Update app.py

app.py

@@ -3,18 +3,16 @@
 import os
 import re
 import tempfile
-import gc  # garbage collector 추가
+import gc
 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
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
 
 # CSV/TXT 분석
 import pandas as pd
@@ -51,7 +49,6 @@ def extract_keywords(text: str, top_k: int = 5) -> str:
 
 ##############################################################################
 # SerpHouse Live endpoint 호출
-# - 상위 20개 결과 JSON을 LLM에 넘길 때 link, snippet 등 모두 포함
 ##############################################################################
 def do_web_search(query: str) -> str:
     """
@@ -61,14 +58,13 @@ def do_web_search(query: str) -> str:
     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",  # 기본 웹 검색
+            "serp_type": "web",
             "device": "desktop",
             "lang": "en",
-            "num": "20"  # 최대 20개 결과만 요청
+            "num": "20"
         }
         
         headers = {
@@ -76,44 +72,33 @@ def do_web_search(query: str) -> str:
         }
         
         logger.info(f"SerpHouse API 호출 중... 검색어: {query}")
-        logger.info(f"요청 URL: {url} - 파라미터: {params}")
         
-        # GET 요청 수행
         response = requests.get(url, headers=headers, params=params, timeout=60)
         response.raise_for_status()
         
-        logger.info(f"SerpHouse API 응답 상태 코드: {response.status_code}")
         data = response.json()
         
         # 다양한 응답 구조 처리
         results = data.get("results", {})
         organic = None
         
-        # 가능한 응답 구조 1
         if isinstance(results, dict) and "organic" in results:
             organic = results["organic"]
-        
-        # 가능한 응답 구조 2 (중첩된 results)
         elif isinstance(results, dict) and "results" in results:
             if isinstance(results["results"], dict) and "organic" in results["results"]:
                 organic = results["results"]["organic"]
-        
-        # 가능한 응답 구조 3 (최상위 organic)
         elif "organic" in data:
             organic = data["organic"]
             
         if not organic:
             logger.warning("응답에서 organic 결과를 찾을 수 없습니다.")
-            logger.debug(f"응답 구조: {list(data.keys())}")
-            if isinstance(results, dict):
-                logger.debug(f"results 구조: {list(results.keys())}")
             return "No web search results found or unexpected API response structure."
 
         # 결과 수 제한 및 컨텍스트 길이 최적화
         max_results = min(20, len(organic))
         limited_organic = organic[:max_results]
         
-        # 결과 형식 개선 - 마크다운 형식으로 출력하여 가독성 향상
+        # 결과 형식 개선 - 마크다운 형식으로 출력
         summary_lines = []
         for idx, item in enumerate(limited_organic, start=1):
             title = item.get("title", "No title")
@@ -121,7 +106,6 @@ def do_web_search(query: str) -> str:
             snippet = item.get("snippet", "No description")
             displayed_link = item.get("displayed_link", link)
             
-            # 마크다운 형식 (링크 클릭 가능)
             summary_lines.append(
                 f"### Result {idx}: {title}\n\n"
                 f"{snippet}\n\n"
@@ -129,7 +113,6 @@ def do_web_search(query: str) -> str:
                 f"---\n"
             )
         
-        # 모델에게 명확한 지침 추가
         instructions = """
 # 웹 검색 결과
 아래는 검색 결과입니다. 질문에 답변할 때 이 정보를 활용하세요:
@@ -147,31 +130,27 @@ def do_web_search(query: str) -> str:
         logger.error(f"Web search failed: {e}")
         return f"Web search failed: {str(e)}"
 
-
 ##############################################################################
-# 모델/프로세서 로딩
+# 모델/토크나이저 로딩 (텍스트 전용)
 ##############################################################################
 MAX_CONTENT_CHARS = 2000
-MAX_INPUT_LENGTH = 2096  # 최대 입력 토큰 수 제한 추가
+MAX_INPUT_LENGTH = 2096
 model_id = os.getenv("MODEL_ID", "VIDraft/Gemma-3-R1984-1B")
 
-processor = AutoProcessor.from_pretrained(model_id, padding_side="left")
-model = Gemma3ForConditionalGeneration.from_pretrained(
+# 텍스트 전용 모델로 로드
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
     model_id,
     device_map="auto",
     torch_dtype=torch.bfloat16,
-    attn_implementation="eager"  # 가능하다면 "flash_attention_2"로 변경
+    attn_implementation="eager"
 )
-MAX_NUM_IMAGES = int(os.getenv("MAX_NUM_IMAGES", "5"))
-
 
