Update app.py

app.py

@@ -1,3 +1,6 @@
+
+Copier
+
 import os
 import re
 import json
@@ -5,74 +8,74 @@ 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": {
@@ -80,11 +83,11 @@ def artifacts_index() -> Dict[str, Any]:
             "tables": _ls(PY_TAB_DIR, (".csv", ".json")),
         },
     }
-
+ 
 # =========================================================
 # PIPELINE RUNNERS
 # =========================================================
-
+ 
 def run_notebook(nb_name: str) -> str:
     ensure_dirs()
     nb_in = BASE_DIR / nb_name
@@ -101,8 +104,8 @@ def run_notebook(nb_name: str) -> str:
         execution_timeout=PAPERMILL_TIMEOUT,
     )
     return f"Executed {nb_name}"
-
-
+ 
+ 
 def run_datacreation() -> str:
     try:
         log = run_notebook(NB1)
@@ -110,8 +113,8 @@ def run_datacreation() -> str:
         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)
@@ -125,8 +128,8 @@ def run_pythonanalysis() -> str:
         )
     except Exception as e:
         return f"FAILED  {e}\n\n{traceback.format_exc()[-2000:]}"
-
-
+ 
+ 
 def run_full_pipeline() -> str:
     logs = []
     logs.append("=" * 50)
@@ -139,20 +142,20 @@ def run_full_pipeline() -> str:
     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":
@@ -163,26 +166,26 @@ def _load_table_safe(path: Path) -> pd.DataFrame:
         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."}])
@@ -190,12 +193,12 @@ def on_table_select(choice: str):
     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():
@@ -204,31 +207,32 @@ def load_kpis() -> Dict[str, Any]:
             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.
-
+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}
-
+ 
+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.
-
+ 
+    - 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.
@@ -237,11 +241,11 @@ RULES:
 - 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:
@@ -256,13 +260,13 @@ def _parse_display_directive(text: str) -> Dict[str, str]:
         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
@@ -276,16 +280,16 @@ def _n8n_call(msg: str) -> Tuple[str, Dict]:
         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)
@@ -303,7 +307,7 @@ def ai_chat(user_msg: str, history: list):
         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,
@@ -323,60 +327,59 @@ def ai_chat(user_msg: str, history: list):
             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
+ 
+    # Resolve artifacts
     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
+            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})*"
-
+ 
+    # FIX Gradio 6: history format is list of dicts with role/content
     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)
@@ -384,57 +387,47 @@ def _keyword_fallback(msg: str, idx: Dict, kpis: Dict) -> Tuple[str, Dict]:
             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"},
-        )
-
+        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"},
-        )
-
+        return (f"Here is the sentiment distribution. {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"},
-        )
-
+        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}",
+            f"Here are the top-selling titles. {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.",
+            f"Dashboard overview: {kpi_text}\n\nAsk me about sales trends, sentiment, "
+            "forecasts, pricing, or top sellers.",
             {"show": "table", "scope": "python", "filename": "df_dashboard.csv"},
         )
-
-    # 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"},
     )
-
-
+ 
+ 
 # =========================================================
-# KPI CARDS (BubbleBusters style)
+# KPI CARDS
 # =========================================================
-
+ 
 def render_kpi_cards() -> str:
     kpis = load_kpis()
     if not kpis:
@@ -443,14 +436,12 @@ def render_kpi_cards() -> str:
             '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 style="font-size:36px;margin-bottom:10px;">&#128202;</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);
@@ -463,14 +454,14 @@ def render_kpi_cards() -> str:
                         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"),
+        ("n_titles",         "&#128218;", "Book Titles",  "#a48de8"),
+        ("n_months",         "&#128197;", "Time Periods", "#7aa6f8"),
+        ("total_units_sold", "&#128230;", "Units Sold",   "#6ee7c7"),
+        ("total_revenue",    "&#128176;", "Revenue",      "#3dcba8"),
     ]
-
+ 
     html = (
         '<div style="display:grid;grid-template-columns:repeat(auto-fit,minmax(140px,1fr));'
         'gap:12px;margin-bottom:24px;">'
@@ -482,25 +473,26 @@ def render_kpi_cards() -> str:
         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 += card("&#128200;", label, str(val), "#8fa8f8")
     html += "</div>"
     return html
-
-
+ 
+ 
 # =========================================================
-# INTERACTIVE PLOTLY CHARTS (BubbleBusters style)
+# INTERACTIVE PLOTLY CHARTS
 # =========================================================
-
+ 
 CHART_PALETTE = ["#7c5cbf", "#2ec4a0", "#e8537a", "#e8a230", "#5e8fef",
                  "#c45ea8", "#3dbacc", "#a0522d", "#6aaa3a", "#d46060"]
-
+ 
+ 
