Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -22,35 +22,33 @@ import pandas as pd
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import PyPDF2
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##############################################################################
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-
# SERPHouse API key
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##############################################################################
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SERPHOUSE_API_KEY = "
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##############################################################################
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#
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# - ์ค์ ํ๊ฒฝ์ ๋ง๊ฒ stopwords, ํํ์ ๋ถ์ ๋ฑ ๊ณ ๋ํ ๊ฐ๋ฅ
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##############################################################################
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def extract_keywords(text: str, top_k: int = 5) -> str:
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text = text.lower()
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# 2) ์ํ๋ฒณ/์ซ์/๊ณต๋ฐฑ ์ ์ธ ๋ฌธ์ ์ ๊ฑฐ
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text = re.sub(r"[^a-z0-9\s]", "", text)
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# 3) ๊ณต๋ฐฑ๋จ์ ํ ํฐ
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tokens = text.split()
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# 4) ์ฐ์ ์ ์์์ ๋ช ๊ฐ ํ ํฐ๋ง ์ฌ์ฉ (top_k=5)
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# - ํ์์ stopword ์ ๊ฑฐ๋ ๋น๋์ ๊ณ์ฐ ํ ์์ k๊ฐ ์ถ์ถํ๋๋ก ๋ณ๊ฒฝ ๊ฐ๋ฅ
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key_tokens = tokens[:top_k]
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# 5) ๊ณต๋ฐฑ์ผ๋ก join
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return " ".join(key_tokens)
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##############################################################################
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#
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# https://api.serphouse.com/serp/live
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##############################################################################
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def do_web_search(query: str) -> str:
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"""
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Returns top-20 results' titles as a bullet list, or an error message.
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"""
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try:
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url = "https://api.serphouse.com/serp/live"
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@@ -60,11 +58,11 @@ def do_web_search(query: str) -> str:
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"lang": "en",
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"device": "desktop",
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"serp_type": "web",
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"num_result": "20", #
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"api_token": SERPHOUSE_API_KEY,
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}
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resp = requests.get(url, params=params, timeout=30)
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resp.raise_for_status() #
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data = resp.json()
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results = data.get("results", {})
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@@ -72,21 +70,21 @@ def do_web_search(query: str) -> str:
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if not organic:
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return "No web search results found."
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# ์์ 20๊ฐ ์ ๋ชฉ๋ง ๋ฝ์์ ์ ๋ฆฌ
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summary_lines = []
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for idx, item in enumerate(organic[:20], start=1):
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title = item.get("title", "No Title")
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summary_lines.append(f"{idx}. {title}")
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# 20๊ฐ๋ฅผ \n ์ผ๋ก ์ฐ๊ฒฐ
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return "\n".join(summary_lines)
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except Exception as e:
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logger.error(f"Web search failed: {e}")
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return f"Web search failed: {str(e)}"
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model_id = os.getenv("MODEL_ID", "google/gemma-3-27b-it")
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processor = AutoProcessor.from_pretrained(model_id, padding_side="left")
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model = Gemma3ForConditionalGeneration.from_pretrained(
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@@ -95,16 +93,19 @@ model = Gemma3ForConditionalGeneration.from_pretrained(
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torch_dtype=torch.bfloat16,
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attn_implementation="eager"
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)
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MAX_NUM_IMAGES = int(os.getenv("MAX_NUM_IMAGES", "5"))
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# CSV, TXT, PDF ๋ถ์ ํจ์
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def analyze_csv_file(path: str) -> str:
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try:
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df = pd.read_csv(path)
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if df.shape[0] > 50 or df.shape[1] > 10:
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df = df.iloc[:50, :10]
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df_str = df.to_string()
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@@ -116,6 +117,9 @@ def analyze_csv_file(path: str) -> str:
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def analyze_txt_file(path: str) -> str:
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try:
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with open(path, "r", encoding="utf-8") as f:
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text = f.read()
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@@ -127,6 +131,9 @@ def analyze_txt_file(path: str) -> str:
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def pdf_to_markdown(pdf_path: str) -> str:
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text_chunks = []
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try:
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with open(pdf_path, "rb") as f:
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@@ -137,6 +144,7 @@ def pdf_to_markdown(pdf_path: str) -> str:
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page_text = page.extract_text() or ""
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page_text = page_text.strip()
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if page_text:
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if len(page_text) > MAX_CONTENT_CHARS // max_pages:
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page_text = page_text[:MAX_CONTENT_CHARS // max_pages] + "...(truncated)"
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text_chunks.append(f"## Page {page_num+1}\n\n{page_text}\n")
