| import os |
| import json |
| import re |
| import pandas as pd |
| import gradio as gr |
|
|
| from inference_core import run_full_inference |
| from fe_avm_bdo26 import location_map_dict |
| from map_builder import generate_area_map_safe as generate_area_map, generate_full_bandung_map_safe as generate_full_bandung_map |
|
|
| from chatbot_core import chat, EMPTY_STATE |
|
|
| |
| |
| |
| client = None |
| gemini_available = False |
|
|
| try: |
| from google import genai |
|
|
| api_key = os.getenv("GEMINI_API_KEY") |
|
|
| if api_key: |
| client = genai.Client(api_key=api_key) |
| gemini_available = True |
| print("Gemini client initialized.") |
| else: |
| print("No GEMINI_API_KEY found. Using fallback parser only.") |
|
|
| except Exception as e: |
| print("Gemini init error:", e) |
| client = None |
| gemini_available = False |
|
|
| print("GEMINI AVAILABLE:", gemini_available) |
| print("CLIENT IS NONE:", client is None) |
| print("ENV KEY FOUND:", os.getenv("GEMINI_API_KEY") is not None) |
| |
| |
| |
| |
| def safe_float(x): |
| if x is None or str(x).strip() == "": |
| return None |
| return float(x) |
|
|
|
|
| def safe_int(x): |
| if x is None or str(x).strip() == "": |
| return None |
| return int(float(x)) |
|
|
|
|
| |
| |
| |
| def fallback_parse_notes(user_notes): |
| if not user_notes: |
| return {} |
|
|
| text = user_notes.lower() |
| parsed = {} |
|
|
| |
| known_locations = sorted( |
| location_map_dict.keys(), |
| key=len, |
| reverse=True |
| ) |
|
|
| for loc in known_locations: |
| if loc.lower() in text: |
| parsed["location"] = loc |
| break |
| |
| m = re.search(r"(\d+)\s*bed", text) |
| if m: |
| parsed["bedroom_total"] = int(m.group(1)) |
|
|
| m = re.search(r"(\d+)\s*bath", text) |
| if m: |
| parsed["bathroom_total"] = int(m.group(1)) |
|
|
| m = re.search(r"(\d+)\s*car", text) |
| if m: |
| parsed["carport_total"] = int(m.group(1)) |
|
|
| |
| budget_patterns = [ |
| r"(?:budget|harga|max|under|below|dibawah|di bawah|sekitar|around)\s*(?:rp\s*)?(\d+(?:\.\d+)?)\s*(m|miliar|billion|jt|juta)?", |
| r"(?:rp\s*)?(\d+(?:\.\d+)?)\s*(m|miliar|billion|jt|juta)\b" |
| ] |
|
|
| budget_val = None |
| unit = "" |
|
|
| for pattern in budget_patterns: |
| m = re.search(pattern, text) |
| if m: |
| budget_val = float(m.group(1)) |
| unit = (m.group(2) or "").lower() |
| break |
|
|
| if budget_val is not None: |
| if unit in ["m", "miliar", "billion"]: |
| budget_val *= 1000 |
| elif unit in ["jt", "juta"]: |
| budget_val = budget_val |
| elif budget_val < 10: |
| budget_val *= 1000 |
|
|
| parsed["budget_million_idr"] = budget_val |
| |
| return parsed |
|
|
|
|
| |
| |
| |
| def parse_preferences_with_gemini(user_notes, active_client): |
| if not user_notes: |
| return {} |
|
|
| fallback = fallback_parse_notes(user_notes) |
| parsed = {} |
|
|
| if active_client is None: |
| return fallback |
|
|
| try: |
| prompt = f""" |
| Extract housing preferences from this text. |
| |
| Return ONLY valid JSON. DO NOT explain anything. Keep concise. Exact location from Bandung where possible. Correct OBVIOUS typos and use right caps. e.g: sayap dago into Sayap Dago, daog Pakar into Dago Pakar. |
| |
| Budget rules: |
| - if budget number is below 10, assume it is in billions |
| - convert to million IDR |
| - example: 1 = 1000, 1.5 = 1500, 9.9 = 9900 |
| - words like under, below, max mean budget |
| |
| Use keys: |
| location |
| building_type |
| bedroom_total |
| bathroom_total |
| carport_total |
| budget_million_idr |
| building_area_m2 |
| land_area_m2 |
| |
| Text: |
| {user_notes} |
| """ |
|
|
