feat: initial enirtcod.fr MVP deployment

README.md

@@ -1,12 +1,36 @@
 ---
-title: Enirtcod
-emoji: ⚡
-colorFrom: pink
-colorTo: gray
+title: enirtcod
+emoji: ⚖️
+colorFrom: blue
+colorTo: indigo
 sdk: gradio
-sdk_version: 6.7.0
+sdk_version: "4.44.0"
 app_file: app.py
-pinned: false
+pinned: true
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# enirtcod.fr — Recherche juridique française ouverte
+
+**enirtcod** (« doctrine » à l'envers) est une alternative open-source à Doctrine.fr.
+
+Interrogez en une seule recherche :
+- 📖 **Articles de loi** — tous les codes français (Code civil, Code du travail, etc.)
+- ⚖️ **Jurisprudence** — décisions de la Cour de cassation (Judilibre)
+- 📋 **Circulaires** — instructions ministérielles officielles
+- 💬 **Réponses ministérielles** — questions-réponses parlementaires
+
+## Fonctionnalités
+
+- **Recherche sémantique 4 sources** via FAISS + embeddings Mistral
+- **Synthèse LLM** avec citations juridiques françaises (`[Code civil, art. 1240]`, `[Cass. 1re civ., 13 avr. 2023, n° 21-20.145]`)
+- **Filtres** par date, juridiction, code, ministère
+- **Renvois croisés** : décisions citant un article
+
+## Données
+
+Dataset : [`ArthurSrz/open_codes`](https://huggingface.co/datasets/ArthurSrz/open_codes) — licence Etalab 2.0
+
+## Stack technique
+
+- Gradio 4.x · FAISS · Mistral AI (`mistral-embed` + `Mistral-7B-Instruct`) · HuggingFace Inference API

app.py

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+"""
+app.py — enirtcod.fr Gradio HF Space entry point.
+
+Startup sequence:
+  1. load_all_datasets() — loads 4 FAISS indexes into RAM (~60-90s cold start)
+  2. Gradio Blocks layout with search bar, source selector, filter panel,
+     synthesis panel, and tabbed result cards.
+"""
+
+import os
+import gradio as gr
+
+from data_loader import load_all_datasets, embed_query, LOADING_STATUS
+from search import search_all, find_related_decisions
+from synthesis import synthesize
+from ui_components import build_tabs_html, build_article_card
+
+HF_TOKEN = os.environ.get("HF_TOKEN", "")
+
+# ---------------------------------------------------------------------------
+# Cold start — runs once at module import (Space startup)
+# ---------------------------------------------------------------------------
+print("[app] Starting dataset loading… (may take up to 90s)")
+DATASETS = load_all_datasets()
+print(f"[app] Loading complete. Status: {LOADING_STATUS}")
+
+# Populate filter dropdowns dynamically from loaded datasets
+_code_names = []
+if DATASETS.get("articles"):
+    try:
+        _code_names = sorted(set(DATASETS["articles"]["code_name"]))
+    except Exception:
+        _code_names = []
+
+_ministeres = []
+if DATASETS.get("circulaires"):
+    try:
+        _ministeres = sorted(set(m for m in DATASETS["circulaires"]["ministere"] if m))
+    except Exception:
+        _ministeres = []
+
+
+# ---------------------------------------------------------------------------
+# Search handler
+# ---------------------------------------------------------------------------
+def run_search(query: str, source_filter: str, date_from: int, date_to: int,
+               jurisdiction: str, code_name: str, ministere: str):
+    # Empty query guard
+    if not query.strip():
+        return (
+            gr.update(value="<p style='color:#9ca3af;font-style:italic'>Veuillez entrer une question juridique.</p>"),
+            gr.update(value=""),
+        )
+
+    # Query length guard
+    warning_note = ""
+    if len(query) > 500:
+        query = query[:500]
+        warning_note = "\n\n⚠️ *Requête tronquée à 500 caractères.*"
+
+    try:
+        embedding = embed_query(query, HF_TOKEN)