 def _styled_layout(**kwargs) -> dict:
     defaults = dict(
         template="plotly_white",
@@ -517,20 +509,22 @@ def _styled_layout(**kwargs) -> dict:
     )
     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",
+        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)"))],
+            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():
@@ -545,17 +539,18 @@ def build_sales_chart() -> 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),
+            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")))
+                                       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():
@@ -569,8 +564,8 @@ def build_sentiment_chart() -> go.Figure:
     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"),
+            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(
@@ -580,8 +575,8 @@ def build_sentiment_chart() -> go.Figure:
     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():
@@ -601,94 +596,104 @@ def build_top_sellers_chart() -> go.Figure:
     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:
-
+ 
+ 
+with gr.Blocks(title="AIBDM 2026 Workshop App", css=load_css()) as demo:
+ 
     gr.Markdown(
         "# SE21 App Template\n"
         "*This is an app template for SE21 students*",
         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")
             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)
+    # TAB 2 -- Dashboard
     # ===========================================================
     with gr.Tab("Dashboard"):
-        kpi_html = gr.HTML(value=render_kpi_cards)
-
+        kpi_html = gr.HTML(value=render_kpi_cards())   # FIX: call function directly for initial load
+ 
         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")
-
+        chart_sales = gr.Plot(label="Monthly Overview", value=build_sales_chart())           # FIX: initial load
+        chart_sentiment = gr.Plot(label="Sentiment Distribution", value=build_sentiment_chart())  # FIX: initial load
+        chart_top = gr.Plot(label="Top Sellers", value=build_top_sellers_chart())            # FIX: initial load
+ 
         gr.Markdown("#### Static Figures (from notebooks)")
         gallery = gr.Gallery(
             label="Generated Figures",
             columns=2,
             height=480,
             object_fit="contain",
+            value=_load_all_figures(),   # FIX: initial load
         )
-
+ 
         gr.Markdown("#### Data Tables")
+ 
+        # FIX: load initial table choices
+        _initial_idx = artifacts_index()
+        _initial_table_choices = list(_initial_idx["python"]["tables"])
+        _initial_df = pd.DataFrame()
+        if _initial_table_choices:
+            _initial_df = _load_table_safe(PY_TAB_DIR / _initial_table_choices[0])
+ 
         table_dropdown = gr.Dropdown(
             label="Select a table to view",
-            choices=[],
+            choices=_initial_table_choices,
+            value=_initial_table_choices[0] if _initial_table_choices else None,
             interactive=True,
         )
         table_display = gr.Dataframe(
             label="Table Preview",
             interactive=False,
+            value=_initial_df if not _initial_df.empty else None,
         )
-
+ 
         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,
@@ -699,7 +704,7 @@ with gr.Blocks(title="AIBDM 2026 Workshop App") as demo:
             inputs=[table_dropdown],
             outputs=[table_display],
         )
-
+ 
     # ===========================================================
     # TAB 3 -- AI Dashboard
     # ===========================================================
@@ -707,24 +712,24 @@ with gr.Blocks(title="AIBDM 2026 Workshop App") as demo:
         _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."
+            else "Using **keyword matching**. Set `N8N_WEBHOOK_URL` or `HF_API_KEY` to upgrade."
         )
         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}"
+            f"{_ai_status}"
         )
-
+ 
         with gr.Row(equal_height=True):
             with gr.Column(scale=1):
+                # FIX Gradio 6: type="messages" pour le format dict
                 chatbot = gr.Chatbot(
                     label="Conversation",
                     height=380,
+                    type="messages",
                 )
                 user_input = gr.Textbox(
                     label="Ask about your data",
-                    placeholder="e.g. Show me sales trends / What are the top sellers? / Sentiment analysis",
+                    placeholder="e.g. Show me sales trends / What are the top sellers?",
                     lines=1,
                 )
                 gr.Examples(
@@ -738,21 +743,15 @@ with gr.Blocks(title="AIBDM 2026 Workshop App") as demo:
                     ],
                     inputs=user_input,
                 )
-
+ 
             with gr.Column(scale=1):
-                ai_figure = gr.Plot(
-                    label="Interactive Chart",
-                )
-                ai_table = gr.Dataframe(
-                    label="Data Table",
-                    interactive=False,
-                )
-
+                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)])
+ 
+demo.launch(allowed_paths=[str(BASE_DIR)])