import os
import re
import json
import time
import traceback
from pathlib import Path
from typing import Dict, Any, List, Tuple
import pandas as pd
import gradio as gr
import papermill as pm
import plotly.graph_objects as go
# Optional LLM (HuggingFace Inference API)
try:
from huggingface_hub import InferenceClient
except Exception:
InferenceClient = None
# =========================================================
# CONFIG
# =========================================================
BASE_DIR = Path(__file__).resolve().parent
NB1 = os.environ.get("NB1", "datacreation.ipynb").strip()
NB2 = os.environ.get("NB2", "pythonanalysis.ipynb").strip()
RUNS_DIR = BASE_DIR / "runs"
ART_DIR = BASE_DIR / "artifacts"
PY_FIG_DIR = ART_DIR / "py" / "figures"
PY_TAB_DIR = ART_DIR / "py" / "tables"
PAPERMILL_TIMEOUT = int(os.environ.get("PAPERMILL_TIMEOUT", "1800"))
MAX_PREVIEW_ROWS = int(os.environ.get("MAX_FILE_PREVIEW_ROWS", "50"))
MAX_LOG_CHARS = int(os.environ.get("MAX_LOG_CHARS", "8000"))
HF_API_KEY = os.environ.get("HF_API_KEY", "").strip()
MODEL_NAME = os.environ.get("MODEL_NAME", "deepseek-ai/DeepSeek-R1").strip()
HF_PROVIDER = os.environ.get("HF_PROVIDER", "novita").strip()
N8N_WEBHOOK_URL = os.environ.get("N8N_WEBHOOK_URL", "").strip()
LLM_ENABLED = bool(HF_API_KEY) and InferenceClient is not None
llm_client = (
InferenceClient(provider=HF_PROVIDER, api_key=HF_API_KEY)
if LLM_ENABLED else None
)
# =========================================================
# HELPERS
# =========================================================
def ensure_dirs():
for p in [RUNS_DIR, ART_DIR, PY_FIG_DIR, PY_TAB_DIR]:
p.mkdir(parents=True, exist_ok=True)
def stamp():
return time.strftime("%Y%m%d-%H%M%S")
def tail(text: str, n: int = MAX_LOG_CHARS) -> str:
return (text or "")[-n:]
def _ls(dir_path: Path, exts: Tuple[str, ...]) -> List[str]:
if not dir_path.is_dir():
return []
return sorted(p.name for p in dir_path.iterdir()
if p.is_file() and p.suffix.lower() in exts)
def _read_csv(path: Path) -> pd.DataFrame:
return pd.read_csv(path, nrows=MAX_PREVIEW_ROWS)
def _read_json(path: Path):
with path.open(encoding="utf-8") as f:
return json.load(f)
def artifacts_index() -> Dict[str, Any]:
return {
"python": {
"figures": _ls(PY_FIG_DIR, (".png", ".jpg", ".jpeg")),
"tables": _ls(PY_TAB_DIR, (".csv", ".json")),
},
}
# =========================================================
# PIPELINE RUNNERS
# =========================================================
def run_notebook(nb_name: str) -> str:
ensure_dirs()
nb_in = BASE_DIR / nb_name
if not nb_in.exists():
return f"ERROR: {nb_name} not found."
nb_out = RUNS_DIR / f"run_{stamp()}_{nb_name}"
pm.execute_notebook(
input_path=str(nb_in),
output_path=str(nb_out),
cwd=str(BASE_DIR),
log_output=True,
progress_bar=False,
request_save_on_cell_execute=True,
execution_timeout=PAPERMILL_TIMEOUT,
)
return f"Executed {nb_name}"
def run_datacreation() -> str:
try:
log = run_notebook(NB1)
csvs = [f.name for f in BASE_DIR.glob("*.csv")]
return f"OK {log}\n\nCSVs now in /app:\n" + "\n".join(f" - {c}" for c in sorted(csvs))
except Exception as e:
return f"FAILED {e}\n\n{traceback.format_exc()[-2000:]}"
def run_pythonanalysis() -> str:
try:
log = run_notebook(NB2)
idx = artifacts_index()
figs = idx["python"]["figures"]
tabs = idx["python"]["tables"]
return (
f"OK {log}\n\n"
f"Figures: {', '.join(figs) or '(none)'}\n"
f"Tables: {', '.join(tabs) or '(none)'}"
)
except Exception as e:
return f"FAILED {e}\n\n{traceback.format_exc()[-2000:]}"
def run_full_pipeline() -> str:
logs = []
logs.append("=" * 50)
logs.append("STEP 1/2: Data Creation (real data + synthetic enrichment)")
logs.append("=" * 50)
logs.append(run_datacreation())
logs.append("")
logs.append("=" * 50)
logs.append("STEP 2/2: Python Analysis (sentiment, dashboard, decisions)")
logs.append("=" * 50)
logs.append(run_pythonanalysis())
return "\n".join(logs)
