Fixed memory.

storefront_tabs_memory_app.py

@@ -0,0 +1,286 @@
+# storefront_tabs_memory_app.py
+import os, json
+import gradio as gr
+from transformers import pipeline
+
+# ---------------- Model ----------------
+MODEL_NAME = os.getenv("HF_MODEL_GENERATION", "distilgpt2")
+_pipe = None
+def _get_pipe():
+    global _pipe
+    if _pipe is None:
+        _pipe = pipeline("text-generation", model=MODEL_NAME)
+    return _pipe
+
+def model_generate(prompt, max_new_tokens=128, temperature=0.8, top_p=0.95):
+    out = _get_pipe()(
+        prompt,
+        max_new_tokens=int(max_new_tokens),
+        do_sample=True,
+        temperature=float(temperature),
+        top_p=float(top_p),
+        pad_token_id=50256,
+    )
+    return out[0]["generated_text"]
+
+
+# ---------------- Storefront knowledge (helper module preferred) ----------------
+STORE_DATA, USE_HELPERS = None, False
+try:
+    # Optional helper module under agenticcore/
+    from agenticcore.storefront_rules import (
+        load_storefront, answer_faq, get_parking_rules, get_venue_rules, search_products
+    )
+    STORE_DATA = load_storefront()
+    USE_HELPERS = True
+except Exception:
+    # Fallback: try JSON next to this file or under agenticcore/
+    CANDIDATES = [
+        os.path.join(os.path.dirname(__file__), "storefront_data.json"),
+        os.path.join(os.path.dirname(__file__), "agenticcore", "storefront_data.json"),
+    ]
+    for p in CANDIDATES:
+        if os.path.exists(p):
+            with open(p, "r", encoding="utf-8") as f:
+                STORE_DATA = json.load(f)
+            break
+
+# Defaults if JSON/module missing
+DEFAULT_PRODUCTS = [
+    {"SKU": "CG-SET", "Name": "Cap & Gown Set", "Price": 59.00, "Notes": "Tassel included; ship until 10 days before event"},
+    {"SKU": "PK-1",   "Name": "Parking Pass",   "Price": 10.00, "Notes": "Multiple passes allowed per student"},
+]
+DEFAULT_PARKING = ["No double parking.", "Vehicles parked in handicap will be towed."]
+DEFAULT_VENUE   = ["Formal attire recommended (not required).", "No muscle shirts.", "No sagging pants."]
+
+# Normalize JSON to tables for the UI
+if STORE_DATA:
+    try:
+        DEFAULT_PRODUCTS = [{
+            "SKU": p.get("sku",""),
+            "Name": p.get("name",""),
+            "Price": p.get("price_usd",""),
+            "Notes": (p.get("description") or "")[:120],
+        } for p in STORE_DATA.get("products", [])]
+        DEFAULT_PARKING = STORE_DATA.get("policies", {}).get("parking_rules", DEFAULT_PARKING) or DEFAULT_PARKING
+        DEFAULT_VENUE   = STORE_DATA.get("policies", {}).get("venue_rules", DEFAULT_VENUE)     or DEFAULT_VENUE
+    except Exception:
+        pass
+
+
+# ---------------- Memory seeding ----------------
+def seed_storefront_facts() -> str:
+    """Small system-like primer injected ahead of chat history to bias the LLM toward storefront truth."""
+    lines = ["You are answering questions about a graduation storefront.",
+             "Products:"]
+    for p in DEFAULT_PRODUCTS:
+        price = p.get("Price")
+        if isinstance(price, (int, float)):
+            price = f"${price:.2f}"
+        lines.append(f"- {p.get('Name','Item')} ({p.get('SKU','')}) — {price}: {p.get('Notes','')}")
+    lines.append("Venue rules:")
+    for r in DEFAULT_VENUE:
+        lines.append(f"- {r}")
+    lines.append("Parking rules:")
+    for r in DEFAULT_PARKING:
+        lines.append(f"- {r}")
+    lines.append("Answer concisely using these facts. If unsure, say what’s known from the list above.")
+    return "\n".join(lines)
+
+SEED_TEXT = seed_storefront_facts()
+
+def build_prompt_from_history(history, user_text, k=4):
+    """history = [[user, bot], ...] — build a short rolling prompt + seed facts."""
