Upload app.py

app.py

@@ -10,8 +10,11 @@ from huggingface_hub import InferenceClient
 # ────────────────────────────────────────────────────────────────────────────────
 # CONFIG
 
-MODEL_ID = os.environ.get("MODEL_ID", "meta-llama/Llama-3.1-8B-Instruct")
-HF_TOKEN = os.environ.get("HF_TOKEN")  # set as repo secret if model needs auth
+# You can hard-code the model here, or set MODEL_ID as a Space secret.
+MODEL_ID = os.environ.get("MODEL_ID")
+HF_TOKEN = os.environ.get("HF_TOKEN")  # optional for this public model
+
+REPO_PATH = "/data/questions.json"  # where we store generated questions
 
 CATEGORIES = [
     {"key": "alimentation", "icon": "🍎", "fr": "Alimentation", "en": "Nutrition"},
@@ -38,7 +41,7 @@ GUIDES = {
     },
 }
 
-# Few-shot examples for style + fallback
+# Few-shot + fallback pools
 FEWSHOTS = {
     "fr": {
         "alimentation": {
@@ -206,6 +209,38 @@ FEWSHOTS = {
     },
 }
 
+# ────────────────────────────────────────────────────────────────────────────────
+# REPOSITORY HELPERS (questions only, per category)
+
+
+def _default_repo() -> Dict[str, List[str]]:
+    return {c["key"]: [] for c in CATEGORIES}
+
+
+def load_repo() -> Dict[str, List[str]]:
+    os.makedirs(os.path.dirname(REPO_PATH), exist_ok=True)
+    if not os.path.exists(REPO_PATH):
+        data = _default_repo()
+        with open(REPO_PATH, "w", encoding="utf-8") as f:
+            json.dump(data, f, ensure_ascii=False, indent=2)
+        return data
+    try:
+        with open(REPO_PATH, "r", encoding="utf-8") as f:
+            data = json.load(f)
+    except Exception:
+        data = _default_repo()
+    # Ensure all categories exist
+    base = _default_repo()
+    base.update({k: v for k, v in data.items() if isinstance(v, list)})
+    return base
+
+
+def save_repo(data: Dict[str, List[str]]) -> None:
+    os.makedirs(os.path.dirname(REPO_PATH), exist_ok=True)
+    with open(REPO_PATH, "w", encoding="utf-8") as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+
+
 # ────────────────────────────────────────────────────────────────────────────────
 # PROMPT + MODEL HELPERS
 
@@ -248,7 +283,7 @@ Tu crées des cartes-question pour parler de routines du quotidien.
 - Catégorie: {cat['fr']} {cat['icon']}.
 - Focus: {guide}
 - Ton: {tone_fr}
-- Forme: 4 questions + 2 micro-actions, 1 phrase courte chacune.
+- Format: 4 questions + 2 micro-actions, 1 phrase courte chacune.
 - Style: concret, bienveillant, sans jugement.
 - Interdit: conseils médicaux, diagnostics, emojis.
 
@@ -269,7 +304,7 @@ You create question-cards about everyday routines.
 - Category: {cat['en']} {cat['icon']}.
 - Focus: {guide}
 - Tone: {tone_en}
-- Format: 4 questions + 2 micro-actions, 1 short sentence each.
+- Format: 4 questions + 2 micro-actions, one short sentence each.
 - Style: concrete, kind, non-judgmental.
 - Forbidden: medical advice, diagnoses, emojis.
 
@@ -339,7 +374,6 @@ def normalize_output(
     q = [str(x).strip() for x in data.get("questions", []) if str(x).strip()]
     m = [str(x).strip() for x in data.get("micro_actions", []) if str(x).strip()]
 
-    # exactly 4 questions, 2 micro-actions
     q = (q + [""] * 4)[:4]
     m = (m + [""] * 2)[:2]
 
