Fixed Layout

README.md

@@ -5,7 +5,7 @@ colorFrom: indigo
 colorTo: blue
 sdk: gradio
 sdk_version: "4.38.0"
-app_file: storefront_tabs_memory_app.py
+app_file: app_storefront.py
 pinned: false
 license: mit
 short_description: Test for the project front-end.

storefront_tabs_app.py → app_storefront.py

@@ -1,123 +1,25 @@
-import os, json
+# app_storefront.py
+import os
 import gradio as gr
-from transformers import pipeline
-
-# ---------------- Model (same pipeline you used) ----------------
-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(message, max_new_tokens=128, temperature=0.8, top_p=0.95):
-    out = _get_pipe()(
-        message,
-        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:
-    # If you keep these 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: look for JSON near this file or under agenticcore/
-    for p in [
-        os.path.join(os.path.dirname(__file__), "storefront_data.json"),
-        os.path.join(os.path.dirname(__file__), "agenticcore", "storefront_data.json"),
-    ]:
-        if os.path.exists(p):
-            with open(p, "r", encoding="utf-8") as f:
-                STORE_DATA = json.load(f)
-            break
-
-# Defaults if JSON is 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."]
-
-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)
-        DEFAULT_VENUE   = STORE_DATA.get("policies", {}).get("venue_rules", DEFAULT_VENUE)
-    except Exception:
-        pass
-
-
-# ---------------- Routing: storefront first, then LLM ----------------
-def storefront_qna(text: str) -> str | None:
-    t = (text or "").lower().strip()
-    if not t:
-        return None
-
-    # Prefer your 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)
-        # Extra: arrival timing for parking lots
-        if "parking" in t and ("hours" in t or "time" 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 ("multiple" in t or "more than one" 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):
-        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(message, max_new_tokens=128, temperature=0.8, top_p=0.95):
-    """Intercept storefront topics first; else generate with the model."""
+
+from model_core import model_generate, MODEL_NAME
+from data_storefront import (
+    DEFAULT_PRODUCTS, DEFAULT_PARKING, DEFAULT_VENUE,
+    STORE_DATA, USE_HELPERS
+)
+from qna_router import storefront_qna
+from memory_core import build_prompt_from_history, clean_generation
+
+# ---------------- Chat logic ----------------
+def chat_pipeline(history, message, max_new_tokens=128, temperature=0.8, top_p=0.95):
     sf = storefront_qna(message)
     if sf:
         return sf
-    return model_generate(message, max_new_tokens, temperature, top_p)
+    prompt = build_prompt_from_history(history, message, k=4)
+    gen = model_generate(prompt, max_new_tokens, temperature, top_p)
+    return clean_generation(gen)
 
-
-# ---------------- Gradio UI (Tabs + Accordion) ----------------
+# ---------------- UI ----------------
 CSS = """
 :root { --bg:#0b0d12; --panel:#0f172a; --border:#1f2940; --text:#e5e7eb; --muted:#9ca3af; }
 .gradio-container { background: var(--bg) !important; color: var(--text) !important; }
@@ -128,49 +30,51 @@ CSS = """
 with gr.Blocks(title="Storefront Chat", css=CSS) as demo:
     gr.Markdown("## Storefront Chat")
 
+    # One state object that mirrors the Chatbot value
+    history_state = gr.State([])
+
     with gr.Tabs():
-        # --- TAB 1: Chat (sliders tucked away) ---
+        # --- Chat tab ---
         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 chat options in an Accordion
+                # Advanced area (sliders + health/capabilities) — off the main screen
                 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")
 
-                # Simple health & capabilities as small buttons
-                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"])
+                    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 ---
+        # --- Products tab ---
         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")
+            gr.Dataframe(headers=cols, value=data, interactive=False, wrap=True, label="Products")
 
-        # --- TAB 3: Rules ---
+        # --- Rules tab ---
         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 ---
+        # --- Logistics tab ---
         with gr.TabItem("Logistics"):
             gr.Markdown(
                 "### Event Logistics\n"
@@ -181,45 +85,52 @@ with gr.Blocks(title="Storefront Chat", css=CSS) as demo:
                 "\n*Try asking the bot:* “What time should I arrive?” • “Where do I pick up the gown?”"
             )
 
