Delete app-backup.py

app-backup.py

@@ -1,730 +0,0 @@
-#!/usr/bin/env python
-
-import os
-import re
-import tempfile
-import gc  # garbage collector
-from collections.abc import Iterator
-from threading import Thread
-import json
-import requests
-import cv2
-import gradio as gr
-import spaces
-import torch
-from loguru import logger
-from PIL import Image
-from transformers import AutoProcessor, Gemma3ForConditionalGeneration, TextIteratorStreamer
-
-# CSV/TXT analysis
-import pandas as pd
-# PDF text extraction
-import PyPDF2
-
-##############################################################################
-# Memory cleanup function
-##############################################################################
-def clear_cuda_cache():
-    """Clear CUDA cache explicitly."""
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-        gc.collect()
-
-##############################################################################
-# SERPHouse API key from environment variable
-##############################################################################
-SERPHOUSE_API_KEY = os.getenv("SERPHOUSE_API_KEY", "")
-
-##############################################################################
-# Simple keyword extraction function
-##############################################################################
-def extract_keywords(text: str, top_k: int = 5) -> str:
-    """
-    Extract keywords from text
-    """
-    text = re.sub(r"[^a-zA-Z0-9가-힣\s]", "", text)
-    tokens = text.split()
-    key_tokens = tokens[:top_k]
-    return " ".join(key_tokens)
-
-##############################################################################
-# SerpHouse Live endpoint call
-##############################################################################
-def do_web_search(query: str) -> str:
-    """
-    Return top 20 'organic' results as JSON string
-    """
-    try:
-        url = "https://api.serphouse.com/serp/live"
-        
-        # 기본 GET 방식으로 파라미터 간소화하고 결과 수를 20개로 제한
-        params = {
-            "q": query,
-            "domain": "google.com",
-            "serp_type": "web",  # Basic web search
-            "device": "desktop",
-            "lang": "en",
-            "num": "20"  # Request max 20 results
-        }
-        
-        headers = {
-            "Authorization": f"Bearer {SERPHOUSE_API_KEY}"
-        }
-        
-        logger.info(f"SerpHouse API call... query: {query}")
-        logger.info(f"Request URL: {url} - params: {params}")
-        
-        # GET request
-        response = requests.get(url, headers=headers, params=params, timeout=60)
-        response.raise_for_status()
-        
-        logger.info(f"SerpHouse API response status: {response.status_code}")
-        data = response.json()
-        
-        # Handle various response structures
-        results = data.get("results", {})
-        organic = None
-        
-        # Possible response structure 1
-        if isinstance(results, dict) and "organic" in results:
-            organic = results["organic"]
-        
-        # Possible response structure 2 (nested results)
-        elif isinstance(results, dict) and "results" in results:
-            if isinstance(results["results"], dict) and "organic" in results["results"]:
-                organic = results["results"]["organic"]
-        
-        # Possible response structure 3 (top-level organic)
-        elif "organic" in data:
-            organic = data["organic"]
-            
-        if not organic:
-            logger.warning("No organic results found in response.")
-            logger.debug(f"Response structure: {list(data.keys())}")
-            if isinstance(results, dict):
-                logger.debug(f"results structure: {list(results.keys())}")
-            return "No web search results found or unexpected API response structure."
-
-        # Limit results and optimize context length
-        max_results = min(20, len(organic))
-        limited_organic = organic[:max_results]
-        
-        # Format results for better readability
-        summary_lines = []
-        for idx, item in enumerate(limited_organic, start=1):
-            title = item.get("title", "No title")
-            link = item.get("link", "#")
-            snippet = item.get("snippet", "No description")
-            displayed_link = item.get("displayed_link", link)
-            
-            # Markdown format
-            summary_lines.append(
-                f"### Result {idx}: {title}\n\n"
-                f"{snippet}\n\n"
-                f"**Source**: [{displayed_link}]({link})\n\n"
-                f"---\n"
-            )
-        
-        # Add simple instructions for model
-        instructions = """
-# X-RAY Security Scanning Reference Results
-Use this information to enhance your analysis.