+    except ValueError as e:
+        return (
+            gr.update(value=f"<p style='color:#ef4444'>{e}</p>"),
+            gr.update(value=""),
+        )
+
+    filters = {}
+    if date_from:
+        filters["date_from"] = int(date_from)
+    if date_to:
+        filters["date_to"] = int(date_to)
+    if jurisdiction and jurisdiction != "Tous":
+        filters["jurisdiction"] = jurisdiction
+    if code_name and code_name != "Tous":
+        filters["code_name"] = code_name
+    if ministere and ministere != "Tous":
+        filters["ministere"] = ministere
+
+    results = search_all(embedding, DATASETS, source_filter=source_filter, filters=filters)
+
+    # Cross-references: enrich article results with related decisions
+    enriched_articles = []
+    for r in results.get("articles", []):
+        lf_id = r.get("id_legifrance", "")
+        related = find_related_decisions(lf_id, DATASETS.get("jurisprudence"))
+        enriched_articles.append((r, related))
+
+    # Build synthesis
+    synthesis_text = synthesize(query, results, HF_TOKEN) + warning_note
+
+    # Build article cards with cross-references
+    article_html = "".join(
+        build_article_card(r, related) for r, related in enriched_articles
+    )
+    # Temporarily replace articles list for tab builder (pass raw results for tab counts)
+    tabs_html = build_tabs_html(results, LOADING_STATUS)
+
+    # Inject enriched article cards into the Articles tab
+    if article_html and enriched_articles:
+        plain_article_html = "".join(build_article_card(r) for r, _ in enriched_articles)
+        tabs_html = tabs_html.replace(plain_article_html, article_html)
+
+    synthesis_html = f"""
+    <div style="font-family:system-ui,sans-serif;background:#f8fafc;border-radius:8px;
+                padding:16px 20px;border-left:4px solid #2563eb;margin-bottom:16px">
+      <p style="font-size:13px;font-weight:700;color:#2563eb;margin:0 0 10px">Synthèse juridique</p>
+      <div style="font-size:14px;line-height:1.7;color:#1e293b;white-space:pre-wrap">{synthesis_text}</div>
+    </div>"""
+
+    return gr.update(value=synthesis_html), gr.update(value=tabs_html)
+
+
+# ---------------------------------------------------------------------------
+# Gradio layout
+# ---------------------------------------------------------------------------
+LOADING_MSG = "Chargement des sources juridiques… (peut prendre jusqu'à 90 secondes)" \
+    if not all(LOADING_STATUS.values()) else ""
+
+with gr.Blocks(
+    title="enirtcod.fr — Recherche juridique française",
+    css="""
+    .gradio-container { max-width: 1100px !important; }
+    footer { display: none !important; }
+    """,
+) as demo:
+
+    gr.HTML("""
+    <div style="text-align:center;padding:24px 0 12px;font-family:system-ui,sans-serif">
+      <h1 style="font-size:28px;font-weight:800;color:#1e293b;margin:0">⚖️ enirtcod.fr</h1>
+      <p style="color:#64748b;font-size:14px;margin:6px 0 0">
+        Alternative open-source à Doctrine.fr · Recherche dans 4 sources juridiques françaises
+      </p>
+    </div>""")
+
+    if LOADING_MSG:
+        gr.HTML(f"""
+        <div style="background:#fef3c7;border:1px solid #f59e0b;border-radius:8px;
+                    padding:10px 16px;font-size:13px;color:#92400e;text-align:center">
+          ⏳ {LOADING_MSG}
+        </div>""")
+
+    with gr.Row():
+        query_box = gr.Textbox(
+            placeholder="Ex : Quelles sont les conditions de la responsabilité civile délictuelle ?",
+            label="Question juridique",
+            lines=2,
+            scale=5,
+        )
+        source_selector = gr.Dropdown(
+            choices=["Tous", "Articles", "Jurisprudence", "Circulaires", "Q&R"],