| model_candidates = [ |
| "gemini-2.5-flash-lite" |
| ] |
|
|
| for model_name in model_candidates: |
| try: |
| response = active_client.models.generate_content( |
| model=model_name, |
| contents=prompt, |
| config={ |
| "max_output_tokens": 80, |
| "temperature": 0.1, |
| "top_p": 0.9 |
| } |
| ) |
|
|
| if hasattr(response, "text") and response.text: |
| clean_text = response.text.strip() |
| else: |
| clean_text = str(response).strip() |
|
|
| clean_text = ( |
| clean_text |
| .replace("```json", "") |
| .replace("```", "") |
| .strip() |
| ) |
|
|
| gemini_parsed = json.loads(clean_text) |
|
|
| if isinstance(gemini_parsed, dict): |
| |
| parsed.update(gemini_parsed) |
|
|
| |
| for k, v in fallback.items(): |
| if k not in parsed or parsed[k] in [None, ""]: |
| parsed[k] = v |
|
|
| print(f"GEMINI PARSER SUCCESS: {model_name}") |
| break |
|
|
| except Exception as e: |
| print(f"GEMINI PARSER FAILED {model_name}: {e}") |
|
|
| return parsed if parsed else fallback |
|
|
| except Exception as e: |
| print("Gemini parse error:", e) |
| return parsed |
|
|
|
|
| |
| |
| |
| def make_clickable_links(df): |
| if df is None or df.empty: |
| return """ |
| <div class="listing-empty"> |
| No listings found. |
| </div> |
| """ |
|
|
| df = df.copy() |
|
|
| pretty_cols = { |
| "building_type": "Type", |
| "location": "Location", |
| "price_million_idr": "Price (IDR Mn)", |
| "listing_summary": "Listing Summary" |
| } |
|
|
| df = df.rename(columns=pretty_cols) |
|
|
| if "url" in df.columns: |
| def make_short_clickable_link(x): |
| if pd.isna(x) or str(x).strip() == "": |
| return "" |
|
|
| full_url = str(x).strip() |
|
|
| |
| if not full_url.startswith("http"): |
| full_url = "https://" + full_url |
|
|
| |
| display_text = full_url[:25] + "..." if len(full_url) > 25 else full_url |
|
|
| return f'<a href="{full_url}" target="_blank">{display_text}</a>' |
|
|
| df["Link"] = df["url"].apply(make_short_clickable_link) |
|
|
| df = df.drop(columns=["url"]) |
|
|
| df["Listing Summary"] = df["Listing Summary"].apply( |
| lambda x: str(x)[:90] + "..." if pd.notna(x) and len(str(x)) > 90 else x |
| ) |
|
|
| return df.to_html( |
| escape=False, |
| index=False, |
| classes="listing-table", |
| justify="left" |
| ) |
|
|
|
|
| def wrap_params_card(df): |
| if df is None or df.empty: |
| return "<div class='info-card'>No parameters available.</div>" |
|
|
| row = df.iloc[0].to_dict() |
|
|
| html_parts = ["<div class='info-card'>"] |
| html_parts.append("<div class='info-card-title'>Parameters Used</div>") |
| html_parts.append("<div class='param-grid'>") |
|
|
| for key, val in row.items(): |
| show_val = "—" if pd.isna(val) else val |
| html_parts.append( |
| f""" |
| <div class="param-item"> |
| <div class="param-label">{key}</div> |
| <div class="param-value">{show_val}</div> |
| </div> |
| """ |
| ) |
|
|
| html_parts.append("</div></div>") |
| return "".join(html_parts) |
|
|
|
|
| |
| |
| |
| def predict_housing( |
| user_notes, |
| location, |
| building_type, |
| bedroom_total, |
| bathroom_total, |
| carport_total, |
| building_area_m2, |
| land_area_m2, |
| budget_million_idr |
| ): |
| user_info = { |
| "location": location, |
| "building_type": building_type, |
| "bedroom_total": safe_int(bedroom_total), |
| "bathroom_total": safe_int(bathroom_total), |
| "carport_total": safe_int(carport_total), |
| "building_area_m2": safe_float(building_area_m2), |
| "land_area_m2": safe_float(land_area_m2), |
| "budget_million_idr": safe_float(budget_million_idr), |
| } |
|
|
| |
| for key in ["bedroom_total", "bathroom_total", "carport_total"]: |