+    lines = [SEED_TEXT, "", "Conversation so far:"]
+    for u, b in (history or [])[-k:]:
+        if u: lines.append(f"User: {u}")
+        if b: lines.append(f"Assistant: {b}")
+    lines.append(f"User: {user_text}")
+    lines.append("Assistant:")
+    return "\n".join(lines)
+
+
+# ---------------- Storefront Q&A router (storefront first, then LLM) ----------------
+def storefront_qna(text: str) -> str | None:
+    t = (text or "").lower().strip()
+    if not t:
+        return None
+
+    # Single-word catches to avoid LLM drift
+    if t in {"parking"}:
+        return "Parking rules:\n- " + "\n- ".join(DEFAULT_PARKING)
+    if t in {"venue", "attire", "dress", "dress code"}:
+        return "Venue rules:\n- " + "\n- ".join(DEFAULT_VENUE)
+    if t in {"passes", "parking pass", "parking passes"}:
+        return "Yes, multiple parking passes are allowed per student."
+
+    # Prefer helper functions if available
+    if USE_HELPERS and STORE_DATA:
+        a = answer_faq(STORE_DATA, t)
+        if a:
+            return a
+        if "parking" in t and "rule" in t:
+            r = get_parking_rules(STORE_DATA)
+            if r:
+                return "Parking rules:\n- " + "\n- ".join(r)
+        if ("venue" in t and "rule" in t) or "attire" in t or "dress code" in t:
+            r = get_venue_rules(STORE_DATA)
+            if r:
+                return "Venue rules:\n- " + "\n- ".join(r)
+        # Specific timing phrasing to avoid hallucinated dates
+        if "parking" in t and ("hours" in t or "time" in t or "open" in t):
+            return "Parking lots open 2 hours before the ceremony."
+        hits = search_products(STORE_DATA, t)
+        if hits:
+            return "\n".join(
+                f"{p.get('name','Item')} — ${p.get('price_usd',0):.2f}: {p.get('description','')}"
+                for p in hits
+            )
+        return None
+
+    # Fallback rules (no helper module)
+    if "parking" in t and ("more than one" in t or "multiple" in t or "extra" in t):
+        return "Yes, multiple parking passes are allowed per student."
+    if "parking" in t and "rule" in t:
+        return "Parking rules:\n- " + "\n- ".join(DEFAULT_PARKING)
+    if "parking" in t and ("hours" in t or "time" in t or "open" in t):
+        return "Parking lots open 2 hours before the ceremony."
+    if "attire" in t or "dress code" in t or ("venue" in t and "rule" in t):
+        return "Venue rules:\n- " + "\n- ".join(DEFAULT_VENUE)
+    if "cap" in t or "gown" in t:
+        return "\n".join(
+            f"{p['Name']} — ${p['Price']:.2f}: {p['Notes']}"
+            for p in DEFAULT_PRODUCTS
+        )
+    return None
+
+
+def chat_pipeline(history, message, max_new_tokens=128, temperature=0.8, top_p=0.95):
+    # 1) Try storefront knowledge first
+    sf = storefront_qna(message)
+    if sf:
+        return sf
+    # 2) Memory-aware model prompt
+    prompt = build_prompt_from_history(history, message, k=4)
+    return model_generate(prompt, max_new_tokens, temperature, top_p)
+
+
+# ---------------- Gradio UI (Tabs + Accordion) ----------------
+CSS = """
+:root { --bg:#0b0d12; --panel:#0f172a; --border:#1f2940; --text:#e5e7eb; --muted:#9ca3af; }
+.gradio-container { background: var(--bg) !important; color: var(--text) !important; }
+.panel { border:1px solid var(--border); border-radius:16px; background:var(--panel); }
+.small { font-size:12px; color: var(--muted); }
+"""
+
+with gr.Blocks(title="Storefront Chat", css=CSS) as demo:
+    gr.Markdown("## Storefront Chat")
+
+    # Keep a single source of truth for chat history (pairs of [user, bot])
+    history_state = gr.State([])
+
+    with gr.Tabs():
+        # --- TAB 1: Chat (sliders tucked into an accordion) ---
+        with gr.TabItem("Chat"):
+            with gr.Group(elem_classes=["panel"]):
+                chat = gr.Chatbot(height=360, bubble_full_width=False, label="Chat")
+
+                with gr.Row():
+                    msg = gr.Textbox(placeholder="Ask about parking rules, attire, cap & gown, pickup times…", scale=5)
+                    send = gr.Button("Send", scale=1)
+
+                # Quick chips
+                with gr.Row():
+                    chip1 = gr.Button("Parking rules", variant="secondary")
+                    chip2 = gr.Button("Multiple passes", variant="secondary")
+                    chip3 = gr.Button("Attire", variant="secondary")
+                    chip4 = gr.Button("When do lots open?", variant="secondary")