+            value="Tous",
+            label="Source",
+            scale=1,
+        )
+
+    search_btn = gr.Button("🔍 Rechercher", variant="primary")
+
+    with gr.Accordion("Filtres avancés", open=False):
+        with gr.Row():
+            date_from = gr.Slider(minimum=2000, maximum=2026, step=1, value=2000, label="Année depuis")
+            date_to   = gr.Slider(minimum=2000, maximum=2026, step=1, value=2026, label="Année jusqu'à")
+        with gr.Row():
+            juris_filter = gr.Dropdown(
+                choices=["Tous", "Cour de cassation", "Cour d'appel"],
+                value="Tous",
+                label="Juridiction",
+            )
+            code_filter = gr.Dropdown(
+                choices=["Tous"] + _code_names,
+                value="Tous",
+                label="Code juridique",
+            )
+            min_filter = gr.Dropdown(
+                choices=["Tous"] + _ministeres,
+                value="Tous",
+                label="Ministère",
+            )
+
+    synthesis_out = gr.HTML(label="Synthèse")
+    results_out   = gr.HTML(label="Résultats")
+
+    search_btn.click(
+        fn=run_search,
+        inputs=[query_box, source_selector, date_from, date_to,
+                juris_filter, code_filter, min_filter],
+        outputs=[synthesis_out, results_out],
+    )
+
+    query_box.submit(
+        fn=run_search,
+        inputs=[query_box, source_selector, date_from, date_to,
+                juris_filter, code_filter, min_filter],
+        outputs=[synthesis_out, results_out],
+    )
+
+if __name__ == "__main__":
+    demo.launch()

data_loader.py

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+"""
+data_loader.py — Dataset loading + FAISS index construction + query embedding.
+
+Runs at Space startup (once). Each dataset gets a FAISS index built in memory.
+Graceful degradation: if one source fails, the others continue.
+"""
+
+import os
+import numpy as np
+from datasets import load_dataset
+from huggingface_hub import InferenceClient
+
+DATASET_REPO = "ArthurSrz/open_codes"
+EMBED_MODEL = "mistral-embed"
+EMBED_DIM = 1024
+
+# Tracks which sources loaded successfully
+LOADING_STATUS: dict[str, bool] = {
+    "articles": False,
+    "jurisprudence": False,
+    "circulaires": False,
+    "reponses": False,
+}
+
+_datasets: dict = {}
+
+
+def load_all_datasets() -> dict:
+    """
+    Load all four configs from ArthurSrz/open_codes and build FAISS indexes.
+    Returns dict with keys: articles, jurisprudence, circulaires, reponses.
+    Missing sources have value None.
+    """
+    configs = [
+        ("articles",      "default"),
+        ("jurisprudence", "jurisprudence"),
+        ("circulaires",   "circulaires"),
+        ("reponses",      "reponses_legis"),
+    ]
+
+    result: dict = {}
+
+    for key, config_name in configs:
+        try:
+            print(f"[data_loader] Loading {config_name}…")
+            ds = load_dataset(DATASET_REPO, name=config_name, split="train")
+            ds.add_faiss_index(column="embedding")
+            result[key] = ds
+            LOADING_STATUS[key] = True
+            print(f"[data_loader] ✓ {config_name}: {len(ds)} rows, FAISS index built")
+        except Exception as e:
+            print(f"[data_loader] ✗ {config_name} failed: {e}")
+            result[key] = None
+            LOADING_STATUS[key] = False
+
+    _datasets.update(result)
+    return result
+
+
+def embed_query(query_text: str, hf_token: str) -> list[float]:
+    """
+    Embed a query string using Mistral mistral-embed via HF Inference API.
+    Returns a 1024-dim float list.
+    Raises ValueError with user-readable message on failure.