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@@ -152,9 +160,9 @@ def pdf_to_markdown(pdf_path: str) -> str:
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return f"**[PDF File: {os.path.basename(pdf_path)}]**\n\n{full_text}"
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-
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# ์ด๋ฏธ์ง/๋น๋์ค ์
๋ก๋ ์ ํ ๊ฒ์ฌ
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def count_files_in_new_message(paths: list[str]) -> tuple[int, int]:
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image_count = 0
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video_count = 0
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@@ -183,6 +191,13 @@ def count_files_in_history(history: list[dict]) -> tuple[int, int]:
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def validate_media_constraints(message: dict, history: list[dict]) -> bool:
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media_files = []
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for f in message["files"]:
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if re.search(r"\.(png|jpg|jpeg|gif|webp)$", f, re.IGNORECASE) or f.endswith(".mp4"):
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@@ -217,14 +232,14 @@ def validate_media_constraints(message: dict, history: list[dict]) -> bool:
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return True
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-
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# ๋น๋์ค ์ฒ๋ฆฌ
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def downsample_video(video_path: str) -> list[tuple[Image.Image, float]]:
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vidcap = cv2.VideoCapture(video_path)
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fps = vidcap.get(cv2.CAP_PROP_FPS)
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total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
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frame_interval = max(int(fps), int(total_frames / 10))
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frames = []
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for i in range(0, total_frames, frame_interval):
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@@ -255,9 +270,9 @@ def process_video(video_path: str) -> list[dict]:
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return content
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# interleaved <image> ์ฒ๋ฆฌ
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def process_interleaved_images(message: dict) -> list[dict]:
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parts = re.split(r"(<image>)", message["text"])
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content = []
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@@ -277,9 +292,9 @@ def process_interleaved_images(message: dict) -> list[dict]:
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return content
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# PDF + CSV + TXT + ์ด๋ฏธ์ง/๋น๋์ค
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def is_image_file(file_path: str) -> bool:
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return bool(re.search(r"\.(png|jpg|jpeg|gif|webp)$", file_path, re.IGNORECASE))
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@@ -287,9 +302,12 @@ def is_video_file(file_path: str) -> bool:
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return file_path.endswith(".mp4")
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def is_document_file(file_path: str) -> bool:
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return (
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def process_new_user_message(message: dict) -> list[dict]:
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if not message["files"]:
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if "<image>" in message["text"] and image_files:
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interleaved_content = process_interleaved_images({"text": message["text"], "files": image_files})
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if content_list[0]["type"] == "text":
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content_list = content_list[1:]
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return interleaved_content + content_list
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else:
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return content_list
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# history -> LLM ๋ฉ์์ง ๋ณํ
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def process_history(history: list[dict]) -> list[dict]:
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messages = []
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current_user_content: list[dict] = []
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return messages
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# ๋ฉ์ธ ์ถ๋ก ํจ์
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@spaces.GPU(duration=120)
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def run(
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message: dict,
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return
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try:
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#
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#
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if use_web_search:
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user_text = message["text"]
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# ํค์๋ ์ถ์ถ
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ws_query = extract_keywords(user_text, top_k=5)
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logger.info(f"[Auto WebSearch Keyword] {ws_query!r}")
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# ์์ 20๊ฐ ๊ฒฐ๊ณผ
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ws_result = do_web_search(ws_query)
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#
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system_search_content = f"[Search top-20 Titles Based on user prompt]\n{ws_result}\n"
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# system ๋ฉ์์ง๋ก ์ถ๊ฐ
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# (LLM์ด ์ด ์ ๋ณด๋ฅผ ์ฐธ๊ณ ํ๋๋ก)
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if system_search_content.strip():
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history_system_msg = {
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"role": "system",
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"role": "system",
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"content": [{"type": "text", "text": "No web search results"}]
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}
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else:
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history_system_msg = None
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": [{"type": "text", "text": system_prompt}]})
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#
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if history_system_msg:
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messages.append(history_system_msg)
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messages.extend(process_history(history))
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user_content = process_new_user_message(message)
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for item in user_content:
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if item["type"] == "text" and len(item["text"]) > MAX_CONTENT_CHARS:
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item["text"] = item["text"][:MAX_CONTENT_CHARS] + "\n...(truncated)..."
