app.py

import os
import shutil
import time
from collections import Counter

import torch
import whisper
from pyannote.audio import Pipeline
from torch.serialization import add_safe_globals
from omegaconf import ListConfig
import nest_asyncio
import uvicorn
from fastapi import FastAPI, UploadFile, File
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from pyngrok import ngrok, conf
from pydub import AudioSegment, effects
import pandas as pd
from moviepy.editor import VideoFileClip
from together import Together
import asyncio
import logging

HF_CACHE_DIR = "/tmp/hf_cache"
WHISPER_CACHE_DIR = "/tmp/whisper_cache"

os.makedirs(HF_CACHE_DIR, exist_ok=True)
os.makedirs(WHISPER_CACHE_DIR, exist_ok=True)

os.environ["HUGGINGFACE_HUB_CACHE"] = HF_CACHE_DIR
os.environ["TORCH_HOME"] = WHISPER_CACHE_DIR

token = os.environ.get('HF_TOKEN')
together_api_key = os.environ.get('TOGETHER_API_KEY')
ngrok_auth_token = os.environ.get('NGROK_AUTH_TOKEN')

pipelines, models, others = [], [], []

def load_model_bundle():
    n = torch.cuda.device_count()
    logger.info(f"🖥️ Found {n} CUDA device(s)")
    
    if n == 0:
        device = "cpu"
        pipeline = Pipeline.from_pretrained(
            "pyannote/speaker-diarization-3.1",
            use_auth_token=token,
            cache_dir=HF_CACHE_DIR
        ).to(device)
        model = whisper.load_model("large", download_root=WHISPER_CACHE_DIR).to(device)
    elif n == 1:
        device = "cuda:0"
        pipeline = Pipeline.from_pretrained(
            "pyannote/speaker-diarization-3.1",
            use_auth_token=token,
            cache_dir=HF_CACHE_DIR
        ).to(device)
        model = whisper.load_model("large", download_root=WHISPER_CACHE_DIR).to(device)
    else:
        device_pyannote = torch.device("cuda:0")
        device_whisper = torch.device("cuda:1")
        pipeline = Pipeline.from_pretrained(
            "pyannote/speaker-diarization-3.1",
            use_auth_token=token,
            cache_dir=HF_CACHE_DIR
        ).to(device_pyannote)
        model = whisper.load_model("large", download_root=WHISPER_CACHE_DIR).to(device_whisper)
    
    pipelines.append(pipeline)
    models.append(model)


together = Together(api_key=together_api_key)
conf.get_default().auth_token = ngrok_auth_token

add_safe_globals({ListConfig})

UPLOAD_FOLDER = "/tmp/uploads"
os.makedirs(UPLOAD_FOLDER, exist_ok=True)

app = FastAPI()

origins = os.getenv("CORS_ORIGINS", "").split(",")
origins = [o.strip() for o in origins if o.strip()] or [
    "*",
]

app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


@app.get("/")
async def check_api():
    async with model_lock:
        if not pipelines or not models:
            logger.info("🔁 Lazy loading models now...")
            await load_model_bundle()

    return {
        "status": "running",
        "models_loaded": {
            "pipelines": len(pipelines),
            "whisper_models": len(models)
        },
        "cuda_available": torch.cuda.is_available(),
        "cuda_devices": torch.cuda.device_count() if torch.cuda.is_available() else 0
    }

@app.get("/key")
async def check_env():
    return {
        "env": os.environ.get("ENV", "dev"),
        "openai_key_exists": bool(os.environ.get("OPENAI_API_KEY")),
    }

def save_uploaded_file(file: UploadFile) -> str:
    os.makedirs(UPLOAD_FOLDER, exist_ok=True)
    filepath = os.path.join(UPLOAD_FOLDER, file.filename)
    with open(filepath, "wb") as f:
        shutil.copyfileobj(file.file, f)
    return filepath

def extract_and_normalize_audio(video_path: str) -> str:
    clip = VideoFileClip(video_path)
    audio_path = os.path.join(UPLOAD_FOLDER, "extracted_audio.wav")
    clip.audio.write_audiofile(audio_path)

    audio = AudioSegment.from_wav(audio_path)
    normalized_audio = effects.normalize(audio)
    cleaned_path = os.path.join(UPLOAD_FOLDER, "cleaned.wav")
    normalized_audio.export(cleaned_path, format="wav")
    return cleaned_path

