| import os |
| import re |
| import json |
| import time |
| import traceback |
| from pathlib import Path |
| from typing import Dict, Any, List, Tuple |
|
|
| import pandas as pd |
| import gradio as gr |
| import papermill as pm |
| import plotly.graph_objects as go |
|
|
| |
| try: |
| from huggingface_hub import InferenceClient |
| except Exception: |
| InferenceClient = None |
|
|
| |
| |
| |
|
|
| 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 |
| ) |
|
|
| |
| |
| |
|
|
| 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")), |
| }, |
| } |
|
|
| |
| |
| |
|
|
| 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) |
|
|
|
|
| |
| |
| |
|
|
| def _load_all_figures() -> List[Tuple[str, str]]: |
| """Return list of (filepath, caption) for Gallery.""" |
| items = [] |
| for p in sorted(PY_FIG_DIR.glob("*.png")): |
| items.append((str(p), p.stem.replace('_', ' ').title())) |
| return items |
|
|
|
|
| def _load_table_safe(path: Path) -> pd.DataFrame: |
| try: |
| if path.suffix == ".json": |
| obj = _read_json(path) |
| if isinstance(obj, dict): |
| return pd.DataFrame([obj]) |
| return pd.DataFrame(obj) |
| return _read_csv(path) |
| except Exception as e: |
| return pd.DataFrame([{"error": str(e)}]) |
|
|
|
|
| def refresh_gallery(): |
| """Called when user clicks Refresh on Gallery tab.""" |
| figures = _load_all_figures() |
| idx = artifacts_index() |
|
|
| table_choices = list(idx["python"]["tables"]) |
|
|
| default_df = pd.DataFrame() |
| if table_choices: |
| default_df = _load_table_safe(PY_TAB_DIR / table_choices[0]) |
|
|
| return ( |
| figures if figures else [], |
| gr.update(choices=table_choices, value=table_choices[0] if table_choices else None), |
| default_df, |
| ) |
|
|
|
|
| def on_table_select(choice: str): |
| if not choice: |
| return pd.DataFrame([{"hint": "Select a table above."}]) |
| path = PY_TAB_DIR / choice |
| if not path.exists(): |
| return pd.DataFrame([{"error": f"File not found: {choice}"}]) |
| return _load_table_safe(path) |
|
|
|
|
| |
| |
| |
|
|
| 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 {} |
|
|
|
|
| |
| |
| |
|
|
| DASHBOARD_SYSTEM = """You are an AI dashboard assistant for a book-sales analytics app. |
| The user asks questions or requests about their data. You have access to pre-computed |
| artifacts from a Python analysis pipeline. |
| |
| AVAILABLE ARTIFACTS (only reference ones that exist): |
| {artifacts_json} |
| |
| KPI SUMMARY: {kpis_json} |
| |
| YOUR JOB: |
| 1. Answer the user's question conversationally using the KPIs and your knowledge of the artifacts. |
| 2. At the END of your response, output a JSON block (fenced with ```json ... ```) that tells |
| the dashboard which artifact to display. The JSON must have this shape: |
| {{"show": "figure"|"table"|"none", "scope": "python", "filename": "..."}} |
| |
| - Use "show": "figure" to display a chart image. |
| - Use "show": "table" to display a CSV/JSON table. |
| - Use "show": "none" if no artifact is relevant. |
| |
| RULES: |
| - If the user asks about sales trends or forecasting by title, show sales_trends or arima figures. |
| - If the user asks about sentiment, show sentiment figure or sentiment_counts table. |
| - If the user asks about forecast accuracy or ARIMA, show arima figures. |
| - If the user asks about top sellers, show top_titles_by_units_sold.csv. |
| - If the user asks a general data question, pick the most relevant artifact. |
| - Keep your answer concise (2-4 sentences), then the JSON block. |
| """ |
|
|
| JSON_BLOCK_RE = re.compile(r"```json\s*(\{.*?\})\s*```", re.DOTALL) |
| FALLBACK_JSON_RE = re.compile(r"\{[^{}]*\"show\"[^{}]*\}", re.DOTALL) |
|
|
|
|
| def _parse_display_directive(text: str) -> Dict[str, str]: |
| m = JSON_BLOCK_RE.search(text) |
| if m: |
| try: |
| return json.loads(m.group(1)) |
| except json.JSONDecodeError: |
| pass |