| if user_info.get(key) == 0: |
| user_info[key] = None |
|
|
| parsed_notes = parse_preferences_with_gemini( |
| user_notes, |
| client |
| ) |
| print("PARSED NOTES:", parsed_notes) |
|
|
| if user_notes and user_notes.strip(): |
| for key, val in parsed_notes.items(): |
| if user_info.get(key) in [None, "", 0]: |
| if val not in [None, ""]: |
| user_info[key] = val |
|
|
| if user_notes and user_notes.strip(): |
| user_info["notes"] = user_notes |
|
|
| print("FINAL USER INFO:", user_info) |
|
|
| result = run_full_inference( |
| user_info, |
| client=client |
| ) |
|
|
| params_df = pd.DataFrame([result["params_used"]]) |
| params_card_html = wrap_params_card(params_df) |
|
|
| pred = result["prediction"] |
| estimate_md = f""" |
| <div class="estimate-card"> |
| <div class="estimate-title">House Price Estimate</div> |
| <div class="estimate-main">IDR {pred['Predicted Price (IDR Mn)']:.0f} Mn</div> |
| <div class="estimate-subgrid"> |
| <div class="estimate-subitem"> |
| <div class="estimate-sub-label">Lower Bound</div> |
| <div class="estimate-sub-value">IDR {pred['Lower Range']:.0f} Mn</div> |
| </div> |
| <div class="estimate-subitem"> |
| <div class="estimate-sub-label">Upper Bound</div> |
| <div class="estimate-sub-value">IDR {pred['Upper Range']:.0f} Mn</div> |
| </div> |
| </div> |
| </div> |
| """ |
|
|
| map_html = generate_area_map( |
| location=user_info.get("location"), |
| price_mid=round(pred["Predicted Price (IDR Mn)"], -1), |
| price_lo=round(pred["Lower Range"], -1), |
| price_hi=round(pred["Upper Range"], -1) |
| ) |
|
|
| comparison_intro_text = ( |
| f"### If you like **{user_info.get('location')}**, " |
| f"you may also want to consider:" |
| ) |
|
|
| comparison_df = result["comparison_table"].copy() |
| comparison_df = comparison_df.rename( |
| columns={"Desirability Score": "Desirability Score"} |
| ) |
|
|
| target_title = ( |
| f"### Example listings in **{user_info.get('location')}**" |
| ) |
| alt_title = "### Example listings in **alternative areas**" |
|
|
| target_html = make_clickable_links(result["target_listings"]) |
| rec_html = make_clickable_links(result["recommended_listings"]) |
|
|
| target_block = f"{target_title}\n{target_html}" |
| alt_block = f"{alt_title}\n{rec_html}" |
|
|
| summary_md = f""" |
| <div class="summary-card"> |
| {result["summary"]} |
| </div> |
| """ |
|
|
| return ( |
| params_card_html, |
| estimate_md, |
| map_html, |
| result["market_summary"], |
| comparison_intro_text, |
| comparison_df, |
| target_block, |
| alt_block, |
| summary_md |
| ) |
|
|
|
|
| |
| |
| |
| def get_booklet_path(): |
| candidates = [ |
| "booklet_rmt52_boni.pdf", |
| "RMT52_product_booklet.pdf", |
| "RMT52 Booklet.pdf", |
| "RMT52_booklet.pdf", |
| "company_booklet.pdf", |
| "product_booklet.pdf" |
| ] |
|
|
| for path in candidates: |
| if os.path.exists(path): |
| return path |
| return None |
|
|
|
|
| BOOKLET_PATH = get_booklet_path() |
|
|
|
|
| |
| |
| |
| custom_css = """ |
| :root { |
| --rmt-main: #330066; |
| --rmt-text: #142B4D; |
| --rmt-secondary: #4D6A92; |
| --rmt-accent: #660033; |
| --rmt-bg: #FFFFFF; |
| --rmt-soft: #FAF8F4; |
| --rmt-line: #E7EDF3; |
| } |
| |
| .gradio-container { |
| max-width: 1380px !important; |
| margin: auto; |
| background: var(--rmt-bg); |
| } |
| |
| .block-title { |
| background: var(--rmt-main); |
| color: white !important; |
| padding: 14px 18px; |
| border-radius: 14px; |
| font-weight: 700; |
| margin-bottom: 14px; |
| font-size: 18px; |
| } |
| |
| .section-title { |
| color: var(--rmt-text); |