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messages.append({"role": "user", "content": user_content})
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inputs = processor.apply_chat_template(
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messages,
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add_generation_prompt=True,
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for new_text in streamer:
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output += new_text
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yield output
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except Exception as e:
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logger.error(f"Error in run: {str(e)}")
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yield f"์ฃ์กํฉ๋๋ค. ์ค๋ฅ๊ฐ ๋ฐ์ํ์ต๋๋ค: {str(e)}"
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-
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examples = [
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-
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[
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{
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"text": "๋ PDF ํ์ผ ๋ด์ฉ์ ๋น๊ตํ๋ผ.",
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"files": [
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"assets/additional-examples/before.pdf",
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"assets/additional-examples/after.pdf",
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],
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}
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],
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[
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"text": "CSV ํ์ผ ๋ด์ฉ์ ์์ฝ, ๋ถ์ํ๋ผ",
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"files": ["assets/additional-examples/sample-csv.csv"],
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}
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],
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[
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{
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"text": "์ด ์์์ ๋ด์ฉ์ ์ค๋ช
ํ๋ผ",
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"files": ["assets/additional-examples/tmp.mp4"],
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}
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],
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[
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{
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"text": "ํ์ง ๋ด์ฉ์ ์ค๋ช
ํ๊ณ ๊ธ์๋ฅผ ์ฝ์ด์ฃผ์ธ์.",
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"files": ["assets/additional-examples/maz.jpg"],
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}
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],
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[
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{
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"text": "์ด๋ฏธ ์ด ์์์ ๋ฅผ <image> ๊ฐ์ง๊ณ ์๊ณ , ์ด ์ ํ <image>์ ์๋ก ์ฌ๋ ค ํฉ๋๋ค. ํจ๊ป ์ญ์ทจํ ๋ ์ฃผ์ํด์ผ ํ ์ ์ด ์์๊น์?",
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"files": ["assets/additional-examples/pill1.png", "assets/additional-examples/pill2.png"],
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}
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],
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[
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{
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"text": "์ด ์ ๋ถ๏ฟฝ๏ฟฝ๏ฟฝ ํ์ด์ฃผ์ธ์.",
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"files": ["assets/additional-examples/4.png"],
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}
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],
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[
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{
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"text": "์ด ํฐ์ผ์ ์ธ์ ๋ฐ๊ธ๋ ๊ฒ์ด๊ณ , ๊ฐ๊ฒฉ์ ์ผ๋ง์ธ๊ฐ์?",
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"files": ["assets/additional-examples/2.png"],
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}
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],
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[
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{
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"text": "์ด๋ฏธ์ง๋ค์ ์์๋ฅผ ๋ฐํ์ผ๋ก ์งง์ ์ด์ผ๊ธฐ๋ฅผ ๋ง๋ค์ด ์ฃผ์ธ์.",
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"text": "๋์ผํ ๋ง๋ ๊ทธ๋ํ๋ฅผ ๊ทธ๋ฆฌ๋ matplotlib ์ฝ๋๋ฅผ ์์ฑํด์ฃผ์ธ์.",
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"files": ["assets/additional-examples/barchart.png"],
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}
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],
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[
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{
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"text": "์ด ์ธ๊ณ์์ ์ด๊ณ ์์ ์๋ฌผ๋ค์ ์์ํด์ ๋ฌ์ฌํด์ฃผ์ธ์.",
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"files": ["assets/sample-images/08.png"],
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}
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],
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-
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[
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{
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"text": "์ด๋ฏธ์ง์ ์๋ ํ
์คํธ๋ฅผ ๊ทธ๋๋ก ์ฝ์ด์ ๋งํฌ๋ค์ด ํํ๋ก ์ ์ด์ฃผ์ธ์.",
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"files": ["assets/additional-examples/3.png"],
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}
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],
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-
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[
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{
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"text": "์ด ํ์งํ์๋ ๋ฌด์จ ๋ฌธ๊ตฌ๊ฐ ์ ํ ์๋์?",
