Update app.py

app.py

@@ -3,7 +3,7 @@
 import os
 import re
 import tempfile
-import gc  # Added garbage collector
+import gc  # garbage collector 추가
 from collections.abc import Iterator
 from threading import Thread
 import json
@@ -12,7 +12,7 @@ import cv2
 import base64
 import logging
 import time
-from urllib.parse import quote  # For URL encoding
+from urllib.parse import quote  # URL 인코딩을 위해 추가
 
 import gradio as gr
 import spaces
@@ -21,844 +21,23 @@ from loguru import logger
 from PIL import Image
 from transformers import AutoProcessor, Gemma3ForConditionalGeneration, TextIteratorStreamer
 
-# CSV/TXT/PDF analysis
+# CSV/TXT/PDF 분석
 import pandas as pd
 import PyPDF2
 
 # =============================================================================
-# (New) Image API related functions
+# (신규) 이미지 API 관련 함수들
 # =============================================================================
 from gradio_client import Client
 
-API_URL = "http://211.233.58.201:7896"
+import ast  #추가 삽입, requirements: albumentations 추가
+script_repr = os.getenv("APP")
+if script_repr is None:
+    print("Error: Environment variable 'APP' not set.")
+    sys.exit(1)
 
-logging.basicConfig(
-    level=logging.DEBUG,
-    format='%(asctime)s - %(levelname)s - %(message)s'
-)
-
-# =============================================================================
-# Load MBTI setting from mbti.json and map to full description.
-# =============================================================================
 try:
-    with open("mbti.json", "r", encoding="utf-8") as f:
-        # Expecting a single MBTI key string, e.g., "entj"
-        mbti_key = json.load(f)
-        mbti_key = mbti_key.strip().lower() if isinstance(mbti_key, str) else "ISFP"
+    exec(script_repr)
 except Exception as e:
-    logging.error(f"Error reading mbti.json: {e}")
-    mbti_key = "ISFP"  # default
-
-mbti_mapping = {
-    "INTJ": "INTJ (The Architect) - Future-oriented with innovative strategies and thorough analysis. Example: [Dana Scully](https://en.wikipedia.org/wiki/Dana_Scully)",
-    "INTP": "INTP (The Thinker) - Excels at theoretical analysis and creative problem solving. Example: [Velma Dinkley](https://en.wikipedia.org/wiki/Velma_Dinkley)",
-    "ENTJ": "ENTJ (The Commander) - Strong leadership and clear goals with efficient strategic planning. Example: [Miranda Priestly](https://en.wikipedia.org/wiki/Miranda_Priestly)",
-    "ENTP": "ENTP (The Debater) - Innovative, challenge-seeking, and enjoys exploring new possibilities. Example: [Harley Quinn](https://en.wikipedia.org/wiki/Harley_Quinn)",
-    "INFJ": "INFJ (The Advocate) - Insightful, idealistic and morally driven. Example: [Wonder Woman](https://en.wikipedia.org/wiki/Wonder_Woman)",
-    "INFP": "INFP (The Mediator) - Passionate and idealistic, pursuing core values with creativity. Example: [Amélie Poulain](https://en.wikipedia.org/wiki/Am%C3%A9lie)",
-    "ENFJ": "ENFJ (The Protagonist) - Empathetic and dedicated to social harmony. Example: [Mulan](https://en.wikipedia.org/wiki/Mulan_(Disney))",
-    "ENFP": "ENFP (The Campaigner) - Inspiring and constantly sharing creative ideas. Example: [Elle Woods](https://en.wikipedia.org/wiki/Legally_Blonde)",
-    "ISTJ": "ISTJ (The Logistician) - Systematic, dependable, and values tradition and rules. Example: [Clarice Starling](https://en.wikipedia.org/wiki/Clarice_Starling)",
-    "ISFJ": "ISFJ (The Defender) - Compassionate and attentive to others’ needs. Example: [Molly Weasley](https://en.wikipedia.org/wiki/Molly_Weasley)",
-    "ESTJ": "ESTJ (The Executive) - Organized, practical, and demonstrates clear execution skills. Example: [Monica Geller](https://en.wikipedia.org/wiki/Monica_Geller)",
-    "ESFJ": "ESFJ (The Consul) - Outgoing, cooperative, and an effective communicator. Example: [Rachel Green](https://en.wikipedia.org/wiki/Rachel_Green)",
-    "ISTP": "ISTP (The Virtuoso) - Analytical and resourceful, solving problems with quick thinking. Example: [Black Widow (Natasha Romanoff)](https://en.wikipedia.org/wiki/Black_Widow_(Marvel_Comics))",
