app.py

#!/usr/bin/env python

import os
import re
import tempfile
from collections.abc import Iterator
from threading import Thread

import cv2
import gradio as gr
import spaces
import torch
from loguru import logger
from PIL import Image
from transformers import AutoProcessor, Gemma3ForConditionalGeneration, TextIteratorStreamer

# CSV/TXT 분석
import pandas as pd
# PDF 텍스트 추출용
import PyPDF2

##################################################
# 상수 및 모델 로딩
##################################################
MAX_CONTENT_CHARS = 8000  # 너무 큰 파일 내용은 이 정도까지만 표시
model_id = os.getenv("MODEL_ID", "google/gemma-3-27b-it")

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"))


##################################################
# 1) CSV, TXT, PDF 분석 함수
##################################################
def analyze_csv_file(path: str) -> str:
    """CSV 파일을 읽어 문자열화. 너무 길면 일부만 출력."""
    try:
        df = pd.read_csv(path)
        df_str = df.to_string()
        if len(df_str) > MAX_CONTENT_CHARS:
            df_str = df_str[:MAX_CONTENT_CHARS] + "\n...(truncated)..."
        return f"**[CSV File: {os.path.basename(path)}]**\n\n{df_str}"
    except Exception as e:
        return f"Failed to read CSV ({os.path.basename(path)}): {str(e)}"


def analyze_txt_file(path: str) -> str:
    """TXT 파일 전체를 읽어 문자열 반환. 너무 길면 잘라냄."""
    try:
        with open(path, "r", encoding="utf-8") as f:
            text = f.read()
        if len(text) > MAX_CONTENT_CHARS:
            text = text[:MAX_CONTENT_CHARS] + "\n...(truncated)..."
        return f"**[TXT File: {os.path.basename(path)}]**\n\n{text}"
    except Exception as e:
        return f"Failed to read TXT ({os.path.basename(path)}): {str(e)}"


def pdf_to_markdown(pdf_path: str) -> str:
    """PDF -> 텍스트 추출 -> Markdown 형식으로 변환. 너무 길면 자름."""
    text_chunks = []
    try:
        with open(pdf_path, "rb") as f:
            reader = PyPDF2.PdfReader(f)
            for page_num, page in enumerate(reader.pages, start=1):
                page_text = page.extract_text() or ""
                page_text = page_text.strip()
                if page_text:
                    text_chunks.append(f"## Page {page_num}\n\n{page_text}\n")
    except Exception as e:
        return f"Failed to read PDF ({os.path.basename(pdf_path)}): {str(e)}"

    full_text = "\n".join(text_chunks)
    if len(full_text) > MAX_CONTENT_CHARS:
        full_text = full_text[:MAX_CONTENT_CHARS] + "\n...(truncated)..."

    return f"**[PDF File: {os.path.basename(pdf_path)}]**\n\n{full_text}"


##################################################
# 2) 이미지/비디오 개수 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
        else:
            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:
        # assistant 메시지이거나 content가 str이면 제외
        if item["role"] != "user" or isinstance(item["content"], str):
            continue
        # 이미지/비디오 경로로만 카운트
        if item["content"][0].endswith(".mp4"):
            video_count += 1
        else:
            image_count += 1
    return image_count, video_count


def validate_media_constraints(message: dict, history: list[dict]) -> bool:
    """
    - 이미지/비디오만 대상으로 개수·혼합 제한
    - CSV, PDF, TXT 등은 대상 제외
    - <image> 태그와 실제 이미지 수가 일치하는지 등
    """
    media_files = []
    for f in message["files"]:
        # 이미지 확장자 또는 .mp4
        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

    # 비디오 1개 초과 불가
    if video_count > 1:
        gr.Warning("Only one video is supported.")
        return False
    # 비디오 + 이미지 혼합 불가
    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
    # 이미지 개수 제한
    if video_count == 0 and image_count > MAX_NUM_IMAGES:
        gr.Warning(f"You can upload up to {MAX_NUM_IMAGES} images.")
        return False
    # <image> 태그와 실제 이미지 수가 일치?
    if "<image>" in message["text"] and message["text"].count("<image>") != new_image_count:
        gr.Warning("The number of <image> tags in the text does not match the number of images.")
        return False

    return True


##################################################
# 3) 비디오 처리
##################################################
def downsample_video(video_path: str) -> list[tuple[Image.Image, float]]:
    """영상에서 일정 간격으로 프레임을 추출, PIL 이미지와 timestamp 반환."""
    vidcap = cv2.VideoCapture(video_path)
    fps = vidcap.get(cv2.CAP_PROP_FPS)
    total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
    frame_interval = int(fps / 3)