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"files": ["assets/sample-images/03.png"],
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}
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],
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-
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]
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css = """
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body {
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background: linear-gradient(135deg, #667eea, #764ba2);
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web_search_checkbox = gr.Checkbox(
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label="Web Search",
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value=False,
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info="Check to enable a SERPHouse web search before the chat reply"
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)
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#
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web_search_text = gr.Textbox(
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lines=1,
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label="(Unused) Web Search Query",
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value=2000,
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)
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gr.Markdown("<br><br>")
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with gr.Column(scale=7):
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chat = gr.ChatInterface(
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fn=run,
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system_prompt_box,
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max_tokens_slider,
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web_search_checkbox,
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web_search_text, # ์ค์ ๋ก๋
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],
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stop_btn=False,
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title="Vidraft-Gemma-3-27B",
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gr.Markdown("### Example Inputs (click to load)")
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gr.Examples(
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examples=examples,
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inputs=[], #
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cache_examples=False
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)
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import PyPDF2
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##############################################################################
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+
# SERPHouse API key from environment variable
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##############################################################################
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27 |
+
SERPHOUSE_API_KEY = os.getenv("SERPHOUSE_API_KEY", "")
|
28 |
|
29 |
##############################################################################
|
30 |
+
# ๊ฐ๋จํ ํค์๋ ์ถ์ถ ํจ์ (์ฌ์ฉ์ ํ๋กฌํํธ -> ํค์๋)
|
|
|
31 |
##############################################################################
|
32 |
def extract_keywords(text: str, top_k: int = 5) -> str:
|
33 |
+
"""
|
34 |
+
๊ฐ์ฅ ๊ฐ๋จํ ์์:
|
35 |
+
1) ํ
์คํธ๋ฅผ ์๋ฌธ์๋ก
|
36 |
+
2) ์ํ๋ฒณ/์ซ์/๊ณต๋ฐฑ ์ ์ธ ๋ฌธ์ ์ ๊ฑฐ
|
37 |
+
3) ๊ณต๋ฐฑ ํ ํฐ ๋ถ๋ฆฌ
|
38 |
+
4) ์ ํ ํฐ n๊ฐ ์ถ์ถ
|
39 |
+
"""
|
40 |
text = text.lower()
|
|
|
41 |
text = re.sub(r"[^a-z0-9\s]", "", text)
|
|
|
42 |
tokens = text.split()
|
|
|
|
|
43 |
key_tokens = tokens[:top_k]
|
|
|
44 |
return " ".join(key_tokens)
|
45 |
|
46 |
##############################################################################
|
47 |
+
# SERPHouse Live endpoint ํธ์ถ (์์ 20๊ฐ์ ์ ๋ชฉ์ ์ป์)
|
|
|
48 |
##############################################################################
|
49 |
def do_web_search(query: str) -> str:
|
50 |
"""
|
51 |
+
SERPHouse ๋ผ์ด๋ธ ๊ฒ์ ํธ์ถ, ์์ 20๊ฐ ๊ฒฐ๊ณผ์ 'title'๋ง ๋ฌถ์ด์ ๋ฐํ.
|
|
|
52 |
"""
|
53 |
try:
|
54 |
url = "https://api.serphouse.com/serp/live"
|
|
|
58 |
"lang": "en",
|
59 |
"device": "desktop",
|
60 |
"serp_type": "web",
|
61 |
+
"num_result": "20", # ์์ 20๊ฐ ๊ฒฐ๊ณผ
|
62 |
"api_token": SERPHOUSE_API_KEY,
|
63 |
}
|
64 |
resp = requests.get(url, params=params, timeout=30)
|
65 |
+
resp.raise_for_status() # 4xx/5xx ์๋ฌ ์ ์์ธ
|
66 |
data = resp.json()
|
67 |
|
68 |
results = data.get("results", {})
|
|
|
70 |
if not organic:
|
71 |
return "No web search results found."
|
72 |
|
|
|
73 |
summary_lines = []
|
74 |
for idx, item in enumerate(organic[:20], start=1):
|
75 |
title = item.get("title", "No Title")
|
76 |
summary_lines.append(f"{idx}. {title}")
|
77 |
|
|
|
78 |
return "\n".join(summary_lines)
|
79 |
except Exception as e:
|
80 |
logger.error(f"Web search failed: {e}")
|
81 |
return f"Web search failed: {str(e)}"
|
82 |
|
83 |
|
84 |
+
##############################################################################
|
85 |
+
# ์์ ์ค์
|
86 |
+
##############################################################################
|
87 |
+
MAX_CONTENT_CHARS = 4000 # ๋๋ฌด ํฐ ํ์ผ์ ๋ง๊ธฐ ์ํด ์ต๋ 4000์๋ง ํ์
|
88 |
model_id = os.getenv("MODEL_ID", "google/gemma-3-27b-it")
|
89 |
processor = AutoProcessor.from_pretrained(model_id, padding_side="left")
|
90 |
model = Gemma3ForConditionalGeneration.from_pretrained(
|
|
|
93 |
torch_dtype=torch.bfloat16,
|
94 |
attn_implementation="eager"
|
95 |
)
|
|
|
96 |
MAX_NUM_IMAGES = int(os.getenv("MAX_NUM_IMAGES", "5"))
|
97 |
|
98 |
|
99 |
+
##############################################################################
|
100 |
# CSV, TXT, PDF ๋ถ์ ํจ์
|
101 |
+
##############################################################################
|
102 |
def analyze_csv_file(path: str) -> str:
|
103 |
+
"""
|
104 |
+
CSV ํ์ผ์ ์ ์ฒด ๋ฌธ์์ด๋ก ๋ณํ. ๋๋ฌด ๊ธธ ๊ฒฝ์ฐ ์ผ๋ถ๋ง ํ์.