| font-weight: 700; |
| margin-top: 8px; |
| margin-bottom: 6px; |
| } |
| |
| .soft-note { |
| color: var(--rmt-secondary); |
| font-size: 14px; |
| margin-bottom: 10px; |
| } |
| |
| .info-card { |
| background: white; |
| border: 1px solid var(--rmt-line); |
| border-radius: 16px; |
| padding: 16px; |
| box-shadow: 0 2px 8px rgba(20,43,77,0.04); |
| } |
| |
| .info-card-title { |
| color: var(--rmt-text); |
| font-size: 17px; |
| font-weight: 700; |
| margin-bottom: 12px; |
| } |
| |
| .param-grid { |
| display: grid; |
| grid-template-columns: repeat(2, minmax(0, 1fr)); |
| gap: 10px; |
| } |
| |
| .param-item { |
| background: var(--rmt-soft); |
| border: 1px solid var(--rmt-line); |
| border-radius: 12px; |
| padding: 10px 12px; |
| } |
| |
| .param-label { |
| color: var(--rmt-secondary); |
| font-size: 12px; |
| margin-bottom: 4px; |
| } |
| |
| .param-value { |
| color: var(--rmt-text); |
| font-weight: 600; |
| font-size: 14px; |
| } |
| |
| .estimate-card { |
| background: white; |
| border: 1px solid var(--rmt-line); |
| border-top: 6px solid var(--rmt-main); |
| border-radius: 18px; |
| padding: 18px; |
| box-shadow: 0 3px 10px rgba(20,43,77,0.06); |
| } |
| |
| .estimate-title { |
| color: var(--rmt-text); |
| font-size: 16px; |
| font-weight: 700; |
| margin-bottom: 8px; |
| } |
| |
| .estimate-main { |
| color: var(--rmt-main); |
| font-size: 30px; |
| font-weight: 800; |
| margin-bottom: 14px; |
| } |
| |
| .estimate-subgrid { |
| display: grid; |
| grid-template-columns: 1fr 1fr; |
| gap: 10px; |
| } |
| |
| .estimate-subitem { |
| background: var(--rmt-soft); |
| border-radius: 12px; |
| padding: 12px; |
| border: 1px solid var(--rmt-line); |
| } |
| |
| .estimate-sub-label { |
| color: var(--rmt-secondary); |
| font-size: 12px; |
| margin-bottom: 4px; |
| } |
| |
| .estimate-sub-value { |
| color: var(--rmt-text); |
| font-weight: 700; |
| font-size: 15px; |
| } |
| .leaflet-control-container { |
| z-index: 9999 !important; |
| } |
| |
| .leaflet-top, |
| .leaflet-right { |
| z-index: 9999 !important; |
| } |
| |
| .leaflet-control-layers { |
| z-index: 9999 !important; |
| background: white !important; |
| border-radius: 12px !important; |
| } |
| |
| .summary-card { |
| background: #ffffff; |
| border-left: 6px solid var(--rmt-accent); |
| border-radius: 12px; |
| border-top: 1px solid var(--rmt-line); |
| border-right: 1px solid var(--rmt-line); |
| border-bottom: 1px solid var(--rmt-line); |
| padding: 16px; |
| color: var(--rmt-text); |
| line-height: 1.6; |
| } |
| |
| .listing-table { |
| width: 100%; |
| border-collapse: collapse; |
| border-radius: 12px; |
| overflow: hidden; |
| } |
| |
| .listing-table th { |
| background: var(--rmt-secondary); |
| color: white; |
| padding: 10px; |
| text-align: left; |
| white-space: normal !important; |
| } |
| |
| .listing-table td { |
| padding: 10px; |
| border-bottom: 1px solid var(--rmt-line); |
| white-space: normal !important; |
| word-wrap: break-word !important; |
| overflow-wrap: anywhere !important; |
| color: var(--rmt-text); |
| background: white; |
| } |
| |
| .listing-empty { |
| padding: 12px; |
| border: 1px dashed var(--rmt-line); |
| border-radius: 12px; |
| color: var(--rmt-secondary); |
| background: var(--rmt-soft); |
| } |
| |
| footer { |
| text-align: center; |
| color: var(--rmt-secondary); |
| font-size: 12px; |
| margin-top: 18px; |
| } |
| |
| button.primary, |
| button[variant="primary"] { |
| background: var(--rmt-accent) !important; |
| border: none !important; |
| } |
| |
| .gr-tab-nav button.selected { |
| color: var(--rmt-main) !important; |