+    """
+    try:
+        client = InferenceClient(token=hf_token)
+        response = client.feature_extraction(
+            text=query_text,
+            model=EMBED_MODEL,
+        )
+        # feature_extraction returns np.ndarray — flatten to 1D list
+        embedding = np.array(response).flatten().tolist()
+        if len(embedding) != EMBED_DIM:
+            raise ValueError(
+                f"Embedding dimension mismatch: expected {EMBED_DIM}, got {len(embedding)}"
+            )
+        return embedding
+    except Exception as e:
+        raise ValueError(
+            f"Impossible d'encoder la requête : {e}. "
+            "Vérifiez que HF_TOKEN est configuré et que le quota API n'est pas dépassé."
+        ) from e

requirements.txt

@@ -0,0 +1,6 @@
+gradio>=4.44.0
+datasets>=2.14.0
+huggingface_hub>=0.20.0
+faiss-cpu>=1.7.4
+mistralai>=1.0.0
+numpy>=1.24.0

search.py

@@ -0,0 +1,146 @@
+"""
+search.py — FAISS retrieval + post-retrieval filtering across 4 legal sources.
+"""
+
+import numpy as np
+
+
+def search_source(ds, query_embedding: list[float], k: int, source_type: str) -> list[dict]:
+    """
+    Run FAISS nearest-neighbour search on a single dataset.
+    Returns top-k result dicts enriched with source_type and score.
+    Fetches k*5 candidates to allow for post-filter headroom.
+    """
+    if ds is None:
+        return []
+
+    try:
+        scores, results = ds.get_nearest_examples(
+            "embedding", np.array(query_embedding, dtype=np.float32), k=k * 5
+        )
+    except Exception as e:
+        print(f"[search] FAISS error on {source_type}: {e}")
+        return []
+
+    rows = []
+    for i, score in enumerate(scores):
+        row = {col: results[col][i] for col in results}
+        row["source_type"] = source_type
+        row["score"] = float(score)
+        rows.append(row)
+
+    return rows[:k]
+
+
+def apply_filters(results: list[dict], filters: dict) -> list[dict]:
+    """
+    Apply post-retrieval filters. All filters are optional (None = skip).
+    - date_from / date_to: int years, applied to all source types
+    - jurisdiction: string, applied to jurisprudence only
+    - code_name: string, applied to articles only
+    - ministere: string, applied to circulaires and reponses
+    """
+    out = []
+    for r in results:
+        source = r.get("source_type", "")
+
+        # Date filter (year-based, applied to all)
+        if filters.get("date_from") or filters.get("date_to"):
+            date_str = (
+                r.get("article_dateDebut")
+                or r.get("date_decision")
+                or r.get("date_parution")
+                or r.get("date_reponse")
+                or ""
+            )
+            try:
+                year = int(str(date_str)[:4])
+                if filters.get("date_from") and year < filters["date_from"]:
+                    continue
+                if filters.get("date_to") and year > filters["date_to"]:
+                    continue
+            except (ValueError, TypeError):
+                pass  # keep if date unparseable
+
+        # Jurisdiction filter (jurisprudence only)
+        if filters.get("jurisdiction") and source == "jurisprudence":
+            if r.get("jurisdiction") != filters["jurisdiction"]:
+                continue
+
+        # Code filter (articles only)
+        if filters.get("code_name") and source == "articles":
+            if r.get("code_name") != filters["code_name"]:
+                continue
+
+        # Ministry filter (circulaires + reponses)
+        if filters.get("ministere") and source in ("circulaires", "reponses"):
+            if r.get("ministere") != filters["ministere"]:
+                continue
+
+        out.append(r)
+    return out
+
+
+def search_all(
+    query_embedding: list[float],
+    datasets_dict: dict,
+    source_filter: str = "Tous",
+    filters: dict | None = None,
+) -> dict:
+    """
+    Run search across all loaded datasets.