|
105 |
+
"""
|
106 |
try:
|
107 |
df = pd.read_csv(path)
|
108 |
+
# ์ต๋ 50ํ, 10์ด๊น์ง๋ง ํ์
|
109 |
if df.shape[0] > 50 or df.shape[1] > 10:
|
110 |
df = df.iloc[:50, :10]
|
111 |
df_str = df.to_string()
|
|
|
117 |
|
118 |
|
119 |
def analyze_txt_file(path: str) -> str:
|
120 |
+
"""
|
121 |
+
TXT ํ์ผ ์ ๋ฌธ ์ฝ๊ธฐ. ๋๋ฌด ๊ธธ๋ฉด ์ผ๋ถ๋ง ํ์.
|
122 |
+
"""
|
123 |
try:
|
124 |
with open(path, "r", encoding="utf-8") as f:
|
125 |
text = f.read()
|
|
|
131 |
|
132 |
|
133 |
def pdf_to_markdown(pdf_path: str) -> str:
|
134 |
+
"""
|
135 |
+
PDF โ Markdown. ํ์ด์ง๋ณ๋ก ๊ฐ๋จํ ํ
์คํธ ์ถ์ถ.
|
136 |
+
"""
|
137 |
text_chunks = []
|
138 |
try:
|
139 |
with open(pdf_path, "rb") as f:
|
|
|
144 |
page_text = page.extract_text() or ""
|
145 |
page_text = page_text.strip()
|
146 |
if page_text:
|
147 |
+
# ํ์ด์ง๋ณ ํ
์คํธ ์ ํ
|
148 |
if len(page_text) > MAX_CONTENT_CHARS // max_pages:
|
149 |
page_text = page_text[:MAX_CONTENT_CHARS // max_pages] + "...(truncated)"
|
150 |
text_chunks.append(f"## Page {page_num+1}\n\n{page_text}\n")
|
|
|
160 |
return f"**[PDF File: {os.path.basename(pdf_path)}]**\n\n{full_text}"
|
161 |
|
162 |
|
163 |
+
##############################################################################
|
164 |
# ์ด๋ฏธ์ง/๋น๋์ค ์
๋ก๋ ์ ํ ๊ฒ์ฌ
|
165 |
+
##############################################################################
|
166 |
def count_files_in_new_message(paths: list[str]) -> tuple[int, int]:
|
167 |
image_count = 0
|
168 |
video_count = 0
|
|
|
191 |
|
192 |
|
193 |
def validate_media_constraints(message: dict, history: list[dict]) -> bool:
|
194 |
+
"""
|
195 |
+
- ๋น๋์ค 1๊ฐ ์ด๊ณผ ๋ถ๊ฐ
|
196 |
+
- ๋น๋์ค์ ์ด๋ฏธ์ง ํผํฉ ๋ถ๊ฐ
|
197 |
+
- ์ด๋ฏธ์ง ๊ฐ์(MAX_NUM_IMAGES) ์ด๊ณผ ๋ถ๊ฐ
|
198 |
+
- <image> ํ๊ทธ๊ฐ ์์ผ๋ฉด ํ๊ทธ ์์ ์ค์ ์ด๋ฏธ์ง ์ ์ผ์น
|
199 |
+
- CSV, TXT, PDF ๋ฑ์ ์ฌ๊ธฐ์ ์ ํํ์ง ์์
|
200 |
+
"""
|
201 |
media_files = []
|
202 |
for f in message["files"]:
|
203 |
if re.search(r"\.(png|jpg|jpeg|gif|webp)$", f, re.IGNORECASE) or f.endswith(".mp4"):
|
|
|
232 |
return True
|
233 |
|
234 |
|
235 |
+
##############################################################################
|
236 |
# ๋น๋์ค ์ฒ๋ฆฌ
|
237 |
+
##############################################################################
|
238 |
def downsample_video(video_path: str) -> list[tuple[Image.Image, float]]:
|
239 |
vidcap = cv2.VideoCapture(video_path)
|
240 |
fps = vidcap.get(cv2.CAP_PROP_FPS)
|
241 |
total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
|
242 |
+
frame_interval = max(int(fps), int(total_frames / 10))
|
243 |
frames = []
|
244 |
|
245 |
for i in range(0, total_frames, frame_interval):
|
|
|
270 |
return content
