# app_storefront.py import os import sys import gradio as gr # Ensure "core/" is importable sys.path.append(os.path.join(os.path.dirname(__file__), "core")) # Import only functions; core.storefront doesn't export constants from core.model import model_generate, MODEL_NAME from core.memory import build_prompt_from_history from core.storefront import load_storefront, storefront_qna, extract_products, get_rules from core.storefront import is_storefront_query def chat_pipeline(history, message, max_new_tokens=96, temperature=0.7, top_p=0.9): # 1) Try storefront facts first sf = storefront_qna(DATA, message) if sf: return sf # 2) If not a storefront query, offer guided help (no LLM) if not is_storefront_query(message): return ( "I can help with the graduation storefront. Examples:\n" "- Parking rules, lots opening times\n" "- Attire / dress code\n" "- Cap & Gown details and pickup\n" "- Parking passes (multiple allowed)\n" "Ask one of those, and I’ll answer directly." ) # 3) Otherwise, generate with memory and hard stops prompt = build_prompt_from_history(history, message, k=4) gen = model_generate(prompt, max_new_tokens, temperature, top_p) return clean_generation(gen) def clean_generation(text: str) -> str: return (text or "").strip() # ---------------- Load data + safe fallbacks ---------------- DATA = load_storefront() # may be None if storefront_data.json missing/empty # Fallbacks used if JSON not present FALLBACK_PRODUCTS = [ {"sku": "CG-SET", "name": "Cap & Gown Set", "price": 59.00, "notes": "Tassel included; ships until 10 days before the event"}, {"sku": "PK-1", "name": "Parking Pass", "price": 10.00, "notes": "Multiple passes are allowed per student"} ] FALLBACK_VENUE = [ "Formal attire recommended (not required).", "No muscle shirts.", "No sagging pants." ] FALLBACK_PARKING = [ "No double parking.", "Vehicles parked in handicap spaces will be towed." ] # Normalize products/rules for the tabs if DATA: PRODUCTS = extract_products(DATA) or FALLBACK_PRODUCTS venue_rules, parking_rules = get_rules(DATA) VENUE_RULES = venue_rules or FALLBACK_VENUE PARKING_RULES = parking_rules or FALLBACK_PARKING else: PRODUCTS = FALLBACK_PRODUCTS VENUE_RULES = FALLBACK_VENUE PARKING_RULES = FALLBACK_PARKING # ---------------- UI ---------------- 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") # Single history state (kept in sync with Chatbot) history_state = gr.State([]) with gr.Tabs(): # --- TAB: Chat --- 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 (sliders + Health/Capabilities) 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") 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: Products --- with gr.TabItem("Products"): gr.Markdown("### Available Items") cols = ["sku", "name", "price", "notes"] data = [[p.get(c, "") for c in cols] for p in PRODUCTS] gr.Dataframe(headers=[c.upper() for c in cols], value=data, interactive=False, wrap=True, label="Products") # --- TAB: Rules --- with gr.TabItem("Rules"): gr.Markdown("### Venue rules") gr.Markdown("- " + "\n- ".join(VENUE_RULES)) gr.Markdown("### Parking rules") gr.Markdown("- " + "\n- ".join(PARKING_RULES)) # --- TAB: 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 ---------- 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, "" # no-op; shapes must match history = (history or []) + [[t, None]] reply = chat_pipeline(history[:-1], t, max_new_tokens, temperature, top_p) history[-1][1] = reply return history, history, "" def _health_cb(history): md = ( f"### Status: ✅ Healthy\n" f"- Model: `{MODEL_NAME}`\n" f"- Storefront JSON: {'loaded' if bool(DATA) else 'not found'}" ) new_hist = _append_bot_md(history, md) return new_hist, new_hist, "Status: ✅ Healthy" def _caps_cb(history): md = ( "### Capabilities\n" "- Chat (LLM text-generation, memory-aware prompt)\n" "- Storefront Q&A (parking, attire, products, logistics)\n" "- Adjustable: max_new_tokens, temperature, top-p" ) new_hist = _append_bot_md(history, md) return new_hist, new_hist # Wire up (state + 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]) # 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 live inside Advanced 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]) def clean_generation(text: str) -> str: s = (text or "").strip() # If the prompt contained "Assistant:", keep only what comes after the last one last = s.rfind("Assistant:") if last != -1: s = s[last + len("Assistant:"):].strip() # If it accidentally continued into a new "User:" or instructions, cut there cut_marks = ["\nUser:", "\nYOU ARE ANSWERING", "\nProducts:", "\nVenue rules:", "\nParking rules:"] cut_positions = [s.find(m) for m in cut_marks if s.find(m) != -1] if cut_positions: s = s[:min(cut_positions)].strip() # Collapse repeated lines like "Yes, multiple parking passes..." spam lines, out = s.splitlines(), [] seen = set() for ln in lines: # dedupe only exact consecutive repeats; keep normal conversation lines if not out or ln != out[-1]: out.append(ln) return "\n".join(out).strip() if __name__ == "__main__": demo.launch(server_name="0.0.0.0", server_port=int(os.getenv("PORT", "7860")))