-"""
-        
-        search_results = instructions + "\n".join(summary_lines)
-        logger.info(f"Processed {len(limited_organic)} search results")
-        return search_results
-    
-    except Exception as e:
-        logger.error(f"Web search failed: {e}")
-        return f"Web search failed: {str(e)}"
-
-
-##############################################################################
-# Model/Processor loading
-##############################################################################
-MAX_CONTENT_CHARS = 2000
-MAX_INPUT_LENGTH = 2096  # Max input token limit
-model_id = os.getenv("MODEL_ID", "VIDraft/Gemma-3-R1984-4B")
-
-processor = AutoProcessor.from_pretrained(model_id, padding_side="left")
-model = Gemma3ForConditionalGeneration.from_pretrained(
-    model_id,
-    device_map="auto",
-    torch_dtype=torch.bfloat16,
-    attn_implementation="eager"  # Change to "flash_attention_2" if available
-)
-MAX_NUM_IMAGES = int(os.getenv("MAX_NUM_IMAGES", "5"))
-
-
-##############################################################################
-# CSV, TXT, PDF analysis functions
-##############################################################################
-def analyze_csv_file(path: str) -> str:
-    """
-    Convert CSV file to string. Truncate if too long.
-    """
-    try:
-        df = pd.read_csv(path)
-        if df.shape[0] > 50 or df.shape[1] > 10:
-            df = df.iloc[:50, :10]
-        df_str = df.to_string()
-        if len(df_str) > MAX_CONTENT_CHARS:
-            df_str = df_str[:MAX_CONTENT_CHARS] + "\n...(truncated)..."
-        return f"**[CSV File: {os.path.basename(path)}]**\n\n{df_str}"
-    except Exception as e:
-        return f"Failed to read CSV ({os.path.basename(path)}): {str(e)}"
-
-
-def analyze_txt_file(path: str) -> str:
-    """
-    Read TXT file. Truncate if too long.
-    """
-    try:
-        with open(path, "r", encoding="utf-8") as f:
-            text = f.read()
-        if len(text) > MAX_CONTENT_CHARS:
-            text = text[:MAX_CONTENT_CHARS] + "\n...(truncated)..."
-        return f"**[TXT File: {os.path.basename(path)}]**\n\n{text}"
-    except Exception as e:
-        return f"Failed to read TXT ({os.path.basename(path)}): {str(e)}"
-
-
-def pdf_to_markdown(pdf_path: str) -> str:
-    """
-    Convert PDF text to Markdown. Extract text by pages.
-    """
-    text_chunks = []
-    try:
-        with open(pdf_path, "rb") as f:
-            reader = PyPDF2.PdfReader(f)
-            max_pages = min(5, len(reader.pages))
-            for page_num in range(max_pages):
-                page = reader.pages[page_num]
-                page_text = page.extract_text() or ""
-                page_text = page_text.strip()
-                if page_text:
-                    if len(page_text) > MAX_CONTENT_CHARS // max_pages:
-                        page_text = page_text[:MAX_CONTENT_CHARS // max_pages] + "...(truncated)"
-                    text_chunks.append(f"## Page {page_num+1}\n\n{page_text}\n")
-            if len(reader.pages) > max_pages:
-                text_chunks.append(f"\n...(Showing {max_pages} of {len(reader.pages)} pages)...")
-    except Exception as e:
-        return f"Failed to read PDF ({os.path.basename(pdf_path)}): {str(e)}"
-
-    full_text = "\n".join(text_chunks)
-    if len(full_text) > MAX_CONTENT_CHARS:
-        full_text = full_text[:MAX_CONTENT_CHARS] + "\n...(truncated)..."