# =========================================================
# GALLERY LOADERS
# =========================================================
def _load_all_figures() -> List[Tuple[str, str]]:
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():
figures = _load_all_figures()
idx = artifacts_index()
table_choices = list(idx["python"]["tables"])
default_df = pd.DataFrame()
if table_choices:
default_df = _load_table_safe(PY_TAB_DIR / table_choices[0])
return (
figures if figures else [],
gr.update(choices=table_choices,
value=table_choices[0] if table_choices else None),
default_df,
)
def on_table_select(choice: str):
if not choice:
return pd.DataFrame([{"hint": "Select a table above."}])
path = PY_TAB_DIR / choice
if not path.exists():
return pd.DataFrame([{"error": f"File not found: {choice}"}])
return _load_table_safe(path)
# =========================================================
# KPI LOADER
# =========================================================
def load_kpis() -> Dict[str, Any]:
# Check both the tables folder and the root directory
for candidate in [
PY_TAB_DIR / "kpis.json",
BASE_DIR / "kpis.json",
]:
if candidate.exists():
try:
return _read_json(candidate)
except Exception:
pass
return {}
# =========================================================
# KPI CARDS
# =========================================================
def render_kpi_cards() -> str:
kpis = load_kpis()
if not kpis:
return (
''
'
๐
'
'
No KPI data yet
'
'
Run the pipeline or upload kpis.json to populate these cards.
'
'
'
f'
{icon}
'
f'
{label}
'
f'
{value}
'
f'
'
)
shown = set()
for key, icon, label, colour in kpi_config:
val = kpis.get(key)
if val is None:
continue
shown.add(key)
if isinstance(val, float):
display_val = f"{val:.2f}"
elif isinstance(val, int) and val > 999:
display_val = f"{val:,}"
else:
display_val = str(val)
html += card(icon, label, display_val, colour)
# Any extra keys not in config
for key, val in kpis.items():
if key not in shown:
label = key.replace("_", " ").title()
display_val = f"{val:,.0f}" if isinstance(val, (int, float)) and val > 100 else str(val)
html += card("๐", label, display_val, "#8fa8f8")
html += "
"
return html
# =========================================================
# INTERACTIVE PLOTLY CHARTS โ Food Reviews
# =========================================================
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:
"""Rating & Sentiment overview โ reads df_dashboard.csv."""
# Try both locations: artifacts/py/tables/ and root
for candidate in [PY_TAB_DIR / "df_dashboard.csv", BASE_DIR / "df_dashboard.csv"]:
if candidate.exists():
path = candidate
break
else:
return _empty_chart("Rating & Sentiment Overview โ run the pipeline first")
try:
df = pd.read_csv(path)
except Exception as e:
return _empty_chart(f"Error reading df_dashboard.csv: {e}")
if "sentiment_label" not in df.columns:
return _empty_chart("sentiment_label column not found in df_dashboard.csv")
fig = go.Figure()
# Bar: number of reviews per sentiment
if "n_reviews" in df.columns:
colors = []
for s in df["sentiment_label"]:
sl = str(s).lower()
if sl == "positive": colors.append("#2ec4a0")
elif sl == "negative": colors.append("#e8537a")
else: colors.append("#5e8fef")
fig.add_trace(go.Bar(
x=df["sentiment_label"],
y=df["n_reviews"],
name="Number of Reviews",
marker_color=colors,
hovertemplate="%{x}
Reviews: %{y}",
))
# Line: avg rating per sentiment on secondary axis
if "avg_rating" in df.columns:
fig.add_trace(go.Scatter(
x=df["sentiment_label"],
y=df["avg_rating"],
name="Avg Rating",
mode="lines+markers",
line=dict(color="#7c5cbf", width=3),
marker=dict(size=10),
yaxis="y2",
hovertemplate="%{x}
Avg Rating: %{y:.2f}โญ",
))
fig.update_layout(
**_styled_layout(
height=420,
title=dict(text="Reviews & Avg Rating by Sentiment"),
yaxis=dict(title="Number of Reviews"),
yaxis2=dict(
title="Avg Star Rating",
overlaying="y", side="right",
range=[0, 5.5], showgrid=False,
),
barmode="group",
)
)
return fig
def build_sentiment_chart() -> go.Figure:
"""Sentiment pie chart โ reads df_dashboard.csv."""