    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)
            pil_image = Image.fromarray(image)
            timestamp = round(i / fps, 2)
            frames.append((pil_image, timestamp))
    vidcap.release()
    return frames


def process_video(video_path: str) -> list[dict]:
    content = []
    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)
            content.append({"type": "text", "text": f"Frame {timestamp}:"})
            content.append({"type": "image", "url": temp_file.name})
    logger.debug(f"{content=}")
    return content


##################################################
# 4) interleaved <image> 처리
##################################################
def process_interleaved_images(message: dict) -> list[dict]:
    parts = re.split(r"(<image>)", message["text"])
    content = []
    image_index = 0
    for part in parts:
        if part == "<image>":
            content.append({"type": "image", "url": message["files"][image_index]})
            image_index += 1
        elif part.strip():
            content.append({"type": "text", "text": part.strip()})
        else:
            # 공백만 있는 경우
            if isinstance(part, str) and part != "<image>":
                content.append({"type": "text", "text": part})
    return content


##################################################
# 5) CSV/PDF/TXT는 텍스트로만, 이미지/비디오는 경로로
##################################################
def process_new_user_message(message: dict) -> list[dict]:
    if not message["files"]:
        return [{"type": "text", "text": message["text"]}]

    # 확장자별 분류
    video_files = [f for f in message["files"] if f.endswith(".mp4")]
    image_files = [f for f in message["files"] if re.search(r"\.(png|jpg|jpeg|gif|webp)$", f, re.IGNORECASE)]
    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")]

    # user 텍스트 먼저 추가
    content_list = [{"type": "text", "text": message["text"]}]

    # CSV
    for csv_path in csv_files:
        csv_analysis = analyze_csv_file(csv_path)
        # 분석 내용만 넣음 (파일 경로를 히스토리에 추가하지 않음)
        content_list.append({"type": "text", "text": csv_analysis})

    # TXT
    for txt_path in txt_files:
        txt_analysis = analyze_txt_file(txt_path)
        content_list.append({"type": "text", "text": txt_analysis})

    # PDF
    for pdf_path in pdf_files:
        pdf_markdown = pdf_to_markdown(pdf_path)
        content_list.append({"type": "text", "text": pdf_markdown})

    # 비디오
    if video_files:
        content_list += process_video(video_files[0])
        return content_list

    # 이미지
    if "<image>" in message["text"]:
        # interleaved
        return process_interleaved_images(message)
    else:
        # 여러 장 이미지
        for img_path in image_files:
            content_list.append({"type": "image", "url": img_path})

    return content_list


##################################################
# 6) history -> LLM 메시지 변환
##################################################
def process_history(history: list[dict]) -> list[dict]:
    messages = []
    current_user_content: list[dict] = []
    for item in history:
        if item["role"] == "assistant":
            if current_user_content:
                messages.append({"role": "user", "content": current_user_content})
                current_user_content = []
            messages.append({"role": "assistant", "content": [{"type": "text", "text": item["content"]}]})
        else:
            content = item["content"]
            if isinstance(content, str):
                current_user_content.append({"type": "text", "text": content})
            else:
                # 이미지 or 기타 파일 url
                current_user_content.append({"type": "image", "url": content[0]})
    return messages


##################################################
# 7) 메인 추론 함수
##################################################
@spaces.GPU(duration=120)
def run(message: dict, history: list[dict], system_prompt: str = "", max_new_tokens: int = 512) -> Iterator[str]:
    # a) 이미지/비디오 제한 검사
    if not validate_media_constraints(message, history):
        yield ""
        return

    # b) 대화 기록 + 이번 메시지
    messages = []
    if system_prompt:
        messages.append({"role": "system", "content": [{"type": "text", "text": system_prompt}]})
    messages.extend(process_history(history))
    messages.append({"role": "user", "content": process_new_user_message(message)})

    # c) 모델 추론
    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)

    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.generate, kwargs=gen_kwargs)
    t.start()

    output = ""
    for new_text in streamer:
        output += new_text
        yield output


##################################################
# 예시들 (한글화 버전)
##################################################
examples = [

