Update app.py

app.py

@@ -1,15 +1,37 @@
-from dash import Dash, html, dcc, Input, Output, dash_table
+from dash import Dash, html, dcc, dash_table
 import dash_bootstrap_components as dbc
 import pandas as pd
 import plotly.express as px
+from datasets import load_dataset
 
 # -----------------------------
-# Dados de exemplo
+# Carregar dataset do Hugging Face
 # -----------------------------
-df = pd.DataFrame({
-    "Categoria": ["A", "B", "C", "D", "E"],
-    "Vendas": [120, 200, 150, 80, 220],
-    "Lucro": [30, 70, 40, 20, 90]
+ds = load_dataset("nvidia/Nemotron-Image-Training-v3", "aokvqa_1")
+df = ds["train"].to_pandas()
+
+# Limitar para evitar dashboard pesado
+df_preview = df.head(20).copy()
+
+# Criar dataframe de resumo
+resumo_df = pd.DataFrame({
+    "Métrica": [
+        "Total de linhas",
+        "Total de colunas"
+    ],
+    "Valor": [
+        len(df),
+        len(df.columns)
+    ]
+})
+
+colunas_df = pd.DataFrame({
+    "Colunas": list(df.columns)
+})
+
+grafico_df = pd.DataFrame({
+    "Tipo": ["Registos"],
+    "Quantidade": [len(df)]
 })
 
 # -----------------------------
@@ -24,35 +46,20 @@ server = app.server
 app.layout = dbc.Container([
     dbc.Row([
         dbc.Col([
-            html.H1("Dashboard em Dash", className="text-center my-4"),
+            html.H1("Dashboard do Dataset Nemotron", className="text-center my-4"),
             html.P(
-                "Exemplo simples de dashboard com Dash + Bootstrap + Plotly.",
+                "Dataset carregado com Hugging Face datasets: nvidia/Nemotron-Image-Training-v3 / aokvqa_1",
                 className="text-center text-muted"
             )
         ])
     ]),
 
-    dbc.Row([
-        dbc.Col([
-            html.Label("Escolhe a métrica:", className="fw-bold"),
-            dcc.Dropdown(
-                id="coluna-dropdown",
-                options=[
-                    {"label": "Vendas", "value": "Vendas"},
-                    {"label": "Lucro", "value": "Lucro"}
-                ],
-                value="Vendas",
-                clearable=False
-            )
-        ], md=4)
-    ], className="mb-4"),
-
     dbc.Row([
         dbc.Col(
             dbc.Card(
                 dbc.CardBody([
-                    html.H5("Total", className="card-title"),
-                    html.H2(id="total-card", className="text-primary")
+                    html.H5("Total de registos", className="card-title"),
+                    html.H2(f"{len(df)}", className="text-primary")
                 ])
             ),
             md=4
@@ -60,8 +67,8 @@ app.layout = dbc.Container([
         dbc.Col(
             dbc.Card(
                 dbc.CardBody([
-                    html.H5("Média", className="card-title"),
-                    html.H2(id="media-card", className="text-success")
+                    html.H5("Total de colunas", className="card-title"),
+                    html.H2(f"{len(df.columns)}", className="text-success")
                 ])
             ),
             md=4
@@ -69,8 +76,8 @@ app.layout = dbc.Container([
         dbc.Col(
             dbc.Card(
                 dbc.CardBody([
-                    html.H5("Máximo", className="card-title"),
-                    html.H2(id="max-card", className="text-danger")
+                    html.H5("Subset", className="card-title"),
+                    html.H2("aokvqa_1", className="text-danger")
                 ])
             ),
             md=4
@@ -79,54 +86,52 @@ app.layout = dbc.Container([
 
     dbc.Row([
         dbc.Col([
-            dcc.Graph(id="grafico-barras")
-        ], md=8),
+            dcc.Graph(
+                figure=px.bar(
+                    grafico_df,
+                    x="Tipo",
+                    y="Quantidade",
+                    title="Quantidade de registos no dataset"
+                )
+            )
+        ], md=6),
+
+        dbc.Col([
+            html.H4("Colunas disponíveis"),
+            dash_table.DataTable(
+                columns=[{"name": i, "id": i} for i in colunas_df.columns],
+                data=colunas_df.to_dict("records"),
+                style_table={"overflowX": "auto", "height": "350px", "overflowY": "auto"},
+                style_cell={"textAlign": "left", "padding": "8px"},
+                style_header={"fontWeight": "bold", "backgroundColor": "#f8f9fa"},
+                page_size=10
+            )
+        ], md=6)
+    ], className="mb-4"),
 
+    dbc.Row([
         dbc.Col([
+            html.H4("Primeiras 20 linhas"),
             dash_table.DataTable(
-                id="tabela",
-                columns=[{"name": i, "id": i} for i in df.columns],
-                data=df.to_dict("records"),
+                columns=[{"name": i, "id": i} for i in df_preview.columns],
+                data=df_preview.astype(str).to_dict("records"),
                 style_table={"overflowX": "auto"},
                 style_cell={
-                    "textAlign": "center",
-                    "padding": "10px"
+                    "textAlign": "left",
+                    "padding": "8px",
+                    "maxWidth": "250px",
+                    "whiteSpace": "normal"
                 },
                 style_header={
-                    "backgroundColor": "#f8f9fa",
-                    "fontWeight": "bold"
+                    "fontWeight": "bold",
+                    "backgroundColor": "#f8f9fa"
                 },
-                page_size=5
+                page_size=20
             )
-        ], md=4)
+        ])
     ])
 ], fluid=True)
 
-# -----------------------------
-# Callback
-# -----------------------------
-@app.callback(
-    Output("grafico-barras", "figure"),
-    Output("total-card", "children"),
-    Output("media-card", "children"),
-    Output("max-card", "children"),
-    Input("coluna-dropdown", "value")
-)
-def atualizar_dashboard(coluna_escolhida):
-    fig = px.bar(
-        df,
-        x="Categoria",
-        y=coluna_escolhida,
-        color="Categoria",
-        title=f"{coluna_escolhida} por Categoria"
-    )
-
-    total = df[coluna_escolhida].sum()
-    media = round(df[coluna_escolhida].mean(), 2)
-    maximo = df[coluna_escolhida].max()
-
-    return fig, total, media, maximo
-
 # -----------------------------
 # Run app
 # -----------------------------