for candidate in [PY_TAB_DIR / "df_dashboard.csv", BASE_DIR / "df_dashboard.csv"]:
if candidate.exists():
path = candidate
break
else:
return _empty_chart("Sentiment Distribution โ run the pipeline first")
try:
df = pd.read_csv(path)
except Exception as e:
return _empty_chart(f"Error reading df_dashboard.csv: {e}")
if "sentiment_label" not in df.columns:
return _empty_chart("sentiment_label column not found in df_dashboard.csv")
color_map = {
"positive": "#2ec4a0",
"neutral": "#5e8fef",
"negative": "#e8537a",
}
colors = [
color_map.get(str(s).lower(), "#888")
for s in df["sentiment_label"]
]
metric_col = (
"n_reviews" if "n_reviews" in df.columns
else df.select_dtypes("number").columns[0]
)
fig = go.Figure(go.Pie(
labels=df["sentiment_label"],
values=df[metric_col],
marker=dict(colors=colors, line=dict(color="white", width=2)),
textinfo="label+percent",
hovertemplate="%{label}
Reviews: %{value}
Share: %{percent}",
hole=0.35,
))
fig.update_layout(
**_styled_layout(
height=420,
title=dict(text="Sentiment Distribution"),
)
)
return fig
def build_top_sellers_chart() -> go.Figure:
"""Top products bar chart โ reads product_performance.csv."""
for candidate in [PY_TAB_DIR / "product_performance.csv", BASE_DIR / "product_performance.csv"]:
if candidate.exists():
path = candidate
break
else:
return _empty_chart("Top Products โ run the pipeline first")
try:
df = pd.read_csv(path)
except Exception as e:
return _empty_chart(f"Error reading product_performance.csv: {e}")
# Find name column and rating column
name_col = next(
(c for c in df.columns if "name" in c.lower() or "product" in c.lower()),
df.columns[0],
)
val_col = next(
(c for c in df.columns if "rating" in c.lower()),
df.select_dtypes("number").columns[0]
if len(df.select_dtypes("number").columns) > 0
else df.columns[1],
)
df = df.dropna(subset=[name_col, val_col])
df = df.sort_values(val_col, ascending=True).tail(10)
# Color by positive_ratio if available, else fixed palette
if "positive_ratio" in df.columns:
bar_colors = [
f"rgba({int(46 + x*150)},{int(196 - x*50)},{int(160 + x*30)},0.85)"
for x in df["positive_ratio"].fillna(0.5)
]
else:
bar_colors = CHART_PALETTE[: len(df)]
hover = (
"%{y}
"
+ val_col.replace("_", " ").title()
+ ": %{x:.2f}"
)
if "n_reviews" in df.columns:
hover = (
"%{y}
"
+ val_col.replace("_", " ").title()
+ ": %{x:.2f}
Reviews: "
+ df["n_reviews"].astype(str)
+ ""
)
hover = "%{y}
Avg Rating: %{x:.2f}"
fig = go.Figure(go.Bar(
y=df[name_col],
x=df[val_col],
orientation="h",
marker_color=bar_colors,
hovertemplate=hover,
))
fig.update_layout(
**_styled_layout(
height=max(380, len(df) * 50),
title=dict(text="Products Ranked by Average Rating"),
showlegend=False,
)
)
fig.update_xaxes(title="Average Star Rating", range=[0, 5.5])
fig.update_yaxes(autorange="reversed")
return fig
def refresh_dashboard():
return (
render_kpi_cards(),
build_sales_chart(),
build_sentiment_chart(),
build_top_sellers_chart(),
)
# =========================================================
# AI DASHBOARD
# =========================================================
DASHBOARD_SYSTEM = """You are an AI dashboard assistant for a food e-commerce analytics app.
The user asks questions about Amazon food product reviews analysed with sentiment analysis.
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:
{{"show": "figure"|"table"|"none", "scope": "python", "filename": "...", "chart": "sales"|"sentiment"|"top_sellers"|""}}
RULES:
- sentiment / reviews / positive / negative โ chart: "sentiment"
- rating / score / overview / trend โ chart: "sales"
- top / best / product / popular / rank โ chart: "top_sellers"
- churn / risk / decision / pricing โ show table: "business_decisions.csv"
- dashboard / summary / kpi โ show table: "df_dashboard.csv"
- pain points / complaints / negative reviews โ show table: "top_negative_reviews.csv"
Keep answers 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:
return JSON_BLOCK_RE.sub("", text).strip()
def _n8n_call(msg: str) -> Tuple[str, Dict]:
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 _keyword_fallback(msg: str, idx: Dict, kpis: Dict) -> Tuple[str, Dict]:
"""Keyword matcher for food review data."""