@@ -363,7 +397,6 @@ def normalize_output(
 
 def ai_generate(lang: str, category_key: str, variant: str) -> Dict[str, Any]:
     prompt = build_prompt(lang, category_key, variant)
-
     raw_text = None
     try:
         raw_text = model_call(prompt)
@@ -374,30 +407,92 @@ def ai_generate(lang: str, category_key: str, variant: str) -> Dict[str, Any]:
 
     if parsed:
         normalized = normalize_output(parsed, lang, category_key, variant)
-    else:
-        # Fallback: sample from local examples so it never breaks
-        few = FEWSHOTS[lang][category_key]
-        questions_pool = few["questions"][:]
-        micro_pool = few["micro_actions"][:]
-        random.shuffle(questions_pool)
-        random.shuffle(micro_pool)
-        normalized = {
-            "category": category_key,
-            "language": lang,
-            "questions": (questions_pool + [""] * 4)[:4],
-            "micro_actions": (micro_pool + [""] * 2)[:2],
-            "tone": "fallback",
-            "safety_notes": "",
-        }
+        return normalized
 
+    # fallback: shuffle fewshots
+    few = FEWSHOTS[lang][category_key]
+    q_pool = few["questions"][:]
+    m_pool = few["micro_actions"][:]
+    random.shuffle(q_pool)
+    random.shuffle(m_pool)
     return {
-        "questions": normalized["questions"],
-        "micro_actions": normalized["micro_actions"],
-        "raw_json": json.dumps(normalized, ensure_ascii=False, indent=2),
+        "category": category_key,
+        "language": lang,
+        "questions": (q_pool + [""] * 4)[:4],
+        "micro_actions": (m_pool + [""] * 2)[:2],
+        "tone": "fallback",
+        "safety_notes": "",
     }
 
+
 # ────────────────────────────────────────────────────────────────────────────────
-# UI – pastel, animated, card-based
+# MAIN LOGIC: REPO + SESSION "SEEN" QUESTIONS
+
+
+def get_questions_and_micro(
+    lang: str,
+    category_key: str,
+    variant: str,
+    seen: List[str],
+) -> Dict[str, Any]:
+    """
+    1. Load /data/questions.json
+    2. If repo has >=4 unseen questions -> sample 4 from repo, micro from local pool.
+    3. Else -> call AI, store any new questions into repo, use AI's questions + micro.
+    4. Update seen list so this session won't see the same question twice.
+    """
+    seen_set = set(seen or [])
+    repo = load_repo()
+    repo_qs = repo.get(category_key, [])
+
+    unseen_repo = [q for q in repo_qs if q and q not in seen_set]
+
+    used_ai = False
+
+    if len(unseen_repo) >= 4:
+        # entirely from repo, no AI call
+        random.shuffle(unseen_repo)
+        questions = unseen_repo[:4]
+        # micro-actions from local fewshot pool (cheap)
+        m_pool = FEWSHOTS[lang][category_key]["micro_actions"][:]
+        random.shuffle(m_pool)
+        micro = (m_pool + ["", ""])[:2]
+    else:
+        # need fresh AI content
+        ai_out = ai_generate(lang, category_key, variant)
+        questions = ai_out["questions"]
+        micro = ai_out["micro_actions"]
+        used_ai = True
+
+        # store new questions in repo
+        new_qs = [q for q in questions if q and q not in repo_qs]
+        if new_qs:
+            repo_qs_extended = repo_qs + new_qs
+            repo[category_key] = repo_qs_extended
+            save_repo(repo)
+
+    # update seen for this session
+    for q in questions:
+        if q:
+            seen_set.add(q)
+
+    payload = {
+        "category": category_key,
+        "language": lang,
+        "questions": questions,
+        "micro_actions": micro,
+        "source": "ai" if used_ai else "repo",
+    }
+    return {
+        "questions": questions,
+        "micro_actions": micro,
+        "raw_json": json.dumps(payload, ensure_ascii=False, indent=2),
+        "seen": list(seen_set),
+    }
+
+
+# ────────────────────────────────────────────────────────────────────────────────
+# UI – pastel, animated, color-coded cards
 
 
 CUSTOM_CSS = """
@@ -407,11 +502,11 @@ CUSTOM_CSS = """
     --nv-border: #e5d8c7;
     --nv-accent: #f38a6b;
     --nv-accent-soft: #ffe4d4;
-    --nv-accent-teal: #9fcfd1;
     --nv-text-main: #262626;
     --nv-text-muted: #6c6459;
 }
 