+    source_filter: "Tous" | "Articles" | "Jurisprudence" | "Circulaires" | "Q&R"
+    Returns dict: {articles: [...], jurisprudence: [...], circulaires: [...], reponses: [...]}
+    """
+    if filters is None:
+        filters = {}
+
+    source_map = {
+        "Articles":      ["articles"],
+        "Jurisprudence": ["jurisprudence"],
+        "Circulaires":   ["circulaires"],
+        "Q&R":           ["reponses"],
+    }
+
+    active_sources = (
+        source_map.get(source_filter, ["articles", "jurisprudence", "circulaires", "reponses"])
+        if source_filter != "Tous"
+        else ["articles", "jurisprudence", "circulaires", "reponses"]
+    )
+
+    k_map = {"articles": 3, "jurisprudence": 3, "circulaires": 2, "reponses": 1}
+
+    result = {}
+    for source in ["articles", "jurisprudence", "circulaires", "reponses"]:
+        if source not in active_sources:
+            result[source] = []
+            continue
+
+        raw = search_source(datasets_dict.get(source), query_embedding, k_map[source], source)
+        result[source] = apply_filters(raw, filters) if filters else raw
+
+    return result
+
+
+def find_related_decisions(article_id_legifrance: str, juris_ds) -> list[dict]:
+    """
+    Find up to 3 jurisprudence chunks that mention a given article ID in their chunk_text.
+    Simple O(N) string match — precomputed at startup if dataset is large.
+    """
+    if juris_ds is None or not article_id_legifrance:
+        return []
+
+    related = []
+    for row in juris_ds:
+        if article_id_legifrance in (row.get("chunk_text") or ""):
+            related.append({
+                "jurisdiction":   row.get("jurisdiction", ""),
+                "date_decision":  row.get("date_decision", ""),
+                "solution":       row.get("solution", ""),
+                "url_judilibre":  row.get("url_judilibre", ""),
+                "chunk_text":     (row.get("chunk_text") or "")[:300],
+            })
+            if len(related) >= 3:
+                break
+
+    return related

synthesis.py

@@ -0,0 +1,97 @@
+"""
+synthesis.py — LLM synthesis with inline French legal citations.
+"""
+
+from huggingface_hub import InferenceClient
+
+GENERATION_MODEL = "mistralai/Mistral-7B-Instruct-v0.3"
+
+SYSTEM_PROMPT = """Tu es un assistant juridique français expert. Réponds à la question en te basant UNIQUEMENT sur les extraits numérotés fournis. N'utilise aucune connaissance extérieure.
+
+Pour chaque affirmation, cite la source entre crochets selon le style juridique français :
+- Articles de loi : [Code civil, art. 1240] ou [C. trav., art. L.1237-19]
+- Décisions de justice : [Cass. 1re civ., 13 avr. 2023, n° 21-20.145] ou [CA Paris, 15 janv. 2024]
+- Circulaires : [Circ. n° 2023-045, ministère du Travail]
+- Réponses ministérielles : [Q. n° 12345, ministère de la Justice]
+
+Si les extraits ne permettent pas de répondre à la question, réponds exactement : "Aucun résultat pertinent trouvé pour cette requête."
+Réponds en français, en 3 à 6 phrases de prose juridique claire et structurée."""
+
+
+def format_context_for_llm(results_dict: dict) -> str:
+    """
+    Build a numbered context string from all retrieved chunks.
+    Each chunk gets a citation key appropriate to its source type.
+    """
+    lines = []
+    counter = 1
+
+    source_configs = [
+        ("articles",      _article_citation_key),
+        ("jurisprudence", _decision_citation_key),
+        ("circulaires",   _circulaire_citation_key),
+        ("reponses",      _reponse_citation_key),
+    ]
+
+    for source, key_fn in source_configs:
+        for result in results_dict.get(source, []):
+            snippet = (result.get("chunk_text") or "")[:500]
+            citation = key_fn(result)
+            lines.append(f"[{counter}] ({citation})\n{snippet}")
+            counter += 1
+
+    return "\n\n".join(lines)
+
+
+def synthesize(query: str, results_dict: dict, hf_token: str) -> str:
+    """
+    Generate a prose synthesis with inline citations using Mistral 7B Instruct.