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"},
)
# Build a short KPI summary string
kpi_text = ""
if kpis:
parts = []
if "total_reviews" in kpis: parts.append(f"**{kpis['total_reviews']:,}** total reviews")
if "unique_products" in kpis: parts.append(f"**{kpis['unique_products']}** unique products")
if "avg_rating" in kpis: parts.append(f"avg rating **{kpis['avg_rating']}โญ**")
if "pct_positive" in kpis: parts.append(f"**{kpis['pct_positive']}%** positive reviews")
if parts:
kpi_text = "Quick summary: " + ", ".join(parts) + "."
if any(w in msg_lower for w in ["sentiment", "positive", "negative", "distribution", "review"]):
return (
f"Here is the sentiment distribution across food reviews. {kpi_text}",
{"show": "figure", "chart": "sentiment"},
)
if any(w in msg_lower for w in ["top", "best", "product", "popular", "rank", "seller"]):
return (
f"Here are the top products ranked by average rating. {kpi_text}",
{"show": "figure", "chart": "top_sellers"},
)
if any(w in msg_lower for w in ["rating", "score", "star", "overview", "trend", "monthly"]):
return (
f"Here is the rating and sentiment overview. {kpi_text}",
{"show": "figure", "chart": "sales"},
)
if any(w in msg_lower for w in ["churn", "risk", "decision", "pricing", "action"]):
return (
f"Here are the business decisions per product. {kpi_text}",
{"show": "table", "scope": "python", "filename": "business_decisions.csv"},
)
if any(w in msg_lower for w in ["pain", "complaint", "problem", "issue", "worst"]):
return (
f"Here are the most helpful negative reviews. {kpi_text}",
{"show": "table", "scope": "python", "filename": "top_negative_reviews.csv"},
)
if any(w in msg_lower for w in ["dashboard", "summary", "kpi", "overview", "data"]):
return (
f"Dashboard overview. {kpi_text}\n\n"
"Ask me about: **sentiment distribution**, **product ratings**, "
"**top products**, **churn risk**, or **business decisions**.",
{"show": "table", "scope": "python", "filename": "df_dashboard.csv"},
)
# Default
return (
f"I can help you explore the food review data. {kpi_text}\n\n"
"Try asking about: **sentiment distribution**, **top products**, "
"**product ratings**, **churn risk**, or **business decisions**.",
{"show": "figure", "chart": "sentiment"},
)
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()
# Priority: n8n webhook โ HF LLM โ 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 โ run the pipeline first)"),
)
msgs = [{"role": "system", "content": system}]
for entry in (history or [])[-6:]:
msgs.append(entry)
msgs.append({"role": "user", "content": user_msg})
try:
r = llm_client.chat_completion(
model=MODEL_NAME, messages=msgs,
temperature=0.3, max_tokens=600, stream=False,
)
raw = (
r["choices"][0]["message"]["content"]
if isinstance(r, dict)
else r.choices[0].message.content
)
directive = _parse_display_directive(raw)
reply = _clean_response(raw)
except Exception as e:
reply = f"LLM error: {e}. Falling back to keyword matching."
reply_fb, directive = _keyword_fallback(user_msg, idx, kpis)
reply += "\n\n" + reply_fb
# Resolve directive โ chart or table
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 "sentiment" in fname:
chart_out = build_sentiment_chart()
elif "product" in fname or "seller" in fname or "top" in fname:
chart_out = build_top_sellers_chart()
else:
chart_out = build_sales_chart()
if show == "table" and fname:
# Try tables folder first, then root
for fp in [PY_TAB_DIR / fname, BASE_DIR / fname]:
if fp.exists():
tab_out = _load_table_safe(fp)
break
if tab_out is None:
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
# =========================================================
# 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"
"*E-Commerce Food Review Intelligence Dashboard*",
elem_id="escp_title",
)
# โโ TAB 1 โ Pipeline Runner โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
with gr.Tab("Pipeline Runner"):
gr.Markdown(
"Run the notebooks to generate data and analysis artifacts. "
"If you have already uploaded the CSV files, you can skip Step 1 "
"and go straight to the Dashboard tab."
)
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])
# โโ TAB 2 โ Dashboard โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
with gr.Tab("Dashboard"):
kpi_html = gr.HTML(value=render_kpi_cards)
refresh_btn = gr.Button("๐ Refresh Dashboard", variant="primary")
gr.Markdown("#### Interactive Charts")
chart_sales = gr.Plot(label="Rating & Sentiment Overview")
chart_sentiment = gr.Plot(label="Sentiment Distribution")
chart_top = gr.Plot(label="Products by Avg Rating")
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],
)
# โโ TAB 3 โ AI Dashboard โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
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**. Set `N8N_WEBHOOK_URL` to connect "
"your n8n workflow, or set `HF_API_KEY` for direct LLM access."
)
gr.Markdown(
"### Ask questions about your food review data\n\n"
f"Type a question and the system picks the right 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 sentiment distribution / "
"Which products have the best ratings? / "
"What are the main customer complaints?"
),
lines=1,
)
gr.Examples(
examples=[
"Show me the sentiment distribution",
"Which products have the best ratings?",
"What are the top products?",
"Show the business decisions",
"What do negative reviews say?",
"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)])