 ##############################################################################
 # CSV, TXT, PDF 분석 함수
 ##############################################################################
 def analyze_csv_file(path: str) -> str:
-    """
-    CSV 파일을 전체 문자열로 변환. 너무 길 경우 일부만 표시.
-    """
+    """CSV 파일을 전체 문자열로 변환. 너무 길 경우 일부만 표시."""
     try:
         df = pd.read_csv(path)
         if df.shape[0] > 50 or df.shape[1] > 10:
@@ -183,11 +162,8 @@ def analyze_csv_file(path: str) -> str:
     except Exception as e:
         return f"Failed to read CSV ({os.path.basename(path)}): {str(e)}"
 
-
 def analyze_txt_file(path: str) -> str:
-    """
-    TXT 파일 전문 읽기. 너무 길면 일부만 표시.
-    """
+    """TXT 파일 전문 읽기. 너무 길면 일부만 표시."""
     try:
         with open(path, "r", encoding="utf-8") as f:
             text = f.read()
@@ -197,11 +173,8 @@ def analyze_txt_file(path: str) -> str:
     except Exception as e:
         return f"Failed to read TXT ({os.path.basename(path)}): {str(e)}"
 
-
 def pdf_to_markdown(pdf_path: str) -> str:
-    """
-    PDF 텍스트를 Markdown으로 변환. 페이지별로 간단히 텍스트 추출.
-    """
+    """PDF 텍스트를 Markdown으로 변환. 페이지별로 간단히 텍스트 추출."""
     text_chunks = []
     try:
         with open(pdf_path, "rb") as f:
@@ -226,146 +199,9 @@ def pdf_to_markdown(pdf_path: str) -> str:
 
     return f"**[PDF File: {os.path.basename(pdf_path)}]**\n\n{full_text}"
 
-
-##############################################################################
-# 이미지/비디오 업로드 제한 검사
-##############################################################################
-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
-
-
 ##############################################################################
-# 비디오 처리 - 임시 파일 추적 코드 추가
+# 문서 파일 확인
 ##############################################################################
-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)
-            # 이미지 크기 줄이기 추가
-            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 = []  # 임시 파일 추적을 위한 리스트
-    
-    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)  # 추적을 위해 경로 저장
-            content.append({"type": "text", "text": f"Frame {timestamp}:"})
-            content.append({"type": "image", "url": temp_file.name})
-    
-    return content, temp_files
-
-
-##############################################################################
-# interleaved <image> 처리
-##############################################################################
-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)$", f, re.IGNORECASE)]
-    
-    for part in parts:
-        if part == "<image>" and image_index < len(image_files):
-            content.append({"type": "image", "url": image_files[image_index]})
-            image_index += 1
-        elif part.strip():
-            content.append({"type": "text", "text": part.strip()})
-        else:
-            if isinstance(part, str) and part != "<image>":
-                content.append({"type": "text", "text": part})
-    return content
-
-
-##############################################################################
-# PDF + CSV + TXT + 이미지/비디오
-##############################################################################
-def is_image_file(file_path: str) -> bool:
-    return bool(re.search(r"\.(png|jpg|jpeg|gif|webp)$", 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")
@@ -373,87 +209,59 @@ def is_document_file(file_path: str) -> bool:
         or file_path.lower().endswith(".txt")
     )
 
-
-def process_new_user_message(message: dict) -> tuple[list[dict], list[str]]:
-    temp_files = []  # 임시 파일 추적용 리스트
+##############################################################################
+# 메시지 처리 (텍스트 및 문서 파일만)
+##############################################################################
+def process_new_user_message(message: dict) -> str:
+    """사용자 메시지와 첨부된 문서 파일들을 처리하여 하나의 텍스트로 결합"""
     
-    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
-
+    content_parts = [message["text"]]
+    
+    if message.get("files"):
+        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")]
+        
+        for csv_path in csv_files:
+            csv_analysis = analyze_csv_file(csv_path)
+            content_parts.append(csv_analysis)
+        
+        for txt_path in txt_files:
+            txt_analysis = analyze_txt_file(txt_path)
+            content_parts.append(txt_analysis)
+        
+        for pdf_path in pdf_files:
+            pdf_markdown = pdf_to_markdown(pdf_path)
+            content_parts.append(pdf_markdown)
+    
+    return "\n\n".join(content_parts)
 