| border-color: var(--rmt-main) !important; |
| } |
| |
| h1, h2, h3, h4 { |
| color: var(--rmt-text); |
| } |
| |
| /* FULL MAP FIX */ |
| #full-map iframe, |
| #area-map iframe { |
| height: 780px !important; |
| min-height: 780px !important; |
| width: 100% !important; |
| border-radius: 18px; |
| } |
| |
| #full-map, |
| #area-map { |
| min-height: 780px !important; |
| overflow: visible !important; |
| } |
| |
| .leaflet-control-container { |
| z-index: 99999 !important; |
| overflow: visible !important; |
| } |
| |
| .leaflet-top, |
| .leaflet-right, |
| .leaflet-control { |
| z-index: 99999 !important; |
| overflow: visible !important; |
| } |
| |
| .leaflet-control-layers { |
| z-index: 99999 !important; |
| overflow: visible !important; |
| max-height: none !important; |
| } |
| |
| iframe { |
| overflow: visible !important; |
| } |
| """ |
|
|
|
|
| with gr.Blocks( |
| title="RMT52", |
| theme=gr.themes.Soft(), |
| css=custom_css |
| ) as demo: |
|
|
| gr.Markdown(""" |
| # RMT52 |
| ### Bandung Housing Decision Support Tool · 2026 |
| """) |
|
|
| with gr.Tabs(): |
| |
| |
| |
| with gr.Tab("🏡 Smart Valuation"): |
| with gr.Row(): |
| |
| |
| |
| with gr.Column(scale=1): |
| gr.HTML( |
| "<div class='section-title' style='font-size:24px;'>What are you looking for today?</div>" |
| ) |
|
|
| user_notes = gr.Textbox( |
| lines=4, |
| label="", |
| placeholder=( |
| "I’m looking for a 4-bedroom house in Riau " |
| "with a budget around 1.5B, preferably near schools." |
| ) |
| ) |
|
|
| chat_parse_btn = gr.Button( |
| "Find With Chat", |
| variant="primary" |
| ) |
|
|
| gr.Markdown( |
| "<div class='soft-note'><b>OR</b> fill in the form below</div>" |
| ) |
|
|
| gr.HTML("<div class='block-title'>House Price Estimate</div>") |
|
|
| location = gr.Textbox( |
| label="Preferred Location", |
| placeholder="e.g. Riau" |
| ) |
|
|
| building_type = gr.Dropdown( |
| choices=["Rumah", "Apartemen", "Ruko"], |
| label="Building Type", |
| value="Rumah" |
| ) |
|
|
| with gr.Row(): |
| land_area_m2 = gr.Textbox( |
| label="Land Area (m²)", |
| placeholder="Optional" |
| ) |
| building_area_m2 = gr.Textbox( |
| label="Building Area (m²)", |
| placeholder="Optional" |
| ) |
|
|
| bedroom_total = gr.Number( |
| label="Bedrooms", |
| precision=0 |
| ) |
| bathroom_total = gr.Number( |
| label="Bathrooms", |
| precision=0 |
| ) |
| carport_total = gr.Number( |
| label="Carport", |
| precision=0 |
| ) |
|
|
| budget_million_idr = gr.Textbox( |
| label="Budget (IDR Mn)", |
| placeholder="Optional" |
| ) |
|
|
| run_btn = gr.Button( |
| "Estimate Price", |
| variant="primary" |
| ) |
| |
| params_used_output = gr.HTML() |
|
|
| |
| |
| |
| with gr.Column(scale=2): |
| estimate_output = gr.HTML() |
| map_output = gr.HTML(elem_id="area-map") |
|
|
| gr.Markdown("# Outputs") |
|
|
| gr.Markdown("## Market Context") |
| market_summary_output = gr.Markdown() |
|
|
| gr.Markdown("## Area Comparison") |
| comparison_intro = gr.Markdown() |
| comparison_output = gr.Dataframe(wrap=True, label="") |
|
|
| target_listing_output = gr.HTML() |
| rec_listing_output = gr.HTML() |
|
|
| gr.Markdown("## Summary") |
| summary_output = gr.HTML() |
|
|
| run_btn.click( |
| fn=predict_housing, |
| inputs=[ |
| user_notes, |
| location, |
| building_type, |
| bedroom_total, |
| bathroom_total, |
| carport_total, |
| building_area_m2, |
| land_area_m2, |
| budget_million_idr |
| ], |
| outputs=[ |
| params_used_output, |
| estimate_output, |
| map_output, |
| market_summary_output, |
| comparison_intro, |
| comparison_output, |