def diarize_audio(audio_path: str) -> pd.DataFrame:
    diarization = pipeline(audio_path)
    return pd.DataFrame([
        {"start": round(turn.start, 3), "end": round(turn.end, 3), "speaker": speaker}
        for turn, _, speaker in diarization.itertracks(yield_label=True)
    ])

def split_segments(audio_path: str, df: pd.DataFrame) -> str:
    segment_folder = os.path.join(UPLOAD_FOLDER, "segments")
    if os.path.exists(segment_folder):
        shutil.rmtree(segment_folder)
    os.makedirs(segment_folder, exist_ok=True)

    audio = AudioSegment.from_file(audio_path)
    for i, row in df.iterrows():
        start_ms = int(row['start'] * 1000)
        end_ms = int(row['end'] * 1000)
        segment = audio[start_ms:end_ms]
        filename = f"segment_{i:03d}_{row['speaker']}.wav"
        segment.export(os.path.join(segment_folder, filename), format="wav")

    return segment_folder

def transcribe_segments(segment_folder: str) -> pd.DataFrame:
    files = sorted(os.listdir(segment_folder))
    results = []
    for filename in files:
        segment_path = os.path.join(segment_folder, filename)
        res = model.transcribe(segment_path, language="th")
        results.append({
            "filename": filename,
            "text": res["text"].strip()
        })
    return pd.DataFrame(results)

def clean_summary(text):
    import re

    if not text or len(str(text).strip()) == 0:
        return "ไม่มีข้อมูลสำคัญที่จะสรุป"

    text = str(text)

    # Patterns to remove (more comprehensive)
    patterns_to_remove = [
        # Headers and labels
        r'สรุป:\s*',
        r'สรุปการประชุม:\s*',
        r'บทสรุป:\s*',
        r'ข้อสรุป:\s*',
        r'\*\*Key Messages:\*\*|\*\*หัวข้อหลัก:\*\*',
        r'\*\*Action Items:\*\*|\*\*ประเด็นสำคัญ:\*\*',
        r'\*\*Summary:\*\*|\*\*สรุป:\*\*',

        # Bullet points and markers
        r'^[-•]\s*Key Messages?:?\s*',
        r'^[-•]\s*Action Items?:?\s*',
        r'^[-•]\s*หัวข้อหลัก:?\s*',
        r'^[-•]\s*ประเด็นสำคัญ:?\s*',
        r'^[-•]\s*ข้อมูลน่าสนใจ:?\s*',
        r'^[-•]\s*บทสรุป:?\s*',

        # Line breaks and formatting
        r'\r\n|\r|\n',
        r'\t+',

        # Disclaimers and notes
        r'หมายเหตุ:.*?(?=\n|\r|$)',
        r'เนื่องจาก.*?(?=\n|\r|$)',
        r'ไม่มีข้อความ.*?(?=\n|\r|$)',
        r'ไม่มีประเด็น.*?(?=\n|\r|$)',
        r'ไม่มี Action Items.*?(?=\n|\r|$)',
        r'ไม่มีรายการ.*?(?=\n|\r|$)',
        r'ต้องการข้อมูลเพิ่มเติม.*?(?=\n|\r|$)',
        r'ต้องขอความชัดเจนเพิ่มเติม.*?(?=\n|\r|$)',

        # Meta comments
        r'\(ตัดประโยคที่ไม่เกี่ยวข้องหรือซ้ำซ้อนออก.*?\)',
        r'\(.*?เพื่อเน้นความชัดเจน.*?\)',

        # AI-generated phrases
        r'ตามที่ได้กล่าวไว้.*?(?=\n|\r|$)',
        r'จากข้อความที่ให้มา.*?(?=\n|\r|$)',
        r'Based on the provided text.*?(?=\n|\r|$)',
        r'According to the text.*?(?=\n|\r|$)',

        # Multiple spaces (keep at end)
        r'\s+'
    ]

    cleaned_text = text

    # Apply cleaning patterns
    for pattern in patterns_to_remove:
        if pattern == r'\s+':
            # Replace multiple spaces with single space
            cleaned_text = re.sub(pattern, ' ', cleaned_text)
        else:
            cleaned_text = re.sub(pattern, '', cleaned_text, flags=re.IGNORECASE | re.MULTILINE | re.DOTALL)

