José Manuel Gómez
663f058c00
Streamlit dashboard con pestañas Proyectos/Tareas/Clientes/Áreas leyendo directamente de la base de Airtable de gestión de proyectos. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
134 lines
4.9 KiB
Python
134 lines
4.9 KiB
Python
"""Dashboard interactivo para pm-dashboard — Streamlit."""
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import streamlit as st
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import pandas as pd
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import sys
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import os
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sys.path.insert(0, os.path.dirname(__file__))
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from airtable_client import AirtableClient
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st.set_page_config(
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page_title="PM Dashboard",
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layout="wide",
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initial_sidebar_state="expanded",
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)
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@st.cache_data(ttl=300, show_spinner="Cargando datos de Airtable...")
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def _load_data():
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at = AirtableClient()
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return {
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"projects": at.get_projects(),
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"tasks": at.get_tasks(),
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"clients": at.get_clients(),
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"areas": at.get_areas(),
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}
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data = _load_data()
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client_name_by_id = {c["id"]: (c["display_name"] or c["company_name"]) for c in data["clients"]}
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project_name_by_id = {p["id"]: p["project"] for p in data["projects"]}
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area_name_by_id = {a["id"]: a["area"] for a in data["areas"]}
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st.title("📊 PM Dashboard")
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tab_proyectos, tab_tareas, tab_clientes, tab_areas = st.tabs(
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["📁 Proyectos", "✅ Tareas", "👥 Clientes", "🗂️ Áreas"]
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)
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# ── Proyectos ─────────────────────────────────────────────────────────────── #
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with tab_proyectos:
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df = pd.DataFrame(data["projects"])
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if df.empty:
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st.info("No hay proyectos.")
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else:
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df["client"] = df["client_ids"].apply(
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lambda ids: ", ".join(client_name_by_id.get(i, "") for i in ids)
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)
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col1, col2 = st.columns(2)
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status_filter = col1.multiselect("Status", sorted(df["status"].dropna().unique()))
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client_filter = col2.multiselect("Cliente", sorted(client_name_by_id.values()))
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view = df.copy()
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if status_filter:
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view = view[view["status"].isin(status_filter)]
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if client_filter:
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view = view[view["client"].apply(lambda c: any(cf in c for cf in client_filter))]
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st.dataframe(
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view[["project", "status", "client", "project_lead", "kickoff_date", "due_date", "budget", "internal"]],
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use_container_width=True,
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hide_index=True,
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)
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st.caption(f"{len(view)} de {len(df)} proyectos")
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# ── Tareas ────────────────────────────────────────────────────────────────── #
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with tab_tareas:
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df = pd.DataFrame(data["tasks"])
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if df.empty:
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st.info("No hay tareas.")
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else:
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df["project"] = df["project_ids"].apply(
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lambda ids: ", ".join(project_name_by_id.get(i, "") for i in ids)
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)
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df["area"] = df["area_ids"].apply(
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lambda ids: ", ".join(area_name_by_id.get(i, "") for i in ids)
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)
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df["assigned"] = df["assigned_to"].apply(lambda names: ", ".join(names))
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col1, col2, col3 = st.columns(3)
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status_filter = col1.multiselect("Status", sorted(df["status"].dropna().unique()))
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prioridad_filter = col2.multiselect("Prioridad", sorted(df["prioridad"].dropna().unique()))
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assigned_filter = col3.multiselect(
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"Asignado a", sorted({n for names in df["assigned_to"] for n in names})
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)
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view = df.copy()
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if status_filter:
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view = view[view["status"].isin(status_filter)]
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if prioridad_filter:
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view = view[view["prioridad"].isin(prioridad_filter)]
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if assigned_filter:
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view = view[view["assigned"].apply(lambda a: any(af in a for af in assigned_filter))]
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st.dataframe(
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view[["task", "project", "area", "status", "prioridad", "assigned", "due_date", "horas_estimadas"]],
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use_container_width=True,
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hide_index=True,
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)
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st.caption(f"{len(view)} de {len(df)} tareas")
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# ── Clientes ──────────────────────────────────────────────────────────────── #
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with tab_clientes:
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df = pd.DataFrame(data["clients"])
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if df.empty:
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st.info("No hay clientes.")
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else:
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df["n_proyectos"] = df["project_ids"].apply(len)
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df["n_tareas"] = df["task_ids"].apply(len)
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st.dataframe(
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df[["display_name", "company_name", "n_proyectos", "n_tareas"]],
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use_container_width=True,
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hide_index=True,
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)
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# ── Áreas ─────────────────────────────────────────────────────────────────── #
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with tab_areas:
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df = pd.DataFrame(data["areas"])
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if df.empty:
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st.info("No hay áreas.")
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else:
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df["n_tareas"] = df["task_ids"].apply(len)
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st.dataframe(
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df[["area", "descripcion", "n_tareas"]],
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use_container_width=True,
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hide_index=True,
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)
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