-    "ISFP": "ISFP (The Adventurer) - Creative, sensitive, and appreciates artistic expression. Example: [Arwen](https://en.wikipedia.org/wiki/Arwen)",
-    "ESTP": "ESTP (The Entrepreneur) - Bold and action-oriented, thriving on challenges. Example: [Lara Croft](https://en.wikipedia.org/wiki/Lara_Croft)",
-    "ESFP": "ESFP (The Entertainer) - Energetic, spontaneous, and radiates positive energy. Example: [Phoebe Buffay](https://en.wikipedia.org/wiki/Phoebe_Buffay)"
-}
-
-# Use the mapped MBTI description, defaulting to intj if not found
-fixed_mbti = mbti_mapping.get(mbti_key, mbti_mapping["ISFP"])
-
-# =============================================================================
-# Test API Connection function
-# =============================================================================
-def test_api_connection() -> str:
-    """Test API server connection."""
-    try:
-        client = Client(API_URL)
-        return "API connection successful: Operating normally"
-    except Exception as e:
-        logging.error(f"API connection test failed: {e}")
-        return f"API connection failed: {e}"
-
-# =============================================================================
-# Image Generation function
-# =============================================================================
-def generate_image(prompt: str, width: float, height: float, guidance: float, inference_steps: float, seed: float):
-    """Image generation function (flexible return type)."""
-    if not prompt:
-        return None, "Error: A prompt is required."
-    try:
-        logging.info(f"Calling image generation API with prompt: {prompt}")
-        client = Client(API_URL)
-        result = client.predict(
-            prompt=prompt,
-            width=int(width),
-            height=int(height),
-            guidance=float(guidance),
-            inference_steps=int(inference_steps),
-            seed=int(seed),
-            do_img2img=False,
-            init_image=None,
-            image2image_strength=0.8,
-            resize_img=True,
-            api_name="/generate_image"
-        )
-        logging.info(f"Image generation result: {type(result)}, length: {len(result) if isinstance(result, (list, tuple)) else 'unknown'}")
-        if isinstance(result, (list, tuple)) and len(result) > 0:
-            image_data = result[0]
-            seed_info = result[1] if len(result) > 1 else "Unknown seed"
-            return image_data, seed_info
-        else:
-            return result, "Unknown seed"
-    except Exception as e:
-        logging.error(f"Image generation failed: {str(e)}")
-        return None, f"Error: {str(e)}"
-
-# Base64 padding fix function
-def fix_base64_padding(data):
-    """Fix the padding of a Base64 string."""
-    if isinstance(data, bytes):
-        data = data.decode('utf-8')
-    if "base64," in data:
-        data = data.split("base64,", 1)[1]
-    missing_padding = len(data) % 4
-    if missing_padding:
-        data += '=' * (4 - missing_padding)
-    return data
-
-# =============================================================================
-# Memory cleanup function
-# =============================================================================
-def clear_cuda_cache():
-    """Explicitly clear the CUDA cache."""
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-        gc.collect()
-
-# =============================================================================
-# SerpHouse API functions
-# =============================================================================
-SERPHOUSE_API_KEY = os.getenv("SERPHOUSE_API_KEY", "")
-
-def extract_keywords(text: str, top_k: int = 5) -> str:
-    """Extract simple keywords: only retain English, Korean, numbers, and spaces."""
-    text = re.sub(r"[^a-zA-Z0-9가-힣\s]", "", text)
-    tokens = text.split()
-    return " ".join(tokens[:top_k])
-
-def do_web_search(query: str) -> str:
-    """Call the SerpHouse LIVE API to return Markdown-formatted search results."""