-
-    return f"**[PDF File: {os.path.basename(pdf_path)}]**\n\n{full_text}"
-
-
-##############################################################################
-# Image/Video upload limit check
-##############################################################################
-def count_files_in_new_message(paths: list[str]) -> tuple[int, int]:
-    image_count = 0
-    video_count = 0
-    for path in paths:
-        if path.endswith(".mp4"):
-            video_count += 1
-        elif re.search(r"\.(png|jpg|jpeg|gif|webp)$", path, re.IGNORECASE):
-            image_count += 1
-    return image_count, video_count
-
-
-def count_files_in_history(history: list[dict]) -> tuple[int, int]:
-    image_count = 0
-    video_count = 0
-    for item in history:
-        if item["role"] != "user" or isinstance(item["content"], str):
-            continue
-        if isinstance(item["content"], list) and len(item["content"]) > 0:
-            file_path = item["content"][0]
-            if isinstance(file_path, str):
-                if file_path.endswith(".mp4"):
-                    video_count += 1
-                elif re.search(r"\.(png|jpg|jpeg|gif|webp)$", file_path, re.IGNORECASE):
-                    image_count += 1
-    return image_count, video_count
-
-
-def validate_media_constraints(message: dict, history: list[dict]) -> bool:
-    media_files = []
-    for f in message["files"]:
-        if re.search(r"\.(png|jpg|jpeg|gif|webp)$", f, re.IGNORECASE) or f.endswith(".mp4"):
-            media_files.append(f)
-
-    new_image_count, new_video_count = count_files_in_new_message(media_files)
-    history_image_count, history_video_count = count_files_in_history(history)
-    image_count = history_image_count + new_image_count
-    video_count = history_video_count + new_video_count
-
-    if video_count > 1:
-        gr.Warning("Only one video is supported.")
-        return False
-    if video_count == 1:
-        if image_count > 0:
-            gr.Warning("Mixing images and videos is not allowed.")
-            return False
-        if "<image>" in message["text"]:
-            gr.Warning("Using <image> tags with video files is not supported.")
-            return False
-    if video_count == 0 and image_count > MAX_NUM_IMAGES:
-        gr.Warning(f"You can upload up to {MAX_NUM_IMAGES} images.")
-        return False
-    
-    if "<image>" in message["text"]:
-        image_files = [f for f in message["files"] if re.search(r"\.(png|jpg|jpeg|gif|webp)$", f, re.IGNORECASE)]
-        image_tag_count = message["text"].count("<image>")
-        if image_tag_count != len(image_files):
-            gr.Warning("The number of <image> tags in the text does not match the number of image files.")
-            return False
-
-    return True
-
-
-##############################################################################
-# Video processing - with temp file tracking
-##############################################################################
-def downsample_video(video_path: str) -> list[tuple[Image.Image, float]]:
-    vidcap = cv2.VideoCapture(video_path)
-    fps = vidcap.get(cv2.CAP_PROP_FPS)
-    total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
-    frame_interval = max(int(fps), int(total_frames / 10))
-    frames = []
-
-    for i in range(0, total_frames, frame_interval):
-        vidcap.set(cv2.CAP_PROP_POS_FRAMES, i)
-        success, image = vidcap.read()
-        if success:
-            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-            # Resize image
-            image = cv2.resize(image, (0, 0), fx=0.5, fy=0.5)
-            pil_image = Image.fromarray(image)
-            timestamp = round(i / fps, 2)
-            frames.append((pil_image, timestamp))
-            if len(frames) >= 5:
-                break
-
-    vidcap.release()
-    return frames
-
-
-def process_video(video_path: str) -> tuple[list[dict], list[str]]:
-    content = []
-    temp_files = []  # List for tracking temp files
-    
-    frames = downsample_video(video_path)
-    for frame in frames:
-        pil_image, timestamp = frame
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
-            pil_image.save(temp_file.name)
-            temp_files.append(temp_file.name)  # Track for deletion later
-            content.append({"type": "text", "text": f"Frame {timestamp}:"})
-            content.append({"type": "image", "url": temp_file.name})
-    
-    return content, temp_files
-
-
-##############################################################################
-# interleaved <image> processing
-##############################################################################
-def process_interleaved_images(message: dict) -> list[dict]:
-    parts = re.split(r"(<image>)", message["text"])
-    content = []
-    image_index = 0
-    
-    image_files = [f for f in message["files"] if re.search(r"\.(png|jpg|jpeg|gif|webp)$", f, re.IGNORECASE)]