 ##############################################################################
-# history -> LLM 메시지 변환
+# 대화 히스토리 처리
 ##############################################################################
-def process_history(history: list[dict]) -> list[dict]:
-    messages = []
-    current_user_content: list[dict] = []
+def process_history(history: list[dict]) -> str:
+    """대화 히스토리를 텍스트 형식으로 변환"""
+    conversation_text = ""
+    
     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:
+            conversation_text += f"\nAssistant: {item['content']}\n"
+        else:  # user
             content = item["content"]
             if isinstance(content, str):
-                current_user_content.append({"type": "text", "text": content})
+                conversation_text += f"\nUser: {content}\n"
             elif isinstance(content, list) and len(content) > 0:
+                # 파일 경로만 표시
                 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
-
+                conversation_text += f"\nUser: [File: {os.path.basename(file_path)}]\n"
+    
+    return conversation_text
 
 ##############################################################################
-# 모델 생성 함수에서 OOM 캐치
+# 모델 생성 함수
 ##############################################################################
 def _model_gen_with_oom_catch(**kwargs):
-    """
-    별도 스레드에서 OutOfMemoryError를 잡아주기 위해
-    """
+    """별도 스레드에서 OutOfMemoryError를 잡아주기 위해"""
     try:
         model.generate(**kwargs)
     except torch.cuda.OutOfMemoryError:
@@ -462,12 +270,10 @@ def _model_gen_with_oom_catch(**kwargs):
             "Max New Tokens을 줄이거나, 프롬프트 길이를 줄여주세요."
         )
     finally:
-        # 생성 완료 후 한번 더 캐시 비우기
         clear_cuda_cache()
 
-
 ##############################################################################
-# 메인 추론 함수 (web search 체크 시 자동 키워드추출->검색->결과 system msg)
+# 메인 추론 함수 (텍스트 전용)
 ##############################################################################
 @spaces.GPU(duration=120)
 def run(
@@ -478,111 +284,83 @@ def run(
     use_web_search: bool = False,
     web_search_query: str = "",
 ) -> Iterator[str]:
-
-    if not validate_media_constraints(message, history):
-        yield ""
-        return
-
-    temp_files = []  # 임시 파일 추적용
     
     try:
-        combined_system_msg = ""
-
-        # 내부적으로만 사용 (UI에서는 보이지 않음)
+        # 전체 프롬프트 구성
+        full_prompt = ""
+        
+        # 시스템 프롬프트
         if system_prompt.strip():
-            combined_system_msg += f"[System Prompt]\n{system_prompt.strip()}\n\n"
-
+            full_prompt += f"System: {system_prompt.strip()}\n\n"
+        
+        # 웹 검색 수행
         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"[Search top-20 Full Items Based on user prompt]\n{ws_result}\n\n"
-                # >>> 추가된 안내 문구 (검색 결과의 link 등 출처를 활용)
-                combined_system_msg += "[참고: 위 검색결과 내용과 link를 출처로 인용하여 답변해 주세요.]\n\n"
-                combined_system_msg += """
-[중요 지시사항]
-1. 답변에 검색 결과에서 찾은 ��보의 출처를 반드시 인용하세요.
-2. 출처 인용 시 "[출처 제목](링크)" 형식의 마크다운 링크를 사용하세요.
-3. 여러 출처의 정보를 종합하여 답변하세요.
-4. 답변 마지막에 "참고 자료:" 섹션을 추가하고 사용한 주요 출처 링크를 나열하세요.
-"""
-            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)  # 임시 파일 추적
+                full_prompt += f"[Web Search Results]\n{ws_result}\n\n"
+                full_prompt += "[중요: 위 검색결과의 출처를 인용하여 답변해 주세요.]\n\n"
         
-        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,
+        # 대화 히스토리
+        if history:
+            conversation_history = process_history(history)
+            full_prompt += conversation_history
+        
+        # 현재 사용자 메시지
+        user_content = process_new_user_message(message)
+        full_prompt += f"\nUser: {user_content}\nAssistant:"
+        
+        # 토큰화
+        inputs = tokenizer(
+            full_prompt,
             return_tensors="pt",
-        ).to(device=model.device, dtype=torch.bfloat16)
+            truncation=True,
+            max_length=MAX_INPUT_LENGTH
+        ).to(device=model.device)
         
-        # 입력 토큰 수 제한 추가
-        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:]
+        # 스트리밍 설정
+        streamer = TextIteratorStreamer(
+            tokenizer,
+            timeout=30.0,
+            skip_prompt=True,
+            skip_special_tokens=True
+        )
         