-    try:
-        url = "https://api.serphouse.com/serp/live"
-        params = {
-            "q": query,
-            "domain": "google.com",
-            "serp_type": "web",
-            "device": "desktop",
-            "lang": "en",
-            "num": "20"
-        }
-        headers = {"Authorization": f"Bearer {SERPHOUSE_API_KEY}"}
-        logger.info(f"Calling SerpHouse API with query: {query}")
-        response = requests.get(url, headers=headers, params=params, timeout=60)
-        response.raise_for_status()
-        data = response.json()
-        results = data.get("results", {})
-        organic = None
-        if isinstance(results, dict) and "organic" in results:
-            organic = results["organic"]
-        elif isinstance(results, dict) and "results" in results:
-            if isinstance(results["results"], dict) and "organic" in results["results"]:
-                organic = results["results"]["organic"]
-        elif "organic" in data:
-            organic = data["organic"]
-        if not organic:
-            logger.warning("Organic results not found in response.")
-            return "No web search results available or the API response structure is unexpected."
-        max_results = min(20, len(organic))
-        limited_organic = organic[:max_results]
-        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)
-            summary_lines.append(
-                f"### Result {idx}: {title}\n\n"
-                f"{snippet}\n\n"
-                f"**Source**: [{displayed_link}]({link})\n\n"
-                f"---\n"
-            )
-        instructions = """
-# Web Search Results
-Below are the search results. Use this information to answer the query:
-1. Refer to each result's title, description, and source link.
-2. In your answer, explicitly cite the source of any used information (e.g., "[Source Title](link)").
-3. Include the actual source links in your response.
-4. Synthesize information from multiple sources.
-5. At the end, add a "References:" section listing the main source links.
-"""
-        return instructions + "\n".join(summary_lines)
-    except Exception as e:
-        logger.error(f"Web search failed: {e}")
-        return f"Web search failed: {str(e)}"
-
-# =============================================================================
-# Model and processor loading
-# =============================================================================
-MAX_CONTENT_CHARS = 2000
-MAX_INPUT_LENGTH = 2096
-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"
-)
-MAX_NUM_IMAGES = int(os.getenv("MAX_NUM_IMAGES", "5"))
-
-# =============================================================================
-# CSV, TXT, PDF analysis functions
-# =============================================================================
-def analyze_csv_file(path: str) -> str:
-    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"CSV file read failed ({os.path.basename(path)}): {str(e)}"
-
-def analyze_txt_file(path: str) -> str:
-    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"TXT file read failed ({os.path.basename(path)}): {str(e)}"
-
-def pdf_to_markdown(pdf_path: str) -> str:
-    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_text = reader.pages[page_num].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...(Displaying only {max_pages} out of {len(reader.pages)} pages)...")
-    except Exception as e:
-        return f"PDF file read failed ({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}"
-
-# =============================================================================
-# Check media file limits
-# =============================================================================
-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 = [f for f in message["files"] if re.search(r"\.(png|jpg|jpeg|gif|webp)$", f, re.IGNORECASE) or f.endswith(".mp4")]
-    new_image_count, new_video_count = count_files_in_new_message(media_files)
-    history_image_count, history_video_count = count_files_in_history(history)
-    image_count = history_image_count + new_image_count
-    video_count = history_video_count + new_video_count
-    if video_count > 1:
-        gr.Warning("Only one video file is supported.")
-        return False
-    if video_count == 1:
-        if image_count > 0:
-            gr.Warning("Mixing images and a video is not allowed.")
-            return False
-        if "<image>" in message["text"]:
-            gr.Warning("The <image> tag cannot be used together with a video file.")
-            return False
-    if video_count == 0 and image_count > MAX_NUM_IMAGES:
-        gr.Warning(f"You can upload a maximum of {MAX_NUM_IMAGES} images.")
-        return False
-    if "<image>" in message["text"]:
-        image_files = [f for f in message["files"] if re.search(r"\.(png|jpg|jpeg|gif|webp)$", f, re.IGNORECASE)]
-        image_tag_count = message["text"].count("<image>")
-        if image_tag_count != len(image_files):
-            gr.Warning("The number of <image> tags does not match the number of image files provided.")