-    
-    for part in parts:
-        if part == "<image>" and image_index < len(image_files):
-            content.append({"type": "image", "url": image_files[image_index]})
-            image_index += 1
-        elif part.strip():
-            content.append({"type": "text", "text": part.strip()})
-        else:
-            if isinstance(part, str) and part != "<image>":
-                content.append({"type": "text", "text": part})
-    return content
-
-
-##############################################################################
-# PDF + CSV + TXT + Image/Video
-##############################################################################
-def is_image_file(file_path: str) -> bool:
-    return bool(re.search(r"\.(png|jpg|jpeg|gif|webp)$", file_path, re.IGNORECASE))
-
-def is_video_file(file_path: str) -> bool:
-    return file_path.endswith(".mp4")
-
-def is_document_file(file_path: str) -> bool:
-    return (
-        file_path.lower().endswith(".pdf")
-        or file_path.lower().endswith(".csv")
-        or file_path.lower().endswith(".txt")
-    )
-
-
-def process_new_user_message(message: dict) -> tuple[list[dict], list[str]]:
-    temp_files = []  # List for tracking temp files
-    
-    if not message["files"]:
-        return [{"type": "text", "text": message["text"]}], temp_files
-
-    video_files = [f for f in message["files"] if is_video_file(f)]
-    image_files = [f for f in message["files"] if is_image_file(f)]
-    csv_files = [f for f in message["files"] if f.lower().endswith(".csv")]
-    txt_files = [f for f in message["files"] if f.lower().endswith(".txt")]
-    pdf_files = [f for f in message["files"] if f.lower().endswith(".pdf")]
-
-    content_list = [{"type": "text", "text": message["text"]}]
-
-    for csv_path in csv_files:
-        csv_analysis = analyze_csv_file(csv_path)
-        content_list.append({"type": "text", "text": csv_analysis})
-
-    for txt_path in txt_files:
-        txt_analysis = analyze_txt_file(txt_path)
-        content_list.append({"type": "text", "text": txt_analysis})
-
-    for pdf_path in pdf_files:
-        pdf_markdown = pdf_to_markdown(pdf_path)
-        content_list.append({"type": "text", "text": pdf_markdown})
-
-    if video_files:
-        video_content, video_temp_files = process_video(video_files[0])
-        content_list += video_content
-        temp_files.extend(video_temp_files)
-        return content_list, temp_files
-
-    if "<image>" in message["text"] and image_files:
-        interleaved_content = process_interleaved_images({"text": message["text"], "files": image_files})
-        if content_list and content_list[0]["type"] == "text":
-            content_list = content_list[1:]
-        return interleaved_content + content_list, temp_files
-    else:
-        for img_path in image_files:
-            content_list.append({"type": "image", "url": img_path})
-
-    return content_list, temp_files
-
-
-##############################################################################
-# history -> LLM message conversion
-##############################################################################
-def process_history(history: list[dict]) -> list[dict]:
-    messages = []
-    current_user_content: list[dict] = []
-    for item in history:
-        if item["role"] == "assistant":
-            if current_user_content:
-                messages.append({"role": "user", "content": current_user_content})
-                current_user_content = []
-            messages.append({"role": "assistant", "content": [{"type": "text", "text": item["content"]}]})
-        else:
-            content = item["content"]
-            if isinstance(content, str):
-                current_user_content.append({"type": "text", "text": content})
-            elif isinstance(content, list) and len(content) > 0:
-                file_path = content[0]
-                if is_image_file(file_path):
-                    current_user_content.append({"type": "image", "url": file_path})
-                else:
-                    current_user_content.append({"type": "text", "text": f"[File: {os.path.basename(file_path)}]"})
-
-    if current_user_content:
-        messages.append({"role": "user", "content": current_user_content})
-        
-    return messages
-
-
-##############################################################################
-# Model generation function with OOM catch
-##############################################################################
-def _model_gen_with_oom_catch(**kwargs):
-    """
-    Catch OutOfMemoryError in separate thread
-    """
-    try:
-        model.generate(**kwargs)
-    except torch.cuda.OutOfMemoryError:
-        raise RuntimeError(
-            "[OutOfMemoryError] GPU memory insufficient. "
-            "Please reduce Max New Tokens or prompt length."