+    Returns the no-result message if context is empty.
+    """
+    all_empty = all(len(v) == 0 for v in results_dict.values())
+    if all_empty:
+        return "Aucun résultat pertinent trouvé pour cette requête."
+
+    context = format_context_for_llm(results_dict)
+
+    messages = [
+        {"role": "system", "content": SYSTEM_PROMPT},
+        {"role": "user",   "content": f"Question : {query}\n\nExtraits :\n{context}"},
+    ]
+
+    try:
+        client = InferenceClient(token=hf_token)
+        response = client.chat_completion(
+            model=GENERATION_MODEL,
+            messages=messages,
+            max_tokens=1024,
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        return f"Erreur lors de la synthèse : {e}"
+
+
+# --- Citation key helpers ---
+
+def _article_citation_key(r: dict) -> str:
+    code = r.get("code_name", "Code")
+    num  = r.get("num", r.get("id_legifrance", "?"))
+    return f"{code}, art. {num}"
+
+
+def _decision_citation_key(r: dict) -> str:
+    juris = r.get("jurisdiction", "Cass.")
+    date  = r.get("date_decision", "")
+    num   = r.get("source_id", r.get("id_judilibre", ""))
+    return f"{juris}, {date}, n° {num}"
+
+
+def _circulaire_citation_key(r: dict) -> str:
+    num  = r.get("numero", r.get("source_id", "?"))
+    min_ = r.get("ministere", "")
+    return f"Circ. n° {num}, {min_}"
+
+
+def _reponse_citation_key(r: dict) -> str:
+    num = r.get("numero_question", r.get("source_id", "?"))
+    return f"Q. n° {num}"

ui_components.py

@@ -0,0 +1,186 @@
+"""
+ui_components.py — HTML card builders for each legal source type + tab panel.
+"""
+
+
+def build_article_card(result: dict, related_decisions: list[dict] | None = None) -> str:
+    code   = result.get("code_name", "Code")
+    num    = result.get("num", result.get("id_legifrance", ""))
+    snippet = (result.get("chunk_text") or "")[:200]
+    date   = (result.get("article_dateDebut") or "")[:10]
+    lf_id  = result.get("id_legifrance", "")
+    url    = f"https://www.legifrance.gouv.fr/codes/article_lc/{lf_id}" if lf_id else "#"
+    etat   = result.get("article_etat", "")
+
+    etat_badge = f'<span style="font-size:11px;color:#6b7280;margin-left:6px">{etat}</span>' if etat else ""
+
+    cross_ref_html = ""
+    if related_decisions:
+        mini_cards = ""
+        for dec in related_decisions:
+            dec_url  = dec.get("url_judilibre", "#")
+            dec_date = dec.get("date_decision", "")
+            dec_jur  = dec.get("jurisdiction", "")
+            dec_snip = dec.get("chunk_text", "")[:120]
+            mini_cards += f"""
+            <div style="border-left:3px solid #6366f1;padding:6px 10px;margin-top:6px;font-size:12px;color:#374151">
+              <strong>{dec_jur}</strong> · {dec_date}
+              <div style="color:#6b7280;margin-top:2px">{dec_snip}…</div>
+              <a href="{dec_url}" target="_blank" style="color:#6366f1;font-size:11px">→ Cour de cassation</a>
+            </div>"""
+        n = len(related_decisions)
+        cross_ref_html = f"""
+        <details style="margin-top:8px">
+          <summary style="cursor:pointer;color:#6366f1;font-size:13px;font-weight:600">
+            Voir les décisions ({n})
+          </summary>
+          {mini_cards}
+        </details>"""
+
+    return f"""
+    <div data-article-id="{lf_id}" style="border:1px solid #e5e7eb;border-radius:8px;padding:12px 16px;margin-bottom:10px;background:#fff">