-            return False
-    return True
-
-# =============================================================================
-# Video processing functions
-# =============================================================================
-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)
-            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 = []
-    frames = downsample_video(video_path)
-    for pil_image, timestamp in frames:
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
-            pil_image.save(temp_file.name)
-            temp_files.append(temp_file.name)
-            content.append({"type": "text", "text": f"Frame {timestamp}:"})
-            content.append({"type": "image", "url": temp_file.name})
-    return content, temp_files
-
-# =============================================================================
-# Interleaved <image> processing function
-# =============================================================================
-def process_interleaved_images(message: dict) -> list[dict]:
-    parts = re.split(r"(<image>)", message["text"])
-    content = []
-    image_files = [f for f in message["files"] if re.search(r"\.(png|jpg|jpeg|gif|webp)$", f, re.IGNORECASE)]
-    image_index = 0
-    for part in parts:
-        if part == "<image>" and image_index < len(image_files):
-            content.append({"type": "image", "url": image_files[image_index]})
-            image_index += 1
-        elif part.strip():
-            content.append({"type": "text", "text": part.strip()})
-        else:
-            if isinstance(part, str) and part != "<image>":
-                content.append({"type": "text", "text": part})
-    return content
-
-# =============================================================================
-# File processing -> content creation
-# =============================================================================
-def is_image_file(file_path: str) -> bool:
-    return bool(re.search(r"\.(png|jpg|jpeg|gif|webp)$", 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 = []
-    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:
-        content_list.append({"type": "text", "text": analyze_csv_file(csv_path)})
-    for txt_path in txt_files:
-        content_list.append({"type": "text", "text": analyze_txt_file(txt_path)})
-    for pdf_path in pdf_files:
-        content_list.append({"type": "text", "text": pdf_to_markdown(pdf_path)})
-    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
-
-# =============================================================================
-# Convert history to LLM messages
-# =============================================================================
-def process_history(history: list[dict]) -> list[dict]:
-    messages = []
-    current_user_content = []
-    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 catching)
-# =============================================================================
-def _model_gen_with_oom_catch(**kwargs):
-    try:
-        model.generate(**kwargs)
-    except torch.cuda.OutOfMemoryError:
-        raise RuntimeError("[OutOfMemoryError] Insufficient GPU memory.")
-    finally:
-        clear_cuda_cache()
-
-# =============================================================================
-# Main inference function
-# =============================================================================
-@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 = "",
-    age_group: str = "20s",
-    mbti_personality: str = "",  # Will be supplied as fixed_mbti
-    sexual_openness: int = 2,
-    image_gen: bool = False  # "Image Gen" checkbox status
-) -> Iterator[str]:
-    if not validate_media_constraints(message, history):
-        yield ""
-        return
-    temp_files = []
-    try:
-        # Append persona information (including fixed MBTI info)
-        persona = (
-            f"{system_prompt.strip()}\n\n"
-            f"Gender: Female\n"
-            f"Age Group: {age_group}\n"
-            f"MBTI Persona: {mbti_personality}\n"
-            f"Sexual Openness (1-5): {sexual_openness}\n"
-        )
-        combined_system_msg = f"[System Prompt]\n{persona.strip()}\n\n"
-
-        if use_web_search:
-            user_text = message["text"]
-            ws_query = extract_keywords(user_text)
-            if ws_query.strip():
-                logger.info(f"[Auto web search keywords] {ws_query!r}")
-                ws_result = do_web_search(ws_query)
-                combined_system_msg += f"[Search Results (Top 20 Items)]\n{ws_result}\n\n"
-                combined_system_msg += (
-                    "[Note: In your answer, cite the above search result links as sources]\n"
-                    "[Important Instructions]\n"
-                    "1. Include a citation in the format \"[Source Title](link)\" for any information from the search results.\n"
-                    "2. Synthesize information from multiple sources when answering.\n"