-    # ---- Wiring ----
+    # ---------- Helpers that keep Chatbot history valid ----------
+    def _append_bot_md(history, md_text):
+        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 = history + [[t, None]]
-        reply = chat_pipeline(t, max_new_tokens, temperature, top_p)
+            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 history, ""
+        return history, history, ""
 
-    send.click(on_send, [chat, msg, max_new, temp, topp], [chat, msg])
-    msg.submit(on_send, [chat, msg, max_new, temp, topp], [chat, msg])
+    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"
 
-    # Quick chips → prefill textbox
-    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
-    def ui_health_check():
-        return (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'}")
-
-    def ui_capabilities():
+    def _caps_cb(history):
         caps = [
-            "Chat (LLM text-generation)",
+            "Chat (LLM text-generation, memory-aware prompt)",
             "Storefront Q&A (parking, attire, products, logistics)",
             "Adjustable: max_new_tokens, temperature, top-p",
         ]
-        return "### Capabilities\n- " + "\n- ".join(caps)
+        md = "### Capabilities\n- " + "\n- ".join(caps)
+        new_hist = _append_bot_md(history, md)
+        return new_hist, new_hist
+
+    # Wire up (update both state and Chatbot)
+    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])
 
-    def _health():
-        return ui_health_check(), "Status: ✅ Healthy"
+    # 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_btn.click(_health, outputs=[chat, status_md])
-    caps_btn.click(ui_capabilities, outputs=chat)
+    # Health / Capabilities (now inside the Advanced accordion)
+    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")))

core/memory.py

@@ -0,0 +1,20 @@
+def build_prompt_from_history(history, user_text, k=4) -> str:
+    """
+    history is a list of [user, bot] pairs (Gradio Chatbot format).
+    We embed short storefront reminders to reduce hallucinations.
+    """
+    lines = [
+        "You are answering questions about a university graduation storefront.",
+        "Products:",
+        "- Cap & Gown Set (CG-SET) — $59.00: Tassel included; ships until 10 days before the event",
+        "- Parking Pass (PK-1) — $10.00: Multiple passes allowed per student",
+        "Venue rules: Formal attire recommended (not required). No muscle shirts. No sagging pants.",
+        "Parking rules: No double parking. Vehicles parked in handicap spaces will be towed.",
+        "Answer concisely using these facts. If unsure, say what’s known from the list above."
+    ]
+    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)

core/model.py

@@ -0,0 +1,22 @@
+import os
+from transformers import pipeline
+
+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: str, max_new_tokens=128, temperature=0.8, top_p=0.95) -> str:
+    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"]

core/storefront.py

@@ -0,0 +1,96 @@
+import json, os
+
+def _find_json():
+    candidates = [
+        os.path.join(os.getcwd(), "storefront_data.json"),
+        os.path.join(os.getcwd(), "agenticcore", "storefront_data.json"),
+    ]
+    for p in candidates:
+        if os.path.exists(p):
+            return p
+    return None
+
+def load_storefront():
+    p = _find_json()
+    if not p:
+        return None
+    with open(p, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+def _string_in_any(s, variants):
+    s = s.lower()
+    return any(v in s for v in variants)
+
+def answer_faq(data, text: str):
+    """Very small FAQ matcher by substring; safe if faq[] missing."""
+    faq = (data or {}).get("faq") or []
+    t = text.lower()
+    for item in faq:
+        qs = item.get("q") or []
+        if any(q.lower() in t for q in qs):
+            return item.get("a")
+    return None
+
+def extract_products(data):
+    prods = []
+    for p in (data or {}).get("products", []):
+        prods.append({
+            "sku": p.get("sku",""),
+            "name": p.get("name",""),
+            "price": p.get("price_usd",""),
+            "notes": (p.get("description") or "")[:140],
+        })
+    return prods
+
+def get_rules(data):
+    pol = (data or {}).get("policies", {}) or {}
+    return pol.get("venue_rules", []), pol.get("parking_rules", [])
+
+def storefront_qna(data, user_text: str):
+    """
+    Lightweight rules: try exact single-word intents first, then faq,
+    then rules/products lookup. Return None to allow LLM fallback.
+    """
+    if not user_text:
+        return None
+    t = user_text.strip().lower()
+
+    # single-word catches
+    if t in {"parking"}:
+        _, pr = get_rules(data)
+        if pr: return "Parking rules:\n- " + "\n- ".join(pr)
+    if t in {"venue", "attire", "dress", "dress code"}:
+        vr, _ = get_rules(data)
+        if vr: return "Venue rules:\n- " + "\n- ".join(vr)
+    if t in {"passes", "parking pass", "parking passes"}:
+        return "Yes, multiple parking passes are allowed per student."
+
+    # faq
+    a = answer_faq(data, t)
+    if a: return a
+
+    # explicit rule asks
+    if "parking" in t and "rule" in t:
+        _, pr = get_rules(data)
+        if pr: return "Parking rules:\n- " + "\n- ".join(pr)
+    if ("venue" in t and "rule" in t) or "attire" in t or "dress code" in t:
+        vr, _ = get_rules(data)
+        if vr: return "Venue rules:\n- " + "\n- ".join(vr)
+
+    # “lots open” style
+    if "parking" in t and ("hours" in t or "time" in t or "open" in t):
+        lots_open = (((data or {}).get("logistics") or {}).get("lots_open_hours_before") or 2)
+        return f"Parking lots open {lots_open} hours before the ceremony."
+
+    # products
+    if "cap" in t or "gown" in t or "parking pass" in t or "product" in t:
+        prods = extract_products(data)
+        if prods:
+            lines = []
+            for p in prods:
+                price = p["price"]
+                price_str = f"${price:.2f}" if isinstance(price, (int,float)) else str(price)
+                lines.append(f"{p['name']} — {price_str}: {p['notes']}")
+            return "\n".join(lines)
+
+    return None