    [
        {
            "text": "PDF 파일 내용을 요약, 분석하라.",
            "files": ["assets/additional-examples/pdf.pdf"],
        }
    ],
    [
        {
            "text": "CSV 파일 내용을 요약, 분석하라",
            "files": ["assets/additional-examples/sample-csv.csv"],
        }
    ],    
    [
        {
            "text": "동일한 막대 그래프를 그리는 matplotlib 코드를 작성해주세요.",
            "files": ["assets/additional-examples/barchart.png"],
        }
    ],
    [
        {
            "text": "이 영상에서 이상한 점이 무엇인가요?",
            "files": ["assets/additional-examples/tmp.mp4"],
        }
    ],
    [
        {
            "text": "이미 이 영양제를 <image> 가지고 있고, 이 제품 <image>을 새로 사려 합니다. 함께 섭취할 때 주의해야 할 점이 있을까요?",
            "files": ["assets/additional-examples/pill1.png", "assets/additional-examples/pill2.png"],
        }
    ],
    [
        {
            "text": "이미지의 시각적 요소에서 영감을 받아 시를 작성해주세요.",
            "files": ["assets/sample-images/06-1.png", "assets/sample-images/06-2.png"],
        }
    ],
    [
        {
            "text": "이미지의 시각적 요소를 토대로 짧은 악곡을 작곡해주세요.",
            "files": [
                "assets/sample-images/07-1.png",
                "assets/sample-images/07-2.png",
                "assets/sample-images/07-3.png",
                "assets/sample-images/07-4.png",
            ],
        }
    ],
    [
        {
            "text": "이 집에서 무슨 일이 있었을지 짧은 이야기를 지어보세요.",
            "files": ["assets/sample-images/08.png"],
        }
    ],
    [
        {
            "text": "이미지들의 순서를 바탕으로 짧은 이야기를 만들어 주세요.",
            "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": "이 세계에서 살고 있을 생물들을 상상해서 묘사해주세요.",
            "files": ["assets/sample-images/10.png"],
        }
    ],
    [
        {
            "text": "이미지에 적힌 텍스트를 읽어주세요.",
            "files": ["assets/additional-examples/1.png"],
        }
    ],
    [
        {
            "text": "이 티켓은 언제 발급된 것이고, 가격은 얼마인가요?",
            "files": ["assets/additional-examples/2.png"],
        }
    ],
    [
        {
            "text": "이미지에 있는 텍스트를 그대로 읽어서 마크다운 형태로 적어주세요.",
            "files": ["assets/additional-examples/3.png"],
        }
    ],
    [
        {
            "text": "이 적분을 풀어주세요.",
            "files": ["assets/additional-examples/4.png"],
        }
    ],
    [
        {
            "text": "이 이미지를 간단히 캡션으로 설명해주세요.",
            "files": ["assets/sample-images/01.png"],
        }
    ],
    [
        {
            "text": "이 표지판에는 무슨 문구가 적혀 있나요?",
            "files": ["assets/sample-images/02.png"],
        }
    ],
    [
        {
            "text": "두 이미지를 비교해서 공통점과 차이점을 말해주세요.",
            "files": ["assets/sample-images/03.png"],
        }
    ],
    [
        {
            "text": "이미지에 보이는 모든 사물과 그 색상을 나열해주세요.",
            "files": ["assets/sample-images/04.png"],
        }
    ],
    [
        {
            "text": "장면의 분위기를 묘사해주세요.",
            "files": ["assets/sample-images/05.png"],
        }
    ],
]




##################################################
# 9) Gradio ChatInterface
##################################################
demo = gr.ChatInterface(
    fn=run,
    type="messages",
    chatbot=gr.Chatbot(type="messages", scale=1, allow_tags=["image"]),
    # 이미지/동영상 + CSV/TXT/PDF 허용 (이미지: webp 포함)
    textbox=gr.MultimodalTextbox(
        file_types=[
            ".png", ".jpg", ".jpeg", ".gif", ".webp",
            ".mp4", ".csv", ".txt", ".pdf"
        ],
        file_count="multiple",
        autofocus=True
    ),
    multimodal=True,
    additional_inputs=[
        gr.Textbox(
            label="System Prompt",
            value="You are a deeply thoughtful AI. Consider problems thoroughly and derive correct solutions through systematic reasoning. Please answer in korean."
        ),
        gr.Slider(
            label="Max New Tokens",
            minimum=100,
            maximum=8000,
            step=50,
            value=2000
        ),
    ],
    stop_btn=False,
    title="Gemma 3 27B IT",
    examples=examples,
    run_examples_on_click=False,
    cache_examples=False,
    css_paths="style.css",
    delete_cache=(1800, 1800),
)

if __name__ == "__main__":
    demo.launch()