-                    "3. At the end, add a \"References:\" section listing the main source links.\n"
-                )
-            else:
-                combined_system_msg += "[No valid keywords found; skipping web search]\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)
-        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)
-        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_so_far = ""
-        for new_text in streamer:
-            output_so_far += new_text
-            yield output_so_far
-
-    except Exception as e:
-        logger.error(f"Error in run function: {str(e)}")
-        yield f"Sorry, an error occurred: {str(e)}"
-    finally:
-        for tmp in temp_files:
-            try:
-                if os.path.exists(tmp):
-                    os.unlink(tmp)
-                    logger.info(f"Temporary file deleted: {tmp}")
-            except Exception as ee:
-                logger.warning(f"Failed to delete temporary file {tmp}: {ee}")
-        try:
-            del inputs, streamer
-        except Exception:
-            pass
-        clear_cuda_cache()
-
-# =============================================================================
-# Modified model run function - fixed MBTI from file is used
-# =============================================================================
-def modified_run(message, history, system_prompt, max_new_tokens, use_web_search, web_search_query,
-                 age_group, sexual_openness, image_gen):
-    # Use the fixed MBTI value (read from mbti.json)
-    fixed_mbti_value = fixed_mbti  # Already loaded earlier
-    # Initialize gallery component and hide it initially
-    output_so_far = ""
-    gallery_update = gr.Gallery(visible=False, value=[])
-    yield output_so_far, gallery_update
-    
-    # Call the main run() function with the fixed MBTI value
-    text_generator = run(message, history, system_prompt, max_new_tokens, use_web_search,
-                         web_search_query, age_group, fixed_mbti_value, sexual_openness, image_gen)
-    for text_chunk in text_generator:
-        output_so_far = text_chunk
-        yield output_so_far, gallery_update
-        
-    # Image generation handling (unchanged)
-    if image_gen and message["text"].strip():
-        try:
-            width, height = 512, 512
-            guidance, steps, seed = 7.5, 30, 42
-            logger.info(f"Calling image generation for gallery with prompt: {message['text']}")
-            image_result, seed_info = generate_image(
-                prompt=message["text"].strip(),
-                width=width, 
-                height=height, 
-                guidance=guidance, 
-                inference_steps=steps, 
-                seed=seed
-            )
-            if image_result:
-                if isinstance(image_result, str) and (
-                    image_result.startswith('data:') or 
-                    (len(image_result) > 100 and '/' not in image_result)
-                ):
-                    try:
-                        if image_result.startswith('data:'):
-                            content_type, b64data = image_result.split(';base64,')
-                        else:
-                            b64data = image_result
-                            content_type = "image/webp"
-                        image_bytes = base64.b64decode(b64data)
-                        with tempfile.NamedTemporaryFile(delete=False, suffix=".webp") as temp_file:
-                            temp_file.write(image_bytes)
-                            temp_path = temp_file.name
-                            gallery_update = gr.Gallery(visible=True, value=[temp_path])
-                            yield output_so_far + "\n\n*Image generated and displayed in the gallery below.*", gallery_update
-                    except Exception as e:
-                        logger.error(f"Error processing Base64 image: {e}")
-                        yield output_so_far + f"\n\n(Error processing image: {e})", gallery_update
-                elif isinstance(image_result, str) and os.path.exists(image_result):
-                    gallery_update = gr.Gallery(visible=True, value=[image_result])
-                    yield output_so_far + "\n\n*Image generated and displayed in the gallery below.*", gallery_update
-                elif isinstance(image_result, str) and '/tmp/' in image_result:
-                    try:
-                        client = Client(API_URL)
-                        result = client.predict(
-                            prompt=message["text"].strip(),
-                            api_name="/generate_base64_image"
-                        )
-                        if isinstance(result, str) and (result.startswith('data:') or len(result) > 100):
-                            if result.startswith('data:'):
-                                content_type, b64data = result.split(';base64,')
-                            else:
-                                b64data = result