-        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,
+            temperature=0.7,
+            top_p=0.9,
+            do_sample=True,
         )
-
+        
+        # 별도 스레드에서 생성
         t = Thread(target=_model_gen_with_oom_catch, kwargs=gen_kwargs)
         t.start()
-
+        
+        # 스트리밍 출력
         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"죄송합니다. 오류가 발생했습니다: {str(e)}"
     
     finally:
-        # 임시 파일 삭제
-        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}")
-        
-        # 명시적 메모리 정리
+        # 메모리 정리
         try:
-            del inputs, streamer
+            del inputs
         except:
             pass
-        
         clear_cuda_cache()
 
-
-
 ##############################################################################
-# 예시들 (모두 영어로)
+# 예시들 (텍스트 및 문서 파일만)
 ##############################################################################
 examples = [
     [
@@ -602,100 +380,49 @@ examples = [
     ],
     [
         {
-            "text": "Assume the role of a friendly and understanding girlfriend. Describe 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 <image> and I plan to buy this product <image>. Are there any precautions when taking them together?",
-            "files": ["assets/additional-examples/pill1.png", "assets/additional-examples/pill2.png"],
+            "text": "What are the key findings from this research paper?",
+            "files": ["assets/additional-examples/research.pdf"],
         }
     ],
     [
         {
-            "text": "Solve this integral.",
-            "files": ["assets/additional-examples/4.png"],
+            "text": "Analyze the data trends in this CSV file.",
+            "files": ["assets/additional-examples/data.csv"],
         }
     ],
     [
         {
-            "text": "When was this ticket issued, and what is its price?",
-            "files": ["assets/additional-examples/2.png"],
+            "text": "Summarize the main points from this text document.",
+            "files": ["assets/additional-examples/document.txt"],
         }
     ],
-    [
-        {
-            "text": "Based on the sequence 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 plot a bar chart that matches this image.",
-            "files": ["assets/additional-examples/barchart.png"],
-        }
-    ],
-    [
-        {
-            "text": "Read the text in the image and write it out in Markdown format.",
-            "files": ["assets/additional-examples/3.png"],
-        }
-    ],
-    [
-        {
-            "text": "What does this sign say?",
-            "files": ["assets/sample-images/02.png"],
-        }
-    ],
-    [
-        {
-            "text": "Compare the two images and describe their similarities and differences.",
-            "files": ["assets/sample-images/03.png"],
-        }
-    ], 
 ]
 
 ##############################################################################
-# Gradio UI (Blocks) 구성 (좌측 사이드 메뉴 없이 전체화면 채팅)
+# Gradio UI
 ##############################################################################
 css = """
-/* 1) UI를 처음부터 가장 넓게 (width 100%) 고정하여 표시 */
 .gradio-container {
-    background: rgba(255, 255, 255, 0.7); /* 배경 투명도 증가 */
+    background: rgba(255, 255, 255, 0.7);
     padding: 30px 40px;
-    margin: 20px auto;  /* 위아래 여백만 유지 */
+    margin: 20px auto;
     width: 100% !important;
-    max-width: none !important; /* 1200px 제한 제거 */
+    max-width: none !important;
 }
 .fillable {
     width: 100% !important; 
     max-width: 100% !important; 
 }
-/* 2) 배경을 완전히 투명하게 변경 */
 body {
-    background: transparent; /* 완전 투명 배경 */
+    background: transparent;
     margin: 0;
     padding: 0;
     font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif;
     color: #333;
 }
-/* 버튼 색상 완전히 제거하고 투명하게 */
 button, .btn {
-    background: transparent !important; /* 색상 완전히 제거 */
-    border: 1px solid #ddd; /* 경계선만 살짝 추가 */
+    background: transparent !important;
+    border: 1px solid #ddd;
     color: #333;
     padding: 12px 24px;
     text-transform: uppercase;
@@ -704,93 +431,32 @@ button, .btn {
     cursor: pointer;
 }
 button:hover, .btn:hover {
-    background: rgba(0, 0, 0, 0.05) !important; /* 호버 시 아주 살짝 어둡게만 */
-}
-
-/* examples 관련 모든 색상 제거 */
-#examples_container, .examples-container {
-    margin: auto;
-    width: 90%;
-    background: transparent !important;
-}
-#examples_row, .examples-row {
-    justify-content: center;
-    background: transparent !important;
-}
-
-/* examples 버튼 내부의 모든 색상 제거 */
-.gr-samples-table button,
-.gr-samples-table .gr-button,
-.gr-samples-table .gr-sample-btn,
-.gr-examples button,
-.gr-examples .gr-button,
-.gr-examples .gr-sample-btn,
-.examples button,
-.examples .gr-button,
-.examples .gr-sample-btn {
-    background: transparent !important;
-    border: 1px solid #ddd;
-    color: #333;
-}
-
-/* examples 버튼 호버 시에도 색상 없게 */
-.gr-samples-table button:hover,
-.gr-samples-table .gr-button:hover,
-.gr-samples-table .gr-sample-btn:hover,
-.gr-examples button:hover,
-.gr-examples .gr-button:hover,
-.gr-examples .gr-sample-btn:hover,
-.examples button:hover,
-.examples .gr-button:hover,
-.examples .gr-sample-btn:hover {
     background: rgba(0, 0, 0, 0.05) !important;
 }
-
-/* 채팅 인터페이스 요소들도 투명하게 */
-.chatbox, .chatbot, .message {
-    background: transparent !important;
-}
-
-/* 입력창 투명도 조정 */
-.multimodal-textbox, textarea, input {
-    background: rgba(255, 255, 255, 0.5) !important;
-}
-
-/* 모든 컨테이너 요소에 배경색 제거 */
-.container, .wrap, .box, .panel, .gr-panel {
-    background: transparent !important;
-}
-
-/* 예제 섹션의 모든 요소에서 배경색 제거 */
-.gr-examples-container, .gr-examples, .gr-sample, .gr-sample-row, .gr-sample-cell {
-    background: transparent !important;
-}
 """
 