-        )
-    finally:
-        # Clear cache after generation
-        clear_cuda_cache()
-
-
-##############################################################################
-# Main inference function (with auto web search)
-##############################################################################
-@spaces.GPU(duration=120)
-def run(
-    message: dict,
-    history: list[dict],
-    system_prompt: str = "",
-    max_new_tokens: int = 512,
-    use_web_search: bool = False,
-    web_search_query: str = "",
-) -> Iterator[str]:
-
-    if not validate_media_constraints(message, history):
-        yield ""
-        return
-
-    temp_files = []  # For tracking temp files
-    
-    try:
-        combined_system_msg = ""
-
-        # Used internally only (hidden from UI)
-        if system_prompt.strip():
-            combined_system_msg += f"[System Prompt]\n{system_prompt.strip()}\n\n"
-
-        if use_web_search:
-            user_text = message["text"]
-            ws_query = extract_keywords(user_text, top_k=5)
-            if ws_query.strip():
-                logger.info(f"[Auto WebSearch Keyword] {ws_query!r}")
-                ws_result = do_web_search(ws_query)
-                combined_system_msg += f"[X-RAY Security Reference Data]\n{ws_result}\n\n"
-            else:
-                combined_system_msg += "[No valid keywords found, skipping WebSearch]\n\n"
-
-        messages = []
-        if combined_system_msg.strip():
-            messages.append({
-                "role": "system",
-                "content": [{"type": "text", "text": combined_system_msg.strip()}],
-            })
-
-        messages.extend(process_history(history))
-
-        user_content, user_temp_files = process_new_user_message(message)
-        temp_files.extend(user_temp_files)  # Track temp files
-        
-        for item in user_content:
-            if item["type"] == "text" and len(item["text"]) > MAX_CONTENT_CHARS:
-                item["text"] = item["text"][:MAX_CONTENT_CHARS] + "\n...(truncated)..."
-        messages.append({"role": "user", "content": user_content})
-
-        inputs = processor.apply_chat_template(
-            messages,
-            add_generation_prompt=True,
-            tokenize=True,
-            return_dict=True,
-            return_tensors="pt",
-        ).to(device=model.device, dtype=torch.bfloat16)
-        
-        # Limit input token count
-        if inputs.input_ids.shape[1] > MAX_INPUT_LENGTH:
-            inputs.input_ids = inputs.input_ids[:, -MAX_INPUT_LENGTH:]
-            if 'attention_mask' in inputs:
-                inputs.attention_mask = inputs.attention_mask[:, -MAX_INPUT_LENGTH:]
-        
-        streamer = TextIteratorStreamer(processor, timeout=30.0, skip_prompt=True, skip_special_tokens=True)
-        gen_kwargs = dict(
-            inputs,
-            streamer=streamer,
-            max_new_tokens=max_new_tokens,
-        )
-
-        t = Thread(target=_model_gen_with_oom_catch, kwargs=gen_kwargs)
-        t.start()
-
-        output = ""
-        for new_text in streamer:
-            output += new_text
-            yield output
-
-    except Exception as e:
-        logger.error(f"Error in run: {str(e)}")
-        yield f"Error occurred: {str(e)}"
-    
-    finally:
-        # Delete temp files
-        for temp_file in temp_files:
-            try:
-                if os.path.exists(temp_file):
-                    os.unlink(temp_file)
-                    logger.info(f"Deleted temp file: {temp_file}")
-            except Exception as e:
-                logger.warning(f"Failed to delete temp file {temp_file}: {e}")
-        
-        # Explicit memory cleanup
-        try:
-            del inputs, streamer
-        except:
-            pass
-        
-        clear_cuda_cache()
-
-
-
-##############################################################################
-# X-RAY security scanning examples
-##############################################################################
-examples = [
-    [
-        {
-            "text": "업로드한 X-ray 이미지들에 대한 위험 요소 식별 및 분석을 시작합니다.",