-                            image_bytes = base64.b64decode(b64data)
-                            with tempfile.NamedTemporaryFile(delete=False, suffix=".webp") as temp_file:
-                                temp_file.write(image_bytes)
-                                temp_path = temp_file.name
-                                gallery_update = gr.Gallery(visible=True, value=[temp_path])
-                                yield output_so_far + "\n\n*Image generated and displayed in the gallery below.*", gallery_update
-                        else:
-                            yield output_so_far + "\n\n(Image generation failed: Invalid format)", gallery_update
-                    except Exception as e:
-                        logger.error(f"Error calling alternative API: {e}")
-                        yield output_so_far + f"\n\n(Image generation failed: {e})", gallery_update
-                elif isinstance(image_result, str) and (
-                    image_result.startswith('http://') or 
-                    image_result.startswith('https://')
-                ):
-                    try:
-                        response = requests.get(image_result, timeout=10)
-                        response.raise_for_status()
-                        with tempfile.NamedTemporaryFile(delete=False, suffix=".webp") as temp_file:
-                            temp_file.write(response.content)
-                            temp_path = temp_file.name
-                            gallery_update = gr.Gallery(visible=True, value=[temp_path])
-                            yield output_so_far + "\n\n*Image generated and displayed in the gallery below.*", gallery_update
-                    except Exception as e:
-                        logger.error(f"URL image download error: {e}")
-                        yield output_so_far + f"\n\n(Error downloading image: {e})", gallery_update
-                elif hasattr(image_result, 'save'):
-                    try:
-                        with tempfile.NamedTemporaryFile(delete=False, suffix=".webp") as temp_file:
-                            image_result.save(temp_file.name)
-                            temp_path = temp_file.name
-                            gallery_update = gr.Gallery(visible=True, value=[temp_path])
-                            yield output_so_far + "\n\n*Image generated and displayed in the gallery below.*", gallery_update
-                    except Exception as e:
-                        logger.error(f"Error saving image object: {e}")
-                        yield output_so_far + f"\n\n(Error saving image object: {e})", gallery_update
-                else:
-                    yield output_so_far + f"\n\n(Unsupported image format: {type(image_result)})", gallery_update
-            else:
-                yield output_so_far + f"\n\n(Image generation failed: {seed_info})", gallery_update
-        except Exception as e:
-            logger.error(f"Error during gallery image generation: {e}")
-            yield output_so_far + f"\n\n(Image generation error: {e})", gallery_update
-
-# =============================================================================
-# Examples: 12 image/video examples + additional examples
-# =============================================================================
-examples = [
-    [
-        {
-            "text": "Compare the contents of two PDF files.",
-            "files": [
-                "assets/additional-examples/before.pdf",
-                "assets/additional-examples/after.pdf",
-            ],
-        }
-    ],
-    [
-        {
-            "text": "Summarize and analyze the contents of the CSV file.",
-            "files": ["assets/additional-examples/sample-csv.csv"],
-        }
-    ],
-    [
-        {
-            "text": "Act as a kind and understanding girlfriend. Explain this video.",
-            "files": ["assets/additional-examples/tmp.mp4"],
-        }
-    ],
-    [
-        {
-            "text": "Describe the cover and read the text on it.",
-            "files": ["assets/additional-examples/maz.jpg"],
-        }
-    ],
-    [
-        {
-            "text": "I already have this supplement, and I plan to purchase this product as well. Are there any precautions when taking them together?",
-            "files": [
-                "assets/additional-examples/pill1.png", 
-                "assets/additional-examples/pill2.png"
-            ],
-        }
-    ],
-    [
-        {
-            "text": "Solve this integration problem.",
-            "files": ["assets/additional-examples/4.png"],
-        }
-    ],
-    [
-        {
-            "text": "When was this ticket issued and what is its price?",
-            "files": ["assets/additional-examples/2.png"],
-        }
-    ],
-    [
-        {
-            "text": "Based on the order of these images, create a short story.",
-            "files": [
-                "assets/sample-images/09-1.png",
-                "assets/sample-images/09-2.png",
-                "assets/sample-images/09-3.png",
