Update app.py

app.py

@@ -33,18 +33,6 @@ 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
@@ -53,21 +41,11 @@ 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 _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": {
@@ -87,9 +65,8 @@ 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}"
+        return f"ERROR: {nb_name} non trovato."
+    nb_out = RUNS_DIR / f"run_{time.strftime('%Y%m%d-%H%M%S')}_{nb_name}"
     pm.execute_notebook(
         input_path=str(nb_in),
         output_path=str(nb_out),
@@ -98,37 +75,19 @@ def run_notebook(nb_name: str) -> str:
         progress_bar=False,
         execution_timeout=PAPERMILL_TIMEOUT,
     )
-    return f"Executed {nb_name}"
-
-def run_datacreation():
-    try:
-        log = run_notebook(NB1)
-        return f"OK - {log}"
-    except Exception as e:
-        return f"FAILED: {str(e)}"
-
-def run_pythonanalysis():
-    try:
-        log = run_notebook(NB2)
-        return f"OK - {log}"
-    except Exception as e:
-        return f"FAILED: {str(e)}"
+    return f"Eseguito {nb_name}"
 
-def run_r():
+def run_full_pipeline():
     try:
-        log = run_notebook(NB3)
-        return f"OK - {log}"
+        r1 = run_notebook(NB1)
+        r2 = run_notebook(NB2)
+        r3 = run_notebook(NB3)
+        return f"Pipeline Completata!\n1. {r1}\n2. {r2}\n3. {r3}"
     except Exception as e:
-        return f"FAILED: {str(e)}"
-
-def run_full_pipeline():
-    res1 = run_datacreation()
-    res2 = run_pythonanalysis()
-    res3 = run_r()
-    return f"Step 1: {res1}\nStep 2: {res2}\nStep 3: {res3}"
+        return f"ERRORE: {str(e)}"
 
 # =========================================================
-# GALLERY & AI LOGIC
+# UI LOGIC
 # =========================================================
 def refresh_gallery():
     idx = artifacts_index()
@@ -138,11 +97,7 @@ def refresh_gallery():
     for p in sorted(R_FIG_DIR.glob("*.png")):
         figs.append((str(p), f"R | {p.stem}"))
     
-    table_choices = []
-    for s in ["python", "r"]:
-        for t in idx[s]["tables"]:
-            table_choices.append(f"{s}/{t}")
-    
+    table_choices = [f"python/{t}" for t in idx["python"]["tables"]] + [f"r/{t}" for t in idx["r"]["tables"]]
     return figs, gr.update(choices=table_choices), pd.DataFrame()
 
 def on_table_select(choice):
@@ -150,76 +105,64 @@ def on_table_select(choice):
     scope, name = choice.split("/", 1)
     path = (PY_TAB_DIR if scope == "python" else R_TAB_DIR) / name
     try:
-        return pd.read_csv(path) if path.suffix == ".csv" else pd.DataFrame([json.load(open(path))])
+        return pd.read_csv(path).head(MAX_PREVIEW_ROWS) if path.suffix == ".csv" else pd.DataFrame([json.load(open(path))])
     except:
-        return pd.DataFrame({"Error": ["File non caricabile"]})
+        return pd.DataFrame({"Stato": ["Dati non ancora disponibili"]})
 
 def ai_chat(user_msg, history):
-    # Fallback semplice per demo
-    reply = "Ho ricevuto la tua richiesta."
-    directive = {"show": "none"}
-    
-    msg_l = user_msg.lower()
-    if "trend" in msg_l:
-        reply = "Ecco i trend di vendita."
-        directive = {"show": "figure", "scope": "python", "filename": "sales_trends_sampled_titles.png"}
-    elif "sentiment" in msg_l:
-        reply = "Analisi del sentiment completata."
-        directive = {"show": "figure", "scope": "python", "filename": "sentiment_distribution_sampled_titles.png"}
-
-    fig_out, tab_out = None, None
-    if directive["show"] == "figure":
-        base = PY_FIG_DIR if directive["scope"] == "python" else R_FIG_DIR
-        if (base / directive["filename"]).exists():
-            fig_out = str(base / directive["filename"])
-
-    # Aggiorna la cronologia nel formato standard (lista di tuple)
+    # Logica di risposta semplificata per compatibilità
+    reply = "Ho analizzato la tua richiesta sui dati."
+    fig_out = None
+    if "trend" in user_msg.lower():
+        target = PY_FIG_DIR / "sales_trends_sampled_titles.png"
+        if target.exists(): fig_out = str(target)
+
     history = history or []
     history.append((user_msg, reply))
-    
-    return history, "", fig_out, tab_out
+    return history, "", fig_out
 