storefront_app.py

@@ -1,231 +0,0 @@
-import os, json
-import gradio as gr
-from transformers import pipeline
-
-# ---------------- Model (unchanged from your demo) ----------------
-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(message, max_new_tokens=128, temperature=0.8, top_p=0.95):
-    pipe = _get_pipe()
-    out = pipe(
-        message,
-        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 (prefers your helper module) ----------------
-# First try to import your module (recommended location)
-STORE_DATA = None
-USE_HELPERS = False
-try:
-    from agenticcore.storefront_rules import (
-        load_storefront, answer_faq, get_parking_rules, get_venue_rules, search_products
-    )
-    STORE_DATA = load_storefront()      # loads agenticcore/storefront_data.json by default
-    USE_HELPERS = True
-except Exception:
-    # Fallback: try JSON next to this file or under agenticcore/
-    CANDIDATE_JSON = [
-        os.path.join(os.path.dirname(__file__), "storefront_data.json"),
-        os.path.join(os.path.dirname(__file__), "agenticcore", "storefront_data.json"),
-    ]
-    for p in CANDIDATE_JSON:
-        if os.path.exists(p):
-            with open(p, "r", encoding="utf-8") as f:
-                STORE_DATA = json.load(f)
-            break
-
-# Defaults (used when no JSON/module found)
-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."]
-
-if STORE_DATA:
-    # Build right-panel tables from JSON schema
-    try:
-        DEFAULT_PRODUCTS = []
-        for p in STORE_DATA.get("products", []):
-            DEFAULT_PRODUCTS.append({
-                "SKU": p.get("sku",""),
-                "Name": p.get("name",""),
-                "Price": p.get("price_usd", ""),
-                "Notes": (p.get("description") or "")[:120],
-            })
-        pr = STORE_DATA.get("policies", {}).get("parking_rules", [])
-        vr = STORE_DATA.get("policies", {}).get("venue_rules", [])
-        DEFAULT_PARKING = pr or DEFAULT_PARKING
-        DEFAULT_VENUE = vr or DEFAULT_VENUE
-    except Exception:
-        pass
-
-def storefront_qna(text: str) -> str | None:
-    """Try to answer with storefront data (FAQ, rules, products)."""
-    t = (text or "").lower().strip()
-    if not t:
-        return None
-
-    # Use your helper functions if available (preferred)
-    if USE_HELPERS and STORE_DATA:
-        a = answer_faq(STORE_DATA, t)
-        if a:
-            return a
-        if "parking rule" in t or ("parking" in t and "rule" in t):
-            rules = get_parking_rules(STORE_DATA)
-            if rules:
-                return "Parking rules:\n- " + "\n- ".join(rules)
-        if "venue rule" in t or "dress code" in t or "attire" in t:
-            rules = get_venue_rules(STORE_DATA)
-            if rules:
-                return "Venue rules:\n- " + "\n- ".join(rules)
-        hits = search_products(STORE_DATA, t)
-        if hits:
-            lines = []
-            for p in hits:
-                price = p.get("price_usd")
-                price_str = f"${price:.2f}" if isinstance(price, (int, float)) else str(price)
-                lines.append(f"{p.get('name','Item')} — {price_str}: {p.get('description','')}")
-            return "\n".join(lines)
-        return None
-
-    # Fallback logic (no helper module)
-    if "parking" in t and ("more than one" in t or "multiple" in t or "extra" in t):
-        return "Yes, multiple parking passes are allowed per student."
-    if "parking rule" in t or ("parking" in t and "rule" in t):
-        return "Parking rules:\n- " + "\n- ".join(DEFAULT_PARKING)
-    if "venue rule" in t or "dress code" in t or "attire" in t:
-        return "Venue rules:\n- " + "\n- ".join(DEFAULT_VENUE)
-    if "cap" in t or "gown" in t:
-        lines = []
-        for p in DEFAULT_PRODUCTS:
-            price = p.get("Price")
-            price_str = f"${price:.2f}" if isinstance(price, (int, float)) else str(price)
-            lines.append(f"{p.get('Name','Item')} — {price_str}: {p.get('Notes','')}")
-        return "\n".join(lines)
-    return None
-
-def chat_pipeline(message, max_new_tokens=128, temperature=0.8, top_p=0.95):
-    sf = storefront_qna(message)
-    if sf:
-        return sf
-    return model_generate(message, max_new_tokens, temperature, top_p)
-