| target_listing_output, |
| rec_listing_output, |
| summary_output |
| ] |
| ) |
| |
| chat_parse_btn.click( |
| fn=predict_housing, |
| inputs=[ |
| user_notes, |
| location, |
| building_type, |
| bedroom_total, |
| bathroom_total, |
| carport_total, |
| building_area_m2, |
| land_area_m2, |
| budget_million_idr |
| ], |
| outputs=[ |
| params_used_output, |
| estimate_output, |
| map_output, |
| market_summary_output, |
| comparison_intro, |
| comparison_output, |
| target_listing_output, |
| rec_listing_output, |
| summary_output |
| ] |
| ) |
|
|
| |
| |
| |
| with gr.Tab("💬 HomieRMT Chatbot"): |
| gr.Markdown(""" |
| ### 💬 HomieRMT Assistant |
| |
| Ask naturally about: |
| - property price estimates |
| - similar areas |
| - Bandung market context |
| """) |
|
|
| chat_history_box = gr.Markdown( |
| value="**HomieRMT:** Hi! Ask me anything about Bandung housing.\n" |
| ) |
|
|
| chat_input = gr.Textbox( |
| label="Message", |
| placeholder="Ask about housing in Bandung...", |
| lines=2 |
| ) |
|
|
| send_btn = gr.Button("Send", variant="primary") |
|
|
| history_state = gr.State([]) |
| chat_state = gr.State(EMPTY_STATE) |
|
|
| def conversational_chat(msg, transcript, history, state): |
| updated_history, new_state = chat(msg, history, state) |
|
|
| latest_reply = updated_history[-1]["content"] |
|
|
| transcript += f"\n\n**You:** {msg}\n\n**HomieRMT:** {latest_reply}" |
|
|
| return transcript, updated_history, new_state |
|
|
| send_btn.click( |
| fn=conversational_chat, |
| inputs=[chat_input, chat_history_box, history_state, chat_state], |
| outputs=[chat_history_box, history_state, chat_state] |
| ) |
|
|
| chat_input.submit( |
| fn=conversational_chat, |
| inputs=[chat_input, chat_history_box, history_state, chat_state], |
| outputs=[chat_history_box, history_state, chat_state] |
| ) |
| |
| |
| |
| |
| with gr.Tab("🗺️ Bandung Market Map"): |
| gr.Markdown(""" |
| ### Bandung Market Map |
| |
| Explore city-wide housing patterns, facilities, and accessibility layers. |
| Toggle map layers from the control panel on the map. |
| """) |
|
|
| load_full_map_btn = gr.Button( |
| "Load Bandung Market Map", |
| variant="primary" |
| ) |
|
|
| full_map_display = gr.HTML( |
| value="Click the button to load map.", |
| elem_id="full-map" |
| ) |
|
|
| load_full_map_btn.click( |
| fn=generate_full_bandung_map, |
| outputs=full_map_display |
| ) |
| |
| |
| |
| with gr.Tab("ℹ️ About RMT52"): |
| gr.Markdown(""" |
| ## RMT52 |
| |
| RMT52 combines smart valuation, neighborhood intelligence, and interactive market mapping to support better housing decisions in Bandung. |
| |
| ### What it does |
| - smart property valuation |
| - comparable area recommendations |
| - market context and listing examples |
| - interactive Bandung market map |
| - conversational advisor support |
| |
| ### Methodology |
| RMT52 is powered by a calibrated valuation and recommendation stack using location-aware feature engineering and structured market signals from Bandung housing listings. |
| |
| ### Product Booklet |
| Download the product booklet for more information, use cases, and methodology. |
| """) |
|
|
| if BOOKLET_PATH is not None: |
| gr.File( |
| value=BOOKLET_PATH, |
| label="📘 Download the RMT52 Product Booklet" |
| ) |
| else: |
| gr.Markdown( |
| "_Booklet file not found yet. Add your PDF to the app folder and name it one of the expected booklet filenames._" |
| ) |
|
|
| gr.Markdown( |
| "<footer>RMT52, LLC. 2026.</footer>" |
| ) |
|
|
| if __name__ == "__main__": |
| demo.queue() |
| demo.launch(server_name="0.0.0.0") |