+/* page */
 .gradio-container {
     font-family: system-ui, -apple-system, BlinkMacSystemFont, "SF Pro Text", "Segoe UI", sans-serif;
     background: radial-gradient(circle at top left, #fff6ee 0, #f7f2ec 50%, #f2ece6 100%);
@@ -421,7 +516,7 @@ CUSTOM_CSS = """
     position: relative;
 }
 
-/* animated blobs */
+/* floating blobs */
 @keyframes nvBlobFloat {
     0%   { transform: translate3d(0, 0, 0) scale(1);   opacity: 0.6; }
     50%  { transform: translate3d(10px, -8px, 0) scale(1.04); opacity: 0.9; }
@@ -450,7 +545,7 @@ CUSTOM_CSS = """
     animation-delay: 4s;
 }
 
-/* main shell */
+/* main panel */
 @keyframes nvCardIn {
     0%   { opacity: 0; transform: translateY(8px) scale(0.98); }
     100% { opacity: 1; transform: translateY(0)   scale(1); }
@@ -517,7 +612,7 @@ CUSTOM_CSS = """
     transform: translateY(-1px);
 }
 
-/* button */
+/* button (breathing) */
 @keyframes nvPulse {
     0%   { transform: translateY(0) scale(1);   box-shadow: 0 10px 22px rgba(243, 138, 107, 0.30); }
     50%  { transform: translateY(-1px) scale(1.02); box-shadow: 0 14px 26px rgba(243, 138, 107, 0.40); }
@@ -539,39 +634,56 @@ CUSTOM_CSS = """
     animation-duration: 1.4s;
 }
 
-/* cards */
-@keyframes nvCardFloat {
-    0%   { transform: translateY(0); }
-    50%  { transform: translateY(-2px); }
-    100% { transform: translateY(0); }
-}
+/* cards grid */
 .nv-card-grid {
     display: grid;
     grid-template-columns: repeat(auto-fit, minmax(170px, 1fr));
     gap: 10px;
 }
+
+/* deal animation */
+@keyframes nvCardDeal {
+    0%   { opacity: 0; transform: translate3d(0, 0, 0) scale(0.7); }
+    100% { opacity: 1; transform: translate3d(0, 0, 0) scale(1); }
+}
 .nv-card {
-    background: #ffffff;
     border-radius: 18px;
     padding: 10px 11px;
     font-size: 0.9rem;
     border: 1px solid rgba(0,0,0,0.03);
     box-shadow: 0 6px 14px rgba(15, 23, 42, 0.06);
-    animation: nvCardFloat 6s ease-in-out infinite;
+    opacity: 0;
+    transform: scale(0.7);
+    animation: nvCardDeal 420ms ease-out forwards;
+}
+
+/* color-coded by category */
+.nv-card--cat-alimentation {
+    background: radial-gradient(circle at 0 0, #e3f7df 0, #ffffff 60%);
+    border-color: #c3e5bc;
 }
-.nv-card[data-kind="question"] {
-    background: radial-gradient(circle at 0 0, #f9ecff 0, #ffffff 60%);
-    border-color: #e0d2f5;
+.nv-card--cat-mouvement {
+    background: radial-gradient(circle at 0 0, #dbeeff 0, #ffffff 60%);
+    border-color: #b9d8ff;
 }
-.nv-card[data-kind="micro"] {
-    background: radial-gradient(circle at 0 0, #e6f7f8 0, #ffffff 60%);
-    border-color: #cfe6e6;
+.nv-card--cat-cerveau {
+    background: radial-gradient(circle at 0 0, #ffd6d6 0, #ffffff 60%);
+    border-color: #f7b6b6;
 }
+.nv-card--cat-liens {
+    background: radial-gradient(circle at 0 0, #ffead1 0, #ffffff 60%);
+    border-color: #f6d2a3;
+}
+.nv-card--cat-bien-etre {
+    background: radial-gradient(circle at 0 0, #ffe1f2 0, #ffffff 60%);
+    border-color: #f3c3e4;
+}
+
 .nv-card-title {
     font-size: 0.72rem;
     letter-spacing: 0.14em;
     text-transform: uppercase;
-    color: #9a8fb6;
+    color: #7c6a9a;
     margin-bottom: 4px;
 }
 
