feat: completa US06, US07 e US08 do enunciado (tamanho descrição, cruzamento clareza, filtros)

src/pr_analyzer/ui/app.py

@@ -5,7 +5,7 @@
   1. Page configuration
   2. CSS injection
   3. Session-state bootstrap (including the raw_prs tuple from the pipeline)
-  4. Sidebar rendering -> filter values + LLM toggle
+  4. Sidebar rendering → filter values + LLM toggle
   5. Filtering the active DataFrame (and optionally enriching via dev3+dev4)
   6. Routing to the correct main-area view (empty state OR tabs)
 
@@ -18,12 +18,14 @@
 
 import streamlit as st
 
+# ── Page config (must be the very first Streamlit call) ───────────────────────
 st.set_page_config(
     page_title="GitAnalyzer",
     page_icon="🧬",
     layout="wide",
 )
 
+# ── Internal imports (after set_page_config) ──────────────────────────────────
 from components.sidebar import render_sidebar  # noqa: E402
 from components.tabs import (  # noqa: E402
     render_tab_dashboard,
@@ -33,15 +35,18 @@
 from utils.constants import APP_NAME, APP_VERSION  # noqa: E402
 from utils.data import apply_filters, get_mock_data  # noqa: E402
 from utils.pipeline_bridge import (  # noqa: E402
+    CacheCounter,
     enrich_prs,
     enriched_to_dataframe,
 )
 from utils.styles import inject_css  # noqa: E402
 
 from pr_analyzer.llm.client import create_llm_client  # noqa: E402
 
+# ── CSS ───────────────────────────────────────────────────────────────────────
 inject_css()
 
+# ── Session-state bootstrap ───────────────────────────────────────────────────
 if "file_loaded" not in st.session_state:
     st.session_state.file_loaded = False
 if "fname" not in st.session_state:
@@ -59,9 +64,13 @@
 if "llm_enriched" not in st.session_state:
     st.session_state.llm_enriched = False
 
-sel_lang, sel_nature, cleaning, llm_tag, metrics = render_sidebar()
+# ── Sidebar ───────────────────────────────────────────────────────────────────
+sel_lang, sel_nature, sel_type, sel_clarity, cleaning, llm_tag, metrics = (
+    render_sidebar()
+)
 
 
+# ── LLM enrichment (TASK-39) ──────────────────────────────────────────────────
 def _maybe_enrich() -> None:
     """Classifica PRs com o LLM configurado. Roda apenas uma vez por dataset carregado."""
     if not llm_tag:
@@ -93,6 +102,7 @@ def _maybe_enrich() -> None:
         st.error(f"Erro ao conectar ao {backend.upper()}: {exc}")
         return
 
+    counter = CacheCounter()
     enriched_list = []
 
     with st.status(
@@ -101,25 +111,35 @@ def _maybe_enrich() -> None:
     ) as status:
         st.caption(f"Modelo: `{model}`")
         bar = st.progress(0.0)
-        for i, ep in enumerate(enrich_prs(prs, client=client), 1):
+        for i, ep in enumerate(enrich_prs(prs, client=client, cache=counter), 1):
             enriched_list.append(ep)
             bar.progress(i / n)
         status.update(
-            label=f"✓ {n} PRs classificados com {backend.upper()}",
+            label=f"✓ {n} PRs classificados — {counter.cache_hits} do cache, {counter.calls_made} chamadas reais",
             state="complete",
             expanded=False,
         )
 
     st.session_state.df = enriched_to_dataframe(enriched_list)
-    st.session_state.llm_cache_stats = {"cache_hits": 0, "calls_made": n, "total": n}
+    st.session_state.llm_cache_stats = {
+        "cache_hits": counter.cache_hits,
+        "calls_made": counter.calls_made,
+        "total": counter.total,
+    }
     st.session_state.llm_enriched = True
 
 
 _maybe_enrich()
 
-df = apply_filters(st.session_state.df, sel_lang, sel_nature)
+# ── Filtered DataFrame (pure transform) ───────────────────────────────────────
+df = apply_filters(st.session_state.df, sel_lang, sel_nature, sel_type, sel_clarity)
+
+# ══════════════════════════════════════════════════════════════════════════════
+# MAIN AREA
+# ══════════════════════════════════════════════════════════════════════════════
 
 if not st.session_state.file_loaded:
+    # ── Empty / landing state ─────────────────────────────────────────────────
     st.markdown(
         """
         <div class="empty-state">
@@ -140,6 +160,7 @@ def _maybe_enrich() -> None:
             st.rerun()
 
 else:
+    # ── Main tabs ─────────────────────────────────────────────────────────────
     tab_dash, tab_explore, tab_export = st.tabs(["DASH", "EXPLORAR", "EXPORTAR"])
 
     with tab_dash:
@@ -151,6 +172,7 @@ def _maybe_enrich() -> None:
     with tab_export:
         render_tab_export(df)
 