-            "files": [""],
-        }
-    ],
-
-
-]
-
-##############################################################################
-# Gradio UI (Blocks) 구성
-##############################################################################
-css = """
-.gradio-container {
-    background: white;
-    padding: 30px 40px;
-    margin: 20px auto;
-    width: 100% !important;
-    max-width: none !important;
-}
-.fillable {
-    width: 100% !important; 
-    max-width: 100% !important; 
-}
-body {
-    background: white;
-    margin: 0;
-    padding: 0;
-    font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif;
-    color: #333;
-}
-button, .btn {
-    background: transparent !important;
-    border: 1px solid #ddd;
-    color: #333;
-    padding: 12px 24px;
-    text-transform: uppercase;
-    font-weight: bold;
-    letter-spacing: 1px;
-    cursor: pointer;
-}
-button:hover, .btn:hover {
-    background: rgba(0, 0, 0, 0.05) !important;
-}
-
-h1, h2, h3 {
-    color: #333;
-}
-
-.multimodal-textbox, textarea, input {
-    background: rgba(255, 255, 255, 0.5) !important;
-    border: 1px solid #ddd;
-    color: #333;
-}
-
-.chatbox, .chatbot, .message {
-    background: transparent !important;
-}
-
-#examples_container, .examples-container {
-    margin: auto;
-    width: 90%;
-    background: transparent !important;
-}
-"""
-
-title_html = """
-<h1 align="center" style="margin-bottom: 0.2em; font-size: 1.6em;">Gemma-3-R1984-4B-BEAM</h1>
-"""
-
-
-with gr.Blocks(css=css, title="Gemma-3-R1984-4B-BEAM - X-RAY Security Scanner") as demo:
-    gr.Markdown(title_html)
-
-    # Display the web search option (while the system prompt and token slider remain hidden)
-    web_search_checkbox = gr.Checkbox(
-        label="Deep Research",
-        value=False
-    )
-
-    # X-RAY security scanning system prompt
-    system_prompt_box = gr.Textbox(
-        lines=3,
-        value="""반드시 한글로 답변하라. 당신은 위협 탐지와 항공 보안에 특화된 첨단 X-RAY 보안 스캐닝 AI입니다. 당신의 주 임무는 X-RAY 이미지에서 모든 잠재적 보안 위협을 최상의 정확도로 식별하는 것입니다.
-    
-    탐지 우선순위:
-    1. **무기**: 화기(권총, 소총 등), 칼·날붙이·예리한 물체, 호신용·격투 무기
-    2. **폭발물**: 폭탄, 기폭장치, 폭발성 물질, 의심스러운 전자 장치, 배터리가 연결된 전선
-    3. **반입 금지 물품**: 가위, 대용량 배터리, 스프링(무기 부품 가능), 공구류
-    4. **액체**: 100 ml 이상 용기에 담긴 모든 액체(화학 위협 가능)
-    5. **EOD 구성품**: 폭발물로 조립될 수 있는 모든 부품
-    
-    분석 프로토콜:
-    - 좌상단에서 우하단으로 체계적으로 스캔
-    - 위협 위치를 격자 기준으로 보고(예: “좌상단 사분면”)
-    - 위협 심각도 분류  
-      - **HIGH** : 즉각적 위험  
-      - **MEDIUM** : 반입 금지  
-      - **LOW** : 추가 검사 필요
-    - 전문 보안 용어 사용
-    - 각 위협 항목별 권장 조치 제시
-    
-    ⚠️ 중대한 사항: 잠재적 위협을 절대 놓치지 마십시오. 의심스러울 경우 반드시 수동 검사를 요청하십시오.""",
-        visible=False  # hidden from view
-    )
-
-
-    
-    max_tokens_slider = gr.Slider(
-        label="Max New Tokens",
-        minimum=100,
-        maximum=8000,
-        step=50,
-        value=1000,
-        visible=False  # hidden from view
-    )
-    
-    web_search_text = gr.Textbox(
-        lines=1,
-        label="Web Search Query",
-        placeholder="",
-        visible=False  # hidden from view
-    )
-    
-    # Configure the chat interface
-    chat = gr.ChatInterface(
-        fn=run,
-        type="messages",
-        chatbot=gr.Chatbot(type="messages", scale=1, allow_tags=["image"]),
-        textbox=gr.MultimodalTextbox(
-            file_types=[
-                ".webp", ".png", ".jpg", ".jpeg", ".gif",
-                ".mp4", ".csv", ".txt", ".pdf"
-            ],
-            file_count="multiple",
-            autofocus=True
-        ),
-        multimodal=True,
-        additional_inputs=[
-            system_prompt_box,
-            max_tokens_slider,
-            web_search_checkbox,
-            web_search_text,
-        ],
-        stop_btn=False,
-        title='<a href="https://discord.gg/openfreeai" target="_blank">https://discord.gg/openfreeai</a>',
-        # examples 파라미터 삭제
-        run_examples_on_click=False,
-        cache_examples=False,
-        css_paths=None,
-        delete_cache=(1800, 1800),
-    )
-
-
-
-
-if __name__ == "__main__":
-    # Run locally
-    demo.launch()