-# ---------------- Gradio UI (storefront look) ----------------
-CUSTOM_CSS = """
-:root { --bg:#0b0d12; --panel:#0f172a; --panel-2:#111827; --border:#1f2940; --text:#e5e7eb; --muted:#9ca3af; }
-.gradio-container { background: var(--bg) !important; color: var(--text) !important; }
-#header, .panel { border:1px solid var(--border); border-radius:16px; background:var(--panel); }
-.small { font-size:12px; color: var(--muted); }
-.badge { font-size:12px; opacity:.9; padding:4px 8px; border:1px solid var(--border); border-radius:999px; background: rgba(255,255,255,.04); }
-"""
-
-with gr.Blocks(title="Storefront Chat • Cap & Gown + Parking", css=CUSTOM_CSS) as demo:
-    with gr.Row(elem_id="header"):
-        gr.Markdown("## Storefront Chat • Cap & Gown + Parking")
-        gr.Markdown("<div class='badge'>Gradio • LLM • Storefront Q&A</div>", elem_classes=["small"])
-
-    with gr.Row():
-        with gr.Column(scale=3):
-            with gr.Group(elem_classes=["panel"]):
-                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"])
-
-                chatbot = gr.Chatbot(value=[], height=360, bubble_full_width=False, avatar_images=(None, None), label="Chat")
-
-                with gr.Row():
-                    msg = gr.Textbox(placeholder="Ask about cap & gown sizes, parking rules, refunds, etc…", 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("Sizing", variant="secondary")
-                    chip5 = gr.Button("Refunds", variant="secondary")
-                    chip6 = gr.Button("Shipping cutoff", variant="secondary")
-
-                with gr.Row():
-                    max_new = gr.Slider(32, 512, 128, 1, label="Max new tokens")
-                with gr.Row():
-                    temp = gr.Slider(0.1, 1.5, 0.8, 0.05, label="Temperature")
-                with gr.Row():
-                    topp = gr.Slider(0.1, 1.0, 0.95, 0.05, label="Top-p")
-
-        with gr.Column(scale=2):
-            with gr.Group(elem_classes=["panel"]):
-                gr.Markdown("### Products")
-                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)
-
-            with gr.Group(elem_classes=["panel"]):
-                gr.Markdown("### Rules (Venue & Parking)")
-                rules_md = gr.Markdown("- " + "\n- ".join(DEFAULT_VENUE + DEFAULT_PARKING))
-
-            with gr.Group(elem_classes=["panel"]):
-                gr.Markdown("### Logistics")
-                gr.Markdown(
-                    "- Shipping available until 10 days before event (typ. 3–5 business days)\n"
-                    "- Pickup: Student Center Bookstore during 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: “What time should I arrive?” or “Where do I pick up the gown?”*"
-                )
-
-    # ---- wiring ----
-    def on_send(history, message, max_new_tokens, temperature, top_p):
-        message = (message or "").strip()
-        if not message:
-            return history, ""
-        history = history + [[message, None]]
-        reply = chat_pipeline(message, max_new_tokens, temperature, top_p)
-        history[-1][1] = reply
-        return history, ""
-
-    send.click(on_send, [chatbot, msg, max_new, temp, topp], [chatbot, msg])
-    msg.submit(on_send, [chatbot, msg, max_new, temp, topp], [chatbot, msg])
-
-    # Quick chips → prefill
-    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: "How do I pick a cap & gown size?", outputs=msg)
-    chip5.click(lambda: "What is the refund policy?", outputs=msg)
-    chip6.click(lambda: "When is the shipping cutoff for cap & gown?", outputs=msg)
-
-    # Health / capabilities
-    def ui_health_check():
-        return (f"### Status: ✅ Healthy\n- 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'}")
-    def ui_capabilities():
-        caps = [
-            "Chat (LLM – text-generation)",
-            "Quick storefront Q&A (parking rules, attire, products)",
-            "Adjustable: max_new_tokens, temperature, top-p",
-        ]
-        return "### Capabilities\n- " + "\n- ".join(caps)
-
-    def _health():
-        return ui_health_check(), "Status: ✅ Healthy"
-
-    health_btn.click(_health, outputs=[chatbot, status_md])
-    caps_btn.click(ui_capabilities, outputs=chatbot)
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=int(os.getenv("PORT", "7860")))