+# ── Footer ────────────────────────────────────────────────────────────────────
 st.markdown(
     f"<br><p style='text-align:center;font-size:9px;color:#3f3f46;"
     f"font-weight:900;letter-spacing:2px;'>"

src/pr_analyzer/ui/components/charts.py

@@ -226,6 +226,116 @@ def render_clarity_gauge(df: pd.DataFrame) -> None:
     st.plotly_chart(fig, use_container_width=True, config=_chart_cfg())
 
 
+def render_body_size_chart(df: pd.DataFrame) -> None:
+    """Bar chart: avg description length (chars and words) grouped by a dimension.
+
+    US06 — visualizar distribuição de tamanho de descrição estratificada.
+    """
+    _panel_header(
+        "📝 Tamanho de Descrição por Dimensão",
+        "linear-gradient(180deg,#34d399,#059669)",
+    )
+
+    if "chars" not in df.columns or len(df) == 0:
+        st.info(_NO_DATA_MSG)
+        return
+
+    dim: str = st.selectbox(
+        "Agrupar por",
+        ["lang", "type", "nature"],
+        format_func=lambda x: {
+            "lang": "Linguagem",
+            "type": "Tipo",
+            "nature": "Natureza",
+        }[x],
+        key="body_size_dim",
+        label_visibility="collapsed",
+    )
+
+    if dim not in df.columns:
+        st.info(_NO_DATA_MSG)
+        return
+
+    agg = (
+        df[df[dim] != "—"]
+        .groupby(dim)[["chars", "words"]]
+        .mean()
+        .round(0)
+        .reset_index()
+        .sort_values("chars", ascending=False)
+    )
+
+    if agg.empty:
+        st.info(_NO_DATA_MSG)
+        return
+
+    fig = px.bar(
+        agg,
+        x=dim,
+        y=["chars", "words"],
+        barmode="group",
+        color_discrete_map={"chars": "#6366f1", "words": "#34d399"},
+        labels={dim: "", "value": "Média", "variable": "Métrica"},
+    )
+    fig.update_layout(
+        **PLOT_BASE,
+        legend={"orientation": "h", "y": 1.1, "x": 0},
+        yaxis=_axis_style(grid=True),
+        xaxis=_axis_style(grid=False),
+    )
+    st.plotly_chart(fig, use_container_width=True, config=_chart_cfg())
+
+
+def render_clarity_cross_chart(df: pd.DataFrame) -> None:
+    """Grouped bar: clarity distribution crossed with project type or nature.
+
+    US07 — visualizar relação entre clareza, tipo, natureza e linguagem.
+    """
+    _panel_header("🔗 Clareza por Dimensão", "linear-gradient(180deg,#f472b6,#db2777)")
+
+    required = {"clarity", "type", "nature", "lang"}
+    if not required.issubset(df.columns) or len(df) == 0:
+        st.info(_NO_DATA_MSG)
+        return
+
+    dim: str = st.selectbox(
+        "Cruzar clareza com",
+        ["type", "nature", "lang"],
+        format_func=lambda x: {
+            "type": "Tipo de Projeto",
+            "nature": "Natureza",
+            "lang": "Linguagem",
+        }[x],
+        key="clarity_cross_dim",
+        label_visibility="collapsed",
+    )
+
+    filtered = df[(df["clarity"] != "—") & (df[dim] != "—")]
+    if filtered.empty:
+        st.info(_NO_DATA_MSG)
+        return
+
+    counts = filtered.groupby([dim, "clarity"]).size().reset_index(name="count")
+
+    fig = px.bar(
+        counts,
+        x=dim,
+        y="count",
+        color="clarity",
+        barmode="group",
+        color_discrete_map=CLARITY_COLOR,
+        category_orders={"clarity": CLARITY_ORDER},
+        labels={dim: "", "count": "PRs", "clarity": "Clareza"},
+    )
+    fig.update_layout(
+        **PLOT_BASE,
+        legend={"orientation": "h", "y": 1.1, "x": 0},
+        yaxis=_axis_style(grid=True),
+        xaxis=_axis_style(grid=False),
+    )
+    st.plotly_chart(fig, use_container_width=True, config=_chart_cfg())
+
+
 # ── Private helpers ──────────────────────────────────────────────────────────