|
271 |
|
272 |
|
273 |
+
##############################################################################
|
274 |
# interleaved <image> ์ฒ๋ฆฌ
|
275 |
+
##############################################################################
|
276 |
def process_interleaved_images(message: dict) -> list[dict]:
|
277 |
parts = re.split(r"(<image>)", message["text"])
|
278 |
content = []
|
|
|
292 |
return content
|
293 |
|
294 |
|
295 |
+
##############################################################################
|
296 |
# PDF + CSV + TXT + ์ด๋ฏธ์ง/๋น๋์ค
|
297 |
+
##############################################################################
|
298 |
def is_image_file(file_path: str) -> bool:
|
299 |
return bool(re.search(r"\.(png|jpg|jpeg|gif|webp)$", file_path, re.IGNORECASE))
|
300 |
|
|
|
302 |
return file_path.endswith(".mp4")
|
303 |
|
304 |
def is_document_file(file_path: str) -> bool:
|
305 |
+
return (
|
306 |
+
file_path.lower().endswith(".pdf")
|
307 |
+
or file_path.lower().endswith(".csv")
|
308 |
+
or file_path.lower().endswith(".txt")
|
309 |
+
)
|
310 |
+
|
311 |
|
312 |
def process_new_user_message(message: dict) -> list[dict]:
|
313 |
if not message["files"]:
|
|
|
339 |
|
340 |
if "<image>" in message["text"] and image_files:
|
341 |
interleaved_content = process_interleaved_images({"text": message["text"], "files": image_files})
|
342 |
+
if content_list and content_list[0]["type"] == "text":
|
343 |
content_list = content_list[1:]
|
344 |
return interleaved_content + content_list
|
345 |
else:
|
|
|
349 |
return content_list
|
350 |
|
351 |
|
352 |
+
##############################################################################
|
353 |
# history -> LLM ๋ฉ์์ง ๋ณํ
|
354 |
+
##############################################################################
|
355 |
def process_history(history: list[dict]) -> list[dict]:
|
356 |
messages = []
|
357 |
current_user_content: list[dict] = []
|
|
|
378 |
return messages
|
379 |
|
380 |
|
381 |
+
##############################################################################
|
382 |
+
# ๋ฉ์ธ ์ถ๋ก ํจ์ (web search ์ฒดํฌ ์ ์๋ ํค์๋์ถ์ถ->๊ฒ์->๊ฒฐ๊ณผ system msg ๋ฐ์)
|
383 |
+
##############################################################################
|
384 |
@spaces.GPU(duration=120)
|
385 |
def run(
|
386 |
message: dict,
|
|
|
396 |
return
|
397 |
|
398 |
try:
|
399 |
+
# web_search๊ฐ True๋ฉด => ์ฌ์ฉ์๊ฐ ์ง์ ์
๋ ฅํ web_search_query ๋์ ,
|
400 |
+
# message["text"]๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ํค์๋ ์ถ์ถํ์ฌ ๊ฒ์
|
401 |
+
history_system_msg = None
|
402 |
if use_web_search:
|
403 |
user_text = message["text"]
|
404 |
+