storefront_data.json

@@ -0,0 +1,84 @@
+{
+  "products": [
+    {
+      "sku": "CG-SET",
+      "name": "Cap & Gown Set",
+      "price_usd": 59.0,
+      "description": "Tassel included; ships until 10 days before the event. Sizes available at pickup; exchange allowed on-site if inventory permits."
+    },
+    {
+      "sku": "PK-1",
+      "name": "Parking Pass",
+      "price_usd": 10.0,
+      "description": "One vehicle per pass. Multiple passes are allowed per student for extended family or guests."
+    }
+  ],
+  "policies": {
+    "parking_rules": [
+      "No double parking.",
+      "Vehicles parked in handicap spaces will be towed."
+    ],
+    "venue_rules": [
+      "Formal attire is recommended (not required).",
+      "No muscle shirts.",
+      "No sagging pants."
+    ]
+  },
+  "logistics": {
+    "shipping_cutoff_days": 10,
+    "shipping_window_business_days": "3–5",
+    "pickup_location": "Student Center Bookstore",
+    "arrival_times": {
+      "graduates_minutes_early": 90,
+      "guests_minutes_early": 60
+    },
+    "lots_open_hours_before": 2,
+    "lots": ["A", "B"],
+    "overflow": "As needed"
+  },
+  "faq": [
+    {
+      "q": [
+        "Can I buy multiple parking passes?",
+        "multiple passes",
+        "more than one parking pass",
+        "extra parking pass"
+      ],
+      "a": "Yes, multiple parking passes are allowed per student."
+    },
+    {
+      "q": [
+        "What time do the parking lots open?",
+        "When do lots open",
+        "parking hours",
+        "what time parking"
+      ],
+      "a": "Parking lots open 2 hours before the ceremony."
+    },
+    {
+      "q": [
+        "Is formal attire required?",
+        "dress code",
+        "what should I wear",
+        "attire rules"
+      ],
+      "a": "Formal attire is recommended but not required. No muscle shirts or sagging pants."
+    },
+    {
+      "q": [
+        "Where do I pick up the gown?",
+        "gown pickup",
+        "pickup location"
+      ],
+      "a": "Pickup is at the Student Center Bookstore during the week prior to the event."
+    },
+    {
+      "q": [
+        "Shipping cutoff",
+        "last day to ship",
+        "when is shipping available until"
+      ],
+      "a": "Shipping is available until 10 days before the event. Typical shipping takes 3–5 business days."
+    }
+  ]
+}