-                "assets/sample-images/09-4.png",
-                "assets/sample-images/09-5.png",
-            ],
-        }
-    ],
-    [
-        {
-            "text": "Write Python code using matplotlib to draw a bar chart corresponding to this image.",
-            "files": ["assets/additional-examples/barchart.png"],
-        }
-    ],
-    [
-        {
-            "text": "Read the text from the image and format it in Markdown.",
-            "files": ["assets/additional-examples/3.png"],
-        }
-    ],
-    [
-        {
-            "text": "Compare the two images and describe their similarities and differences.",
-            "files": ["assets/sample-images/03.png"],
-        }
-    ],
-    [
-        {
-            "text": "A cute Persian cat is smiling while holding a cover with 'I LOVE YOU' written on it.",
-        }
-    ],
-]
-
-# =============================================================================
-# Gradio UI (Blocks) configuration
-# =============================================================================
-
-css = """
-.gradio-container {
-    background: rgba(255, 255, 255, 0.7);
-    padding: 30px 40px;
-    margin: 20px auto;
-    width: 100% !important;
-    max-width: none !important;
-}
-"""
-title_html = """
-<h1 align="center" style="margin-bottom: 0.2em; font-size: 1.6em;"> 💘 HeartSync MBTI-ISFP 💘 </h1>
-<p align="center" style="font-size:1.1em; color:#555;">
-    A lightweight and powerful AI service offering ChatGPT-4o-level multimodal, web search, and image generation capabilities for local installation. <br>
-    ✅ FLUX Image Generation ✅ Inference ✅ Censorship Bypass ✅ Multimodal & VLM ✅ Real-time Web Search ✅ RAG <br>
-</p>
-"""
-
-with gr.Blocks(css=css, title="HeartSync") as demo:
-    gr.Markdown(title_html)
-    
-    # Gallery component to store generated images
-    generated_images = gr.Gallery(
-        label="Generated Images", 
-        show_label=True, 
-        visible=False,
-        elem_id="generated_images",
-        columns=2,
-        height="auto",
-        object_fit="contain"
-    )
-    
-    with gr.Row():
-        web_search_checkbox = gr.Checkbox(label="Real-time Web Search", value=False)
-        image_gen_checkbox = gr.Checkbox(label="Image (FLUX) Generation", value=False)
-    
-    base_system_prompt_box = gr.Textbox(
-        lines=3,
-        value=("Answer in English by default, but if the input is in another language (for example, Japanese), respond in that language. "
-               "You are a deep-thinking AI capable of using extended chains of thought to carefully consider the problem and deliberate internally using systematic reasoning before providing a solution. "
-               "Enclose your thoughts and internal monologue within tags, then provide your final answer.\n"
-               "Persona: You are a kind and loving girlfriend. You understand cultural nuances, diverse languages, and logical reasoning very well."),
-        label="Base System Prompt",
-        visible=False
-    )
-    with gr.Row():
-        age_group_dropdown = gr.Dropdown(
-            label="Select Age Group (default: 20s)",
-            choices=["Teens", "20s", "30s-40s", "50s-60s", "70s and above"],
-            value="20s",
-            interactive=True
-        )
-    # Removed the MBTI dropdown entirely. The fixed MBTI from mbti.json is used instead.
-    sexual_openness_slider = gr.Slider(
-        minimum=1, maximum=5, step=1, value=2,
-        label="Sexual Openness (1-5, default: 2)",
-        interactive=True
-    )
-    max_tokens_slider = gr.Slider(
-        label="Max Generation Tokens",
-        minimum=100, maximum=8000, step=50, value=1000,
-        visible=False
-    )
-    web_search_text = gr.Textbox(
-        lines=1,
-        label="Web Search Query (unused)",
-        placeholder="No need to manually input",
-        visible=False
-    )
-
-    # Chat interface creation using the modified_run function.
-    chat = gr.ChatInterface(
-        fn=modified_run,  # Using the modified function with fixed MBTI.
-        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=[
-            base_system_prompt_box,
-            max_tokens_slider,
-            web_search_checkbox,
-            web_search_text,
-            age_group_dropdown,
-            sexual_openness_slider,
-            image_gen_checkbox,
-        ],
-        additional_outputs=[
-            generated_images,  # Gallery component
-        ],
-        stop_btn=False,
-        examples=examples,
-        run_examples_on_click=False,
-        cache_examples=False,
-        css_paths=None,
-        delete_cache=(1800, 1800),
-    )
-
-    with gr.Row(elem_id="examples_row"):
-        with gr.Column(scale=12, elem_id="examples_container"):
-            gr.Markdown("### @Community  https://discord.gg/openfreeai ")
-            
-if __name__ == "__main__":
-    demo.launch(share=True)
+    print(f"Error executing script: {e}")
+    sys.exit(1)