| m = FALLBACK_JSON_RE.search(text) |
| if m: |
| try: |
| return json.loads(m.group(0)) |
| except json.JSONDecodeError: |
| pass |
| return {"show": "none"} |
|
|
|
|
| def _clean_response(text: str) -> str: |
| """Strip the JSON directive block from the displayed response.""" |
| return JSON_BLOCK_RE.sub("", text).strip() |
|
|
|
|
| def _n8n_call(msg: str) -> Tuple[str, Dict]: |
| """Call the student's n8n webhook and return (reply, directive).""" |
| import requests as req |
| try: |
| resp = req.post(N8N_WEBHOOK_URL, json={"question": msg}, timeout=20) |
| data = resp.json() |
| answer = data.get("answer", "No response from n8n workflow.") |
| chart = data.get("chart", "none") |
| if chart and chart != "none": |
| return answer, {"show": "figure", "chart": chart} |
| return answer, {"show": "none"} |
| except Exception as e: |
| return f"n8n error: {e}. Falling back to keyword matching.", None |
|
|
|
|
| def ai_chat(user_msg: str, history: list): |
| """Chat function for the AI Dashboard tab.""" |
| if not user_msg or not user_msg.strip(): |
| return history, "", None, None |
|
|
| idx = artifacts_index() |
| kpis = load_kpis() |
|
|
| |
| if N8N_WEBHOOK_URL: |
| reply, directive = _n8n_call(user_msg) |
| if directive is None: |
| reply_fb, directive = _keyword_fallback(user_msg, idx, kpis) |
| reply += "\n\n" + reply_fb |
| elif not LLM_ENABLED: |
| reply, directive = _keyword_fallback(user_msg, idx, kpis) |
| else: |
| system = DASHBOARD_SYSTEM.format( |
| artifacts_json=json.dumps(idx, indent=2), |
| kpis_json=json.dumps(kpis, indent=2) if kpis else "(no KPIs yet, run the pipeline first)", |
| ) |
| msgs = [{"role": "system", "content": system}] |
| for entry in (history or [])[-6:]: |
| msgs.append(entry) |
| msgs.append({"role": "user", "content": user_msg}) |
|
|
| try: |
| r = llm_client.chat_completion( |
| model=MODEL_NAME, |
| messages=msgs, |
| temperature=0.3, |
| max_tokens=600, |
| stream=False, |
| ) |
| raw = ( |
| r["choices"][0]["message"]["content"] |
| if isinstance(r, dict) |
| else r.choices[0].message.content |
| ) |
| directive = _parse_display_directive(raw) |
| reply = _clean_response(raw) |
| except Exception as e: |
| reply = f"LLM error: {e}. Falling back to keyword matching." |
| reply_fb, directive = _keyword_fallback(user_msg, idx, kpis) |
| reply += "\n\n" + reply_fb |
|
|
| |
| chart_out = None |
| tab_out = None |
| show = directive.get("show", "none") |
| fname = directive.get("filename", "") |
| chart_name = directive.get("chart", "") |
|
|
| |
| 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: |
| |
| 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() |
| 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"}, |
| ) |
|
|
| |
| 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"}, |
| ) |
|
|
|
|
| |
| |
| |
|
|
| 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) |
| |
| 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 |
|
|
|
|
| |
| |
| |
|
|
| CHART_PALETTE = ["#7c5cbf", "#2ec4a0", "#e8537a", "#e8a230", "#5e8fef", |
| "#c45ea8", "#3dbacc", "#a0522d", "#6aaa3a", "#d46060"] |
|
|
| def _styled_layout(**kwargs) -> dict: |
| defaults = dict( |
| template="plotly_white", |
| paper_bgcolor="rgba(255,255,255,0.95)", |
| plot_bgcolor="rgba(255,255,255,0.98)", |
| font=dict(family="system-ui, sans-serif", color="#2d1f4e", size=12), |
| margin=dict(l=60, r=20, t=70, b=70), |
| legend=dict( |
| orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1, |
| bgcolor="rgba(255,255,255,0.92)", |
| bordercolor="rgba(124,92,191,0.35)", borderwidth=1, |
| ), |
| title=dict(font=dict(size=15, color="#4b2d8a")), |
| ) |
| defaults.update(kwargs) |
| return defaults |
|
|
|
|
| def _empty_chart(title: str) -> go.Figure: |
| fig = go.Figure() |
| fig.update_layout( |
| title=title, height=420, template="plotly_white", |
| paper_bgcolor="rgba(255,255,255,0.95)", |