 title_html = """
-<h1 align="center" style="margin-bottom: 0.2em; font-size: 1.6em;"> 🤗 Gemma3-R1984-1B </h1>
+<h1 align="center" style="margin-bottom: 0.2em; font-size: 1.6em;"> 🤗 Gemma3-R1984-1B (Text Only) </h1>
 <p align="center" style="font-size:1.1em; color:#555;">
-    ✅Agentic AI Platform ✅Reasoning & Uncensored ✅Multimodal & VLM ✅Deep-Research & RAG <br>
-    Operates on an ✅'NVIDIA L40s / A100(ZeroGPU) GPU' as an independent local server, enhancing security and preventing information leakage.<br>
-    @Model Rpository: VIDraft/Gemma-3-R1984-1B, @Based by 'Google Gemma-3-1b', @Powered by 'MOUSE-II'(VIDRAFT)
+    ✅Agentic AI Platform ✅Reasoning ✅Text Analysis ✅Deep-Research & RAG <br>
+    ✅Document Processing (PDF, CSV, TXT) ✅Web Search Integration<br>
+    Operates on an ✅'NVIDIA L40s / A100(ZeroGPU) GPU' as an independent local server<br>
+    @Model Repository: VIDraft/Gemma-3-R1984-1B, @Based by 'Google Gemma-3-1b'
 </p>
 """
 
-
 with gr.Blocks(css=css, title="Gemma3-R1984-1B") 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
     )
 
-    # Used internally but not visible to the user
     system_prompt_box = gr.Textbox(
         lines=3,
         value="You are a deep thinking AI that may use extremely long chains of thought to thoroughly analyze the problem and deliberate using systematic reasoning processes to arrive at a correct solution before answering.",
-        visible=False  # hidden from view
+        visible=False
     )
     
     max_tokens_slider = gr.Slider(
@@ -799,26 +465,22 @@ with gr.Blocks(css=css, title="Gemma3-R1984-1B") as demo:
         maximum=8000,
         step=50,
         value=1000,
-        visible=False  # hidden from view
+        visible=False
     )
     
     web_search_text = gr.Textbox(
         lines=1,
         label="(Unused) Web Search Query",
         placeholder="No direct input needed",
-        visible=False  # hidden from view
+        visible=False
     )
     
-    # Configure the chat interface
     chat = gr.ChatInterface(
         fn=run,
         type="messages",
-        chatbot=gr.Chatbot(type="messages", scale=1, allow_tags=["image"]),
+        chatbot=gr.Chatbot(type="messages", scale=1),
         textbox=gr.MultimodalTextbox(
-            file_types=[
-                ".webp", ".png", ".jpg", ".jpeg", ".gif",
-                ".mp4", ".csv", ".txt", ".pdf"
-            ],
+            file_types=[".csv", ".txt", ".pdf"],  # 이미지/비디오 제거
             file_count="multiple",
             autofocus=True
         ),
@@ -838,12 +500,9 @@ with gr.Blocks(css=css, title="Gemma3-R1984-1B") as demo:
         delete_cache=(1800, 1800),
     )
 
-    # Example section - since examples are already set in ChatInterface, this is for display only
     with gr.Row(elem_id="examples_row"):
         with gr.Column(scale=12, elem_id="examples_container"):
             gr.Markdown("### Example Inputs (click to load)")
 
-
 if __name__ == "__main__":
-    # Run locally
-    demo.launch()
+    demo.launch()