# 1) ํค์๋ ์ถ์ถ
|
405 |
ws_query = extract_keywords(user_text, top_k=5)
|
406 |
logger.info(f"[Auto WebSearch Keyword] {ws_query!r}")
|
407 |
+
# 2) ์์ 20๊ฐ ๊ฒฐ๊ณผ ๋ถ๋ฌ์ค๊ธฐ
|
408 |
ws_result = do_web_search(ws_query)
|
409 |
+
# 3) ์ด๋ฅผ system ๋ฉ์์ง๋ก ์ถ๊ฐ
|
410 |
system_search_content = f"[Search top-20 Titles Based on user prompt]\n{ws_result}\n"
|
|
|
|
|
411 |
if system_search_content.strip():
|
412 |
history_system_msg = {
|
413 |
"role": "system",
|
|
|
418 |
"role": "system",
|
419 |
"content": [{"type": "text", "text": "No web search results"}]
|
420 |
}
|
|
|
|
|
421 |
|
422 |
+
# ๊ธฐ์กด system prompt
|
423 |
messages = []
|
424 |
if system_prompt:
|
425 |
messages.append({"role": "system", "content": [{"type": "text", "text": system_prompt}]})
|
426 |
+
# web ๊ฒ์ ๊ฒฐ๊ณผ system msg
|
427 |
if history_system_msg:
|
428 |
messages.append(history_system_msg)
|
429 |
|
430 |
+
# ์ด์ ๋ํ์ด๋ ฅ(assistant/user)
|
431 |
messages.extend(process_history(history))
|
432 |
|
433 |
+
# ์ ์ ์ ๋ฉ์์ง ๋ณํ
|
434 |
user_content = process_new_user_message(message)
|
435 |
for item in user_content:
|
436 |
if item["type"] == "text" and len(item["text"]) > MAX_CONTENT_CHARS:
|
437 |
item["text"] = item["text"][:MAX_CONTENT_CHARS] + "\n...(truncated)..."
|
|
|
438 |
messages.append({"role": "user", "content": user_content})
|
439 |
|
440 |
+
# LLM ์
๋ ฅ ์์ฑ
|
441 |
inputs = processor.apply_chat_template(
|
442 |
messages,
|
443 |
add_generation_prompt=True,
|
|
|
460 |
for new_text in streamer:
|
461 |
output += new_text
|
462 |
yield output
|
463 |
+
|
464 |
except Exception as e:
|
465 |
logger.error(f"Error in run: {str(e)}")
|
466 |
yield f"์ฃ์กํฉ๋๋ค. ์ค๋ฅ๊ฐ ๋ฐ์ํ์ต๋๋ค: {str(e)}"
|
467 |
|
468 |
|
469 |
+
##############################################################################
|
470 |
+
# ์์๋ค (ํ๊ธํ)
|
471 |
+
##############################################################################
|
472 |
examples = [
|
|
|
473 |
[
|
474 |
{
|
475 |
"text": "๋ PDF ํ์ผ ๋ด์ฉ์ ๋น๊ตํ๋ผ.",
|
|
|
477 |
"files": [
|
478 |
"assets/additional-examples/before.pdf",
|
479 |
"assets/additional-examples/after.pdf",
|
480 |
+
],
|
481 |
}
|
482 |
],
|
483 |
[
|
|
|
485 |
"text": "CSV ํ์ผ ๋ด์ฉ์ ์์ฝ, ๋ถ์ํ๋ผ",
|
486 |
"files": ["assets/additional-examples/sample-csv.csv"],
|
487 |
}
|
488 |
+
],
|
489 |
[
|
490 |
{
|
491 |
"text": "์ด ์์์ ๋ด์ฉ์ ์ค๋ช
ํ๋ผ",
|
492 |
"files": ["assets/additional-examples/tmp.mp4"],
|
493 |
}
|
494 |
+
],
|
495 |
[
|
496 |
{
|
497 |
"text": "ํ์ง ๋ด์ฉ์ ์ค๋ช
ํ๊ณ ๊ธ์๋ฅผ ์ฝ์ด์ฃผ์ธ์.",
|
498 |
"files": ["assets/additional-examples/maz.jpg"],
|
499 |
}
|
500 |
+
],
|
501 |
[
|
502 |
{
|
503 |