| annotations=[dict(text="Run the pipeline to generate data", |
| x=0.5, y=0.5, xref="paper", yref="paper", showarrow=False, |
| font=dict(size=14, color="rgba(124,92,191,0.5)"))], |
| ) |
| return fig |
|
|
|
|
| def build_sales_chart() -> go.Figure: |
| path = PY_TAB_DIR / "df_dashboard.csv" |
| if not path.exists(): |
| return _empty_chart("Sales Trends — run the pipeline first") |
| df = pd.read_csv(path) |
| date_col = next((c for c in df.columns if "month" in c.lower() or "date" in c.lower()), None) |
| val_cols = [c for c in df.columns if c != date_col and df[c].dtype in ("float64", "int64")] |
| if not date_col or not val_cols: |
| return _empty_chart("Could not auto-detect columns in df_dashboard.csv") |
| df[date_col] = pd.to_datetime(df[date_col], errors="coerce") |
| fig = go.Figure() |
| for i, col in enumerate(val_cols): |
| fig.add_trace(go.Scatter( |
| x=df[date_col], y=df[col], name=col.replace("_", " ").title(), |
| mode="lines+markers", line=dict(color=CHART_PALETTE[i % len(CHART_PALETTE)], width=2), |
| marker=dict(size=4), |
| hovertemplate=f"<b>{col.replace('_',' ').title()}</b><br>%{{x|%b %Y}}: %{{y:,.0f}}<extra></extra>", |
| )) |
| fig.update_layout(**_styled_layout(height=450, hovermode="x unified", |
| title=dict(text="Monthly Overview"))) |
| fig.update_xaxes(gridcolor="rgba(124,92,191,0.15)", showgrid=True) |
| fig.update_yaxes(gridcolor="rgba(124,92,191,0.15)", showgrid=True) |
| return fig |
|
|
|
|
| def build_sentiment_chart() -> go.Figure: |
| path = PY_TAB_DIR / "sentiment_counts_sampled.csv" |
| if not path.exists(): |
| return _empty_chart("Sentiment Distribution — run the pipeline first") |
| df = pd.read_csv(path) |
| title_col = df.columns[0] |
| sent_cols = [c for c in ["negative", "neutral", "positive"] if c in df.columns] |
| if not sent_cols: |
| return _empty_chart("No sentiment columns found in CSV") |
| colors = {"negative": "#e8537a", "neutral": "#5e8fef", "positive": "#2ec4a0"} |
| fig = go.Figure() |
| for col in sent_cols: |
| fig.add_trace(go.Bar( |
| name=col.title(), y=df[title_col], x=df[col], |
| orientation="h", marker_color=colors.get(col, "#888"), |
| hovertemplate=f"<b>{col.title()}</b>: %{{x}}<extra></extra>", |
| )) |
| fig.update_layout(**_styled_layout( |
| height=max(400, len(df) * 28), barmode="stack", |
| title=dict(text="Sentiment Distribution by Book"), |
| )) |
| fig.update_xaxes(title="Number of Reviews") |
| fig.update_yaxes(autorange="reversed") |
| return fig |
|
|
|
|
| def build_top_sellers_chart() -> go.Figure: |
| path = PY_TAB_DIR / "top_titles_by_units_sold.csv" |
| if not path.exists(): |
| return _empty_chart("Top Sellers — run the pipeline first") |
| df = pd.read_csv(path).head(15) |
| title_col = next((c for c in df.columns if "title" in c.lower()), df.columns[0]) |
| val_col = next((c for c in df.columns if "unit" in c.lower() or "sold" in c.lower()), df.columns[-1]) |
| fig = go.Figure(go.Bar( |
| y=df[title_col], x=df[val_col], orientation="h", |
| marker=dict(color=df[val_col], colorscale=[[0, "#c5b4f0"], [1, "#7c5cbf"]]), |
| hovertemplate="<b>%{y}</b><br>Units: %{x:,.0f}<extra></extra>", |
| )) |
| fig.update_layout(**_styled_layout( |
| height=max(400, len(df) * 30), |
| title=dict(text="Top Selling Titles"), showlegend=False, |
| )) |
| fig.update_yaxes(autorange="reversed") |
| fig.update_xaxes(title="Total Units Sold") |
| return fig |
|
|
|
|
| def refresh_dashboard(): |
| return render_kpi_cards(), build_sales_chart(), build_sentiment_chart(), build_top_sellers_chart() |
|
|
|
|
| |
| |
| |
|
|
| 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", |
| ) |
|
|
| |
| |
| |
| 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]) |
|
|
| |
| |
| |
| 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], |
| ) |
|
|
| |
| |
| |
| 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)]) |
|
|