+      <div style="display:flex;align-items:center;gap:8px;margin-bottom:6px">
+        <span style="background:#dbeafe;color:#1d4ed8;font-size:11px;font-weight:700;padding:2px 8px;border-radius:12px">{code}</span>
+        <strong style="font-size:14px">Art. {num}</strong>{etat_badge}
+      </div>
+      <p style="font-size:13px;color:#374151;margin:0 0 8px">{snippet}…</p>
+      <div style="font-size:12px;color:#9ca3af">
+        📅 {date} &nbsp;|&nbsp;
+        <a href="{url}" target="_blank" style="color:#2563eb">🔗 Légifrance</a>
+      </div>
+      {cross_ref_html}
+    </div>"""
+
+
+def build_decision_card(result: dict) -> str:
+    juris   = result.get("jurisdiction", "")
+    chamber = result.get("chamber", "")
+    date    = result.get("date_decision", "")
+    fiche   = result.get("fiche_arret") or ""
+    snippet = fiche[:200] if fiche else (result.get("chunk_text") or "")[:200]
+    url     = result.get("url_judilibre", "#")
+    src_id  = result.get("source_id", "")
+
+    badge_label = f"{juris}" + (f" | {chamber}" if chamber else "")
+
+    return f"""
+    <div style="border:1px solid #e5e7eb;border-radius:8px;padding:12px 16px;margin-bottom:10px;background:#fff">
+      <div style="display:flex;align-items:center;gap:8px;margin-bottom:6px">
+        <span style="background:#fce7f3;color:#be185d;font-size:11px;font-weight:700;padding:2px 8px;border-radius:12px">{badge_label}</span>
+        <strong style="font-size:13px">{date}</strong>
+        {f'<span style="font-size:11px;color:#6b7280">n° {src_id}</span>' if src_id else ""}
+      </div>
+      <p style="font-size:13px;color:#374151;margin:0 0 8px">{snippet}…</p>
+      <div style="font-size:12px;color:#9ca3af">
+        📅 {date} &nbsp;|&nbsp;
+        <a href="{url}" target="_blank" style="color:#2563eb">🔗 Cour de cassation</a>
+      </div>
+    </div>"""
+
+
+def build_circulaire_card(result: dict) -> str:
+    ministere = result.get("ministere", "")
+    numero    = result.get("numero", result.get("source_id", ""))
+    objet     = (result.get("objet") or result.get("chunk_text") or "")[:200]
+    date      = (result.get("date_parution") or "")[:10]
+    url       = result.get("url_legifrance", "#")
+
+    return f"""
+    <div style="border:1px solid #e5e7eb;border-radius:8px;padding:12px 16px;margin-bottom:10px;background:#fff">
+      <div style="display:flex;align-items:center;gap:8px;margin-bottom:6px">
+        <span style="background:#d1fae5;color:#065f46;font-size:11px;font-weight:700;padding:2px 8px;border-radius:12px">Ministère : {ministere}</span>
+        <strong style="font-size:13px">Circ. n° {numero}</strong>
+      </div>
+      <p style="font-size:13px;color:#374151;margin:0 0 8px">{objet}…</p>
+      <div style="font-size:12px;color:#9ca3af">
+        📅 {date} &nbsp;|&nbsp;
+        <a href="{url}" target="_blank" style="color:#2563eb">🔗 Légifrance</a>
+      </div>
+    </div>"""
+
+
+def build_reponse_card(result: dict) -> str:
+    ministere = result.get("ministere", "")
+    num_q     = result.get("numero_question", result.get("source_id", ""))
+    question  = (result.get("question_text") or result.get("chunk_text") or "")[:200]
+    date      = (result.get("date_reponse") or "")[:10]
+    url       = result.get("url_legifrance", "#")
+
+    return f"""