@@ -604,20 +716,57 @@ def _map_category(choice: str) -> str:
     return mapping.get(choice, "alimentation")
 
 
-def update_cards(lang: str, category_choice: str, variant: str):
-    cat_key = _map_category(category_choice)
-    out = ai_generate(lang, cat_key, variant)
-    qs = out["questions"]
-    ms = out["micro_actions"]
+def _card_html(category_key: str, kind: str, title: str, body: str, delay_s: float) -> str:
+    kind_attr = "question" if kind == "q" else "micro"
+    cat_class = f"nv-card--cat-{category_key}"
+    # each card has its own animation delay => "dealing" feel
+    return (
+        f"<div class='nv-card {cat_class}' data-kind='{kind_attr}' "
+        f"style='animation-delay:{delay_s:.2f}s'><div class='nv-card-title'>{title}</div>"
+        f"<div>{body}</div></div>"
+    )
 
-    def card_html(kind: str, title: str, body: str) -> str:
-        kind_attr = "question" if kind == "q" else "micro"
-        return f"<div class='nv-card' data-kind='{kind_attr}'><div class='nv-card-title'>{title}</div><div>{body}</div></div>"
 
-    q_cards = [card_html("q", f"Question {i+1}", qs[i] if i < len(qs) else "") for i in range(4)]
-    m_cards = [card_html("m", f"Micro-action {i+1}", ms[i] if i < len(ms) else "") for i in range(2)]
+def update_cards(lang: str, category_choice: str, variant: str, seen: List[str]):
+    category_key = _map_category(category_choice)
+    result = get_questions_and_micro(lang, category_key, variant, seen or [])
+    questions = result["questions"]
+    micro = result["micro_actions"]
+    raw_json = result["raw_json"]
+    new_seen = result["seen"]
+
+    # Stagger cards a bit
+    delays_q = [0.05, 0.10, 0.15, 0.20]
+    delays_m = [0.25, 0.30]
+
+    q_htmls = []
+    for i in range(4):
+        text = questions[i] if i < len(questions) else ""
+        q_htmls.append(
+            _card_html(
+                category_key,
+                "q",
+                f"Question {i+1}",
+                text,
+                delays_q[i],
+            )
+        )
+
+    m_htmls = []
+    for i in range(2):
+        text = micro[i] if i < len(micro) else ""
+        m_htmls.append(
+            _card_html(
+                category_key,
+                "m",
+                f"Micro-action {i+1}",
+                text,
+                delays_m[i],
+            )
+        )
+
+    return (*q_htmls, *m_htmls, raw_json, new_seen)
 
-    return (*q_cards, *m_cards, out["raw_json"])
 
 # ────────────────────────────────────────────────────────────────────────────────
 # GRADIO APP
@@ -626,6 +775,8 @@ def update_cards(lang: str, category_choice: str, variant: str):
 with gr.Blocks(title="Neurovie – Question Studio") as demo:
     gr.HTML(f"<style>{CUSTOM_CSS}</style>")
 
+    seen_state = gr.State([])  # per-session list of seen questions
+
     with gr.Column(elem_classes="nv-shell"):
         gr.HTML(
             """
@@ -633,7 +784,7 @@ with gr.Blocks(title="Neurovie – Question Studio") as demo:
               <div class="nv-badge">NEUROVIE · FINGER</div>
               <div class="nv-title">Question Studio</div>
               <div class="nv-subtitle">
-                Minimal prompts for rich conversations — draw 4 questions and 2 micro-actions.
+                Minimal prompts for rich conversations — 4 questions and 2 micro-actions par tirage.
               </div>
             </div>
             """
@@ -701,8 +852,8 @@ with gr.Blocks(title="Neurovie – Question Studio") as demo:
 
     btn.click(
         update_cards,
-        [lang, category, variant],
-        [q1, q2, q3, q4, m1, m2, raw_json],
+        [lang, category, variant, seen_state],
+        [q1, q2, q3, q4, m1, m2, raw_json, seen_state],
     )
 
 if __name__ == "__main__":