"text": "์ด๋ฏธ ์ด ์์์ ๋ฅผ <image> ๊ฐ์ง๊ณ ์๊ณ , ์ด ์ ํ <image>์ ์๋ก ์ฌ๋ ค ํฉ๋๋ค. ํจ๊ป ์ญ์ทจํ ๋ ์ฃผ์ํด์ผ ํ ์ ์ด ์์๊น์?",
|
504 |
"files": ["assets/additional-examples/pill1.png", "assets/additional-examples/pill2.png"],
|
505 |
}
|
506 |
+
],
|
507 |
[
|
508 |
{
|
509 |
"text": "์ด ์ ๋ถ๏ฟฝ๏ฟฝ๏ฟฝ ํ์ด์ฃผ์ธ์.",
|
510 |
"files": ["assets/additional-examples/4.png"],
|
511 |
}
|
512 |
+
],
|
513 |
[
|
514 |
{
|
515 |
"text": "์ด ํฐ์ผ์ ์ธ์ ๋ฐ๊ธ๋ ๊ฒ์ด๊ณ , ๊ฐ๊ฒฉ์ ์ผ๋ง์ธ๊ฐ์?",
|
516 |
"files": ["assets/additional-examples/2.png"],
|
517 |
}
|
518 |
+
],
|
519 |
[
|
520 |
{
|
521 |
"text": "์ด๋ฏธ์ง๋ค์ ์์๋ฅผ ๋ฐํ์ผ๋ก ์งง์ ์ด์ผ๊ธฐ๋ฅผ ๋ง๋ค์ด ์ฃผ์ธ์.",
|
|
|
539 |
"text": "๋์ผํ ๋ง๋ ๊ทธ๋ํ๋ฅผ ๊ทธ๋ฆฌ๋ matplotlib ์ฝ๋๋ฅผ ์์ฑํด์ฃผ์ธ์.",
|
540 |
"files": ["assets/additional-examples/barchart.png"],
|
541 |
}
|
542 |
+
],
|
|
|
543 |
[
|
544 |
{
|
545 |
"text": "์ด ์ธ๊ณ์์ ์ด๊ณ ์์ ์๋ฌผ๋ค์ ์์ํด์ ๋ฌ์ฌํด์ฃผ์ธ์.",
|
546 |
"files": ["assets/sample-images/08.png"],
|
547 |
}
|
548 |
],
|
|
|
|
|
549 |
[
|
550 |
{
|
551 |
"text": "์ด๋ฏธ์ง์ ์๋ ํ
์คํธ๋ฅผ ๊ทธ๋๋ก ์ฝ์ด์ ๋งํฌ๋ค์ด ํํ๋ก ์ ์ด์ฃผ์ธ์.",
|
552 |
"files": ["assets/additional-examples/3.png"],
|
553 |
}
|
554 |
],
|
|
|
|
|
555 |
[
|
556 |
{
|
557 |
"text": "์ด ํ์งํ์๋ ๋ฌด์จ ๋ฌธ๊ตฌ๊ฐ ์ ํ ์๋์?",
|
|
|
564 |
"files": ["assets/sample-images/03.png"],
|
565 |
}
|
566 |
],
|
|
|
567 |
]
|
568 |
|
569 |
|
570 |
+
##############################################################################
|
571 |
+
# Gradio UI (Blocks) ๊ตฌ์ฑ
|
572 |
+
##############################################################################
|
573 |
css = """
|
574 |
body {
|
575 |
background: linear-gradient(135deg, #667eea, #764ba2);
|
|
|
642 |
web_search_checkbox = gr.Checkbox(
|
643 |
label="Web Search",
|
644 |
value=False,
|
645 |
+
info="Check to enable a SERPHouse web search (auto keywords) before the chat reply"
|
646 |
)
|
647 |
+
# ์ค์ ๋ก๋ ์๋์ถ์ถ. ์๋ textbox๋ ๋ฏธ์ฌ์ฉ.
|
648 |
web_search_text = gr.Textbox(
|
649 |
lines=1,
|
650 |
label="(Unused) Web Search Query",
|
|
|
669 |
value=2000,
|
670 |
)
|
671 |
|
672 |
+
gr.Markdown("<br><br>") # spacing
|
673 |
|
674 |
+
# Main ChatInterface to the right
|
675 |
with gr.Column(scale=7):
|
676 |
chat = gr.ChatInterface(
|
677 |
fn=run,
|
|
|
690 |
system_prompt_box,
|
691 |
max_tokens_slider,
|
692 |
web_search_checkbox,
|
693 |
+
web_search_text, # ์ค์ ๋ก๋ auto search
|
694 |
],
|
695 |
stop_btn=False,
|
696 |
title="Vidraft-Gemma-3-27B",
|
|
|
706 |
gr.Markdown("### Example Inputs (click to load)")
|
707 |
gr.Examples(
|
708 |
examples=examples,
|
709 |
+
inputs=[], # ๋งํฌํ inputs๊ฐ ์์ผ๋ฏ๋ก ๋น ๋ฆฌ์คํธ
|
710 |
cache_examples=False
|
711 |
)
|
712 |
|