "predict"]):
+        if "compar" in msg_lower or "ets" in msg_lower or "accuracy" in msg_lower:
+            if "forecast_compare.png" in idx.get("r", {}).get("figures", []):
+                return (
+                    "Here is the ARIMA+Fourier vs ETS forecast comparison from the R analysis.",
+                    {"show": "figure", "scope": "r", "filename": "forecast_compare.png"},
+                )
+        return (
+            f"Here are the ARIMA forecasts for sampled titles from the Python analysis. {kpi_text}",
+            {"show": "figure", "scope": "python", "filename": "arima_forecasts_sampled_titles.png"},
+        )
+
+    if any(w in msg_lower for w in ["regression", "lm", "coefficient", "price effect", "rating effect"]):
+        return (
+            "The R notebook focuses on forecasting rather than regression. "
+            "Here is the forecast accuracy comparison instead.",
+            {"show": "table", "scope": "r", "filename": "accuracy_table.csv"},
+        )
+
+    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 ["accuracy", "benchmark", "rmse", "mape"]):
+        return (
+            "Here is the forecast accuracy comparison (ARIMA+Fourier vs ETS) from the R analysis.",
+            {"show": "table", "scope": "r", "filename": "accuracy_table.csv"},
+        )
+
+    if any(w in msg_lower for w in ["r analysis", "r output", "r result"]):
+        if "forecast_compare.png" in idx.get("r", {}).get("figures", []):
+            return (
+                "Here is the main R output: forecast model comparison plot.",
+                {"show": "figure", "scope": "r", "filename": "forecast_compare.png"},
+            )
+
+    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, "
+            "forecast accuracy, or top sellers to see specific visualizations.",
+            {"show": "table", "scope": "python", "filename": "df_dashboard.csv"},
+        )
+
+    return (
+        f"I can show you various analyses. {kpi_text}\n\n"
+        "Try asking about: **sales trends**, **sentiment**, **ARIMA forecasts**, "
+        "**forecast accuracy**, **top sellers**, or **dashboard overview**.",
+        {"show": "none"},
+    )
+
+# =========================================================
+# CSS LOADER (robust injection via <style> tag)
+# =========================================================
+
+def load_css() -> str:
+    css_path = BASE_DIR / "style.css"
+    if css_path.exists():
+        return css_path.read_text(encoding="utf-8")
+    return ""
+
+# =========================================================
+# UI
+# =========================================================
+
+ensure_dirs()
+
+with gr.Blocks(title="RX12 Workshop App") as demo:
+    # ✅ Robust CSS injection
+    css_text = load_css()
+    if css_text.strip():
+        gr.HTML(f"<style>{css_text}</style>")
+
+    gr.Markdown(
+        "# RX12 - Intro to Python and R - Workshop App\n"
+        "*The app to integrate the three notebooks in to get a functioning blueprint of the group project's final product*",
+        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")
+                gr.Markdown("")
+            with gr.Column(scale=1):
+                btn_nb2 = gr.Button("Step 2a: Python Analysis", variant="secondary")
+                gr.Markdown("")
+            with gr.Column(scale=1):
+                btn_r = gr.Button("Step 2b: R Analysis", variant="secondary")
+                gr.Markdown("")
+
+        with gr.Row():
+            btn_all = gr.Button("Run All 3 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_r.click(run_r, outputs=[run_log])
+        btn_all.click(run_full_pipeline, outputs=[run_log])
+
+    # ===========================================================
+    # TAB 2 -- Results Gallery
+    # ===========================================================
+    with gr.Tab("Results Gallery"):
+        gr.Markdown(
+            "### All generated artifacts\n\n"
+            "After running the pipeline, click **Refresh** to load all figures and tables. "
+            "Figures are shown in the gallery; select a table from the dropdown to inspect it."
+        )
+
+        refresh_btn = gr.Button("Refresh Gallery", variant="primary")
+
+        gr.Markdown("#### Figures")
+        gallery = gr.Gallery(
+            label="All Figures (Python + R)",
+            columns=2,
+            height=480,
+            object_fit="contain",
+        )
+
+        gr.Markdown("#### Tables")
+        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_gallery,
+            outputs=[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'):
+        gr.Markdown(
+            "### Ask questions, get visualisations\n\n"
+            "Describe what you want to see and the AI will pick the right chart or table. "
+            + (
+                "*LLM is active.*"
+                if LLM_ENABLED
+                else "*No API key detected — using keyword matching. "
+                     "Set `HF_API_KEY` in Space secrets for full LLM support.*"
+            )
+        )
+
+        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 drives revenue? / Compare forecast models",
+                    lines=1,
+                )
+                gr.Examples(
+                    examples=[
+                        "Show me the sales trends",
+                        "What does the sentiment look like?",
+                        "Which titles sell the most?",
+                        "Show the forecast accuracy comparison",
+                        "Compare the ARIMA and ETS forecasts",
+                        "Give me a dashboard overview",
+                    ],
+                    inputs=user_input,
+                )
+
+            with gr.Column(scale=1):
+                ai_figure = gr.Image(label="Visualisation", height=350)
+                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(allowed_paths=[str(BASE_DIR)])