 # =========================================================
-# UI
+# GRADIO INTERFACE
 # =========================================================
 ensure_dirs()
 
-with gr.Blocks() as demo:
-    gr.Markdown("# RX12 Workshop App")
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    # BANNER SUPERIORE
+    gr.Image("background_top.png", show_label=False, container=False, interactive=False)
     
-    with gr.Tab("Pipeline Runner"):
-        log_box = gr.Textbox(label="Log", lines=10)
+    gr.Markdown("# 🚀 RX12 - Dashboard Integrata Python & R")
+
+    with gr.Tab("1. Esecuzione Analisi"):
+        gr.Markdown("Avvia la pipeline per generare report e grafici.")
+        log_box = gr.Textbox(label="Log di Sistema", lines=8)
+        run_btn = gr.Button("Lancia Pipeline Completa", variant="primary")
+        run_btn.click(run_full_pipeline, outputs=log_box)
+
+    # BANNER CENTRALE
+    gr.Image("background_mid.png", show_label=False, container=False, interactive=False)
+
+    with gr.Tab("2. Galleria Risultati"):
+        refresh_btn = gr.Button("🔄 Aggiorna Visualizzazioni")
+        gallery = gr.Gallery(label="Grafici Analitici", columns=2)
         with gr.Row():
-            btn1 = gr.Button("Step 1")
-            btn2 = gr.Button("Step 2a")
-            btn3 = gr.Button("Step 2b")
-        btn_all = gr.Button("Run All", variant="primary")
-        
-        btn1.click(run_datacreation, outputs=log_box)
-        btn2.click(run_pythonanalysis, outputs=log_box)
-        btn3.click(run_r, outputs=log_box)
-        btn_all.click(run_full_pipeline, outputs=log_box)
-
-    with gr.Tab("Gallery"):
-        refresh_btn = gr.Button("Refresh")
-        gallery = gr.Gallery(label="Output")
-        drop = gr.Dropdown(label="Tables", choices=[])
-        df_out = gr.Dataframe()
+            table_drop = gr.Dropdown(label="Seleziona Tabella", choices=[])
+            table_out = gr.Dataframe(label="Anteprima Dati")
         
-        refresh_btn.click(refresh_gallery, outputs=[gallery, drop, df_out])
-        drop.change(on_table_select, inputs=drop, outputs=df_out)
+        refresh_btn.click(refresh_gallery, outputs=[gallery, table_drop, table_out])
+        table_drop.change(on_table_select, inputs=table_drop, outputs=table_out)
 
-    with gr.Tab("AI Dashboard"):
+    with gr.Tab("3. AI Dashboard"):
         with gr.Row():
-            with gr.Column():
-                # Rimosso type="messages" per compatibilità
-                chatbot = gr.Chatbot(label="Chat") 
-                txt = gr.Textbox(show_label=False, placeholder="Chiedi ai tuoi dati...")
-            with gr.Column():
-                ai_fig = gr.Image()
-                ai_tab = gr.Dataframe()
+            with gr.Column(scale=1):
+                chatbot = gr.Chatbot(label="Assistente Virtuale") # type="messages" RIMOSSO
+                msg = gr.Textbox(label="Chiedi all'AI", placeholder="Es: Mostrami i trend...")
+            with gr.Column(scale=1):
+                ai_img = gr.Image(label="Grafico Suggerito")
         
-        txt.submit(ai_chat, [txt, chatbot], [chatbot, txt, ai_fig, ai_tab])
+        msg.submit(ai_chat, [msg, chatbot], [chatbot, msg, ai_img])
+
+    # BANNER INFERIORE
+    gr.Image("background_bottom.png", show_label=False, container=False, interactive=False)
 
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)