+
+                # Advanced options hidden
+                with gr.Accordion("Advanced chat options", open=False):
+                    max_new = gr.Slider(32, 512, 128, 1, label="Max new tokens")
+                    temp    = gr.Slider(0.1, 1.5, 0.8, 0.05, label="Temperature")
+                    topp    = gr.Slider(0.1, 1.0, 0.95, 0.05, label="Top-p")
+
+                # Small utilities
+                with gr.Row():
+                    health_btn = gr.Button("Health", variant="secondary")
+                    caps_btn   = gr.Button("Capabilities", variant="secondary")
+                    status_md  = gr.Markdown("Status: not checked", elem_classes=["small"])
+
+        # --- TAB 2: Products ---
+        with gr.TabItem("Products"):
+            gr.Markdown("### Available Items")
+            cols = list(DEFAULT_PRODUCTS[0].keys()) if DEFAULT_PRODUCTS else ["SKU","Name","Price","Notes"]
+            data = [[p.get(c,"") for c in cols] for p in DEFAULT_PRODUCTS]
+            _products_tbl = gr.Dataframe(headers=cols, value=data, interactive=False, wrap=True, label="Products")
+
+        # --- TAB 3: Rules ---
+        with gr.TabItem("Rules"):
+            gr.Markdown("### Venue rules")
+            gr.Markdown("- " + "\n- ".join(DEFAULT_VENUE))
+            gr.Markdown("### Parking rules")
+            gr.Markdown("- " + "\n- ".join(DEFAULT_PARKING))
+
+        # --- TAB 4: Logistics ---
+        with gr.TabItem("Logistics"):
+            gr.Markdown(
+                "### Event Logistics\n"
+                "- Shipping available until 10 days before event (typ. 3–5 business days)\n"
+                "- Pickup: Student Center Bookstore during the week prior to event\n"
+                "- Graduates arrive 90 minutes early; guests 60 minutes early\n"
+                "- Lots A & B open 2 hours before; overflow as needed\n"
+                "\n*Try asking the bot:* “What time should I arrive?” • “Where do I pick up the gown?”"
+            )
+
+    # ---------- Helpers that keep Chatbot history valid (list of [u,b]) ----------
+    def _append_bot_md(history, md_text):
+        """Append a bot markdown message without breaking the [user, bot] format."""
+        history = history or []
+        return history + [[None, md_text]]
+
+    # ---------- Callbacks ----------
+    def on_send(history, message, max_new_tokens, temperature, top_p):
+        t = (message or "").strip()
+        if not t:
+            return history, history, ""  # no-op
+        history = (history or []) + [[t, None]]
+        reply = chat_pipeline(history[:-1], t, max_new_tokens, temperature, top_p)
+        history[-1][1] = reply
+        # Return updated state AND what the Chatbot should render
+        return history, history, ""
+
+    def _health_cb(history):
+        md = (f"### Status: ✅ Healthy\n"
+              f"- Model: `{MODEL_NAME}`\n"
+              f"- Storefront module: {'yes' if USE_HELPERS else 'no'}\n"
+              f"- Storefront JSON: {'loaded' if bool(STORE_DATA) else 'not found'}")
+        new_hist = _append_bot_md(history, md)
+        return new_hist, new_hist, "Status: ✅ Healthy"
+
+    def _caps_cb(history):
+        caps = [
+            "Chat (LLM text-generation, memory-aware prompt)",
+            "Storefront Q&A (parking, attire, products, logistics)",
+            "Adjustable: max_new_tokens, temperature, top-p",
+        ]
+        md = "### Capabilities\n- " + "\n- ".join(caps)
+        new_hist = _append_bot_md(history, md)
+        return new_hist, new_hist
+
+    # Wire up (note: always update both the state and the Chatbot value)
+    send.click(on_send, [history_state, msg, max_new, temp, topp], [history_state, chat, msg])
+    msg.submit(on_send, [history_state, msg, max_new, temp, topp], [history_state, chat, msg])
+
+    # Chips
+    chip1.click(lambda: "What are the parking rules?", outputs=msg)
+    chip2.click(lambda: "Can I buy multiple parking passes?", outputs=msg)
+    chip3.click(lambda: "Is formal attire required?", outputs=msg)
+    chip4.click(lambda: "What time do the parking lots open?", outputs=msg)
+
+    # Health / capabilities (append to chat, keep valid tuple format)
+    health_btn.click(_health_cb, inputs=[history_state], outputs=[history_state, chat, status_md])
+    caps_btn.click(_caps_cb, inputs=[history_state], outputs=[history_state, chat])
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=int(os.getenv("PORT", "7860")))