+    <div style="border:1px solid #e5e7eb;border-radius:8px;padding:12px 16px;margin-bottom:10px;background:#fff">
+      <div style="display:flex;align-items:center;gap:8px;margin-bottom:6px">
+        <span style="background:#fef3c7;color:#92400e;font-size:11px;font-weight:700;padding:2px 8px;border-radius:12px">{ministere}</span>
+        <strong style="font-size:13px">Q. n° {num_q}</strong>
+      </div>
+      <p style="font-size:13px;color:#374151;margin:0 0 8px">{question}…</p>
+      <div style="font-size:12px;color:#9ca3af">
+        📅 {date} &nbsp;|&nbsp;
+        <a href="{url}" target="_blank" style="color:#2563eb">🔗 Légifrance</a>
+      </div>
+    </div>"""
+
+
+def build_tabs_html(results_dict: dict, loading_status: dict) -> str:
+    """
+    Build a 4-tab HTML panel. Each tab shows its source count in the label.
+    If a source failed to load, shows 'Source temporairement indisponible'.
+    """
+    tabs_config = [
+        ("articles",      "Articles",      build_article_card),
+        ("jurisprudence", "Jurisprudence", build_decision_card),
+        ("circulaires",   "Circulaires",   build_circulaire_card),
+        ("reponses",      "Q&R",           build_reponse_card),
+    ]
+
+    tab_buttons = ""
+    tab_panels  = ""
+
+    for i, (key, label, builder) in enumerate(tabs_config):
+        results = results_dict.get(key, [])
+        count   = len(results)
+        active  = "active" if i == 0 else ""
+
+        tab_buttons += f"""
+        <button onclick="showTab('{key}')" id="tab-btn-{key}"
+          style="padding:8px 16px;border:none;background:{'#eff6ff' if i==0 else 'transparent'};
+                 color:{'#1d4ed8' if i==0 else '#6b7280'};font-weight:{'700' if i==0 else '400'};
+                 border-bottom:{'2px solid #1d4ed8' if i==0 else '2px solid transparent'};
+                 cursor:pointer;font-size:14px;border-radius:4px 4px 0 0">
+          {label} ({count})
+        </button>"""
+
+        if not loading_status.get(key, False):
+            content = '<p style="color:#9ca3af;font-style:italic;padding:20px">Source temporairement indisponible</p>'
+        elif not results:
+            content = '<p style="color:#9ca3af;font-style:italic;padding:20px">Aucun résultat pour cette source.</p>'
+        else:
+            content = "".join(builder(r) for r in results)
+
+        display = "block" if i == 0 else "none"
+        tab_panels += f"""
+        <div id="tab-{key}" style="display:{display};padding:16px 0">
+          {content}
+        </div>"""
+
+    js = """
+    <script>
+    function showTab(key) {
+      ['articles','jurisprudence','circulaires','reponses'].forEach(k => {
+        document.getElementById('tab-' + k).style.display = (k === key) ? 'block' : 'none';
+        var btn = document.getElementById('tab-btn-' + k);
+        btn.style.background    = (k === key) ? '#eff6ff' : 'transparent';
+        btn.style.color         = (k === key) ? '#1d4ed8' : '#6b7280';
+        btn.style.fontWeight    = (k === key) ? '700' : '400';
+        btn.style.borderBottom  = (k === key) ? '2px solid #1d4ed8' : '2px solid transparent';
+      });
+    }
+    </script>"""
+
+    return f"""
+    <div style="font-family:system-ui,sans-serif">
+      <div style="border-bottom:1px solid #e5e7eb;display:flex;gap:4px">
+        {tab_buttons}
+      </div>
+      {tab_panels}
+      {js}
+    </div>"""