    # Remove markdown formatting but keep content
    cleaned_text = re.sub(r'\*\*(.*?)\*\*', r'\1', cleaned_text)  # Bold
    cleaned_text = re.sub(r'\*(.*?)\*', r'\1', cleaned_text)      # Italic
    cleaned_text = re.sub(r'_{2,}(.*?)_{2,}', r'\1', cleaned_text) # Underline

    # Remove excessive punctuation
    cleaned_text = re.sub(r'[.]{3,}', '...', cleaned_text)
    cleaned_text = re.sub(r'[!]{2,}', '!', cleaned_text)
    cleaned_text = re.sub(r'[?]{2,}', '?', cleaned_text)

    # Clean up bullet points and numbering
    cleaned_text = re.sub(r'^[-•*]\s*', '', cleaned_text, flags=re.MULTILINE)
    cleaned_text = re.sub(r'^\d+\.\s*', '', cleaned_text, flags=re.MULTILINE)

    # Useless phrases (more comprehensive)
    useless_phrases = [
        'ไม่มี',
        'ไม่สามารถสรุปได้',
        'ข้อความต้นฉบับไม่มีความหมาย',
        'ไม่มีข้อมูลเพียงพอ',
        'ไม่มีประเด็นสำคัญ',
        'ไม่มี Action Items',
        'ต้องขอความชัดเจนเพิ่มเติม',
        'ไม่มีข้อมูลที่สำคัญ',
        'ไม่สามารถระบุได้',
        'ข้อมูลไม่ชัดเจน',
        'ไม่มีเนื้อหาที่เกี่ยวข้อง',
        'N/A',
        'n/a',
        'Not applicable',
        'No content',
        'No summary available'
    ]

    cleaned_text = cleaned_text.strip()

    if (len(cleaned_text) < 15 or
        any(phrase.lower() in cleaned_text.lower() for phrase in useless_phrases) or
        cleaned_text.lower() in [phrase.lower() for phrase in useless_phrases]):
        return "ไม่มีข้อมูลสำคัญที่จะสรุปมากพอ"

    cleaned_text = re.sub(r'\s+([.!?])', r'\1', cleaned_text)
    cleaned_text = re.sub(r'([.!?])\s*([A-Za-zก-๙])', r'\1 \2', cleaned_text)

    return cleaned_text

from together import Together
import time

def summarize_texts(texts, api_key, model="deepseek-ai/DeepSeek-V3", delay=1):
    client = Together(api_key=api_key)
    summaries = []

    for idx, text in enumerate(texts):
        prompt = f"""
สรุปข้อความประชุมนี้เป็นภาษาไทยสั้น ๆ เน้นประเด็นสำคัญ (key messages) และ Action Items โดยตัดรายละเอียดที่ไม่สำคัญออก:

ข้อความ:
{text}

สรุป:
- Key Messages:
- Action Items:
"""
        try:
            response = client.chat.completions.create(
                model=model,
                messages=[
                    {"role": "system", "content": "คุณเป็นผู้เชี่ยวชาญในการสรุปเนื้อหา ตอบเป็นภาษาไทยเสมอ เน้นหัวข้อหลักและข้อมูลสำคัญ"},
                    {"role": "user", "content": prompt}
                ],
                max_tokens=1024,
                temperature=0.7,
            )

            summary = response.choices[0].message.content.strip()
            summary = clean_summary(summary)
            summaries.append(summary)

        except Exception as e:
            print(f"Error at index {idx}: {e}")
            summaries.append("ไม่สามารถสรุปได้")

        if idx < len(texts) - 1:
            time.sleep(delay)

    return summaries

@app.post('/video')
async def upload_video(file: UploadFile = File(...)):
    video_path = save_uploaded_file(file)
    return video_path

@app.post("/upload_video/")
async def upload_video(file: UploadFile = File(...)):

    async with model_lock:
        if not pipelines or not models:
            logger.info("🔁 Lazy loading models now...")
            load_model_bundle()

    video_path = save_uploaded_file(file)
    audio_path = extract_and_normalize_audio(video_path)
    df_diarization = diarize_audio(audio_path)
    segment_folder = split_segments(audio_path, df_diarization)
    df_transcriptions = transcribe_segments(segment_folder)

    min_len = min(len(df_diarization), len(df_transcriptions))
    df_merged = pd.concat([
        df_diarization.iloc[:min_len].reset_index(drop=True),
        df_transcriptions.iloc[:min_len].reset_index(drop=True)
    ], axis=1)

    result = df_merged.to_dict(orient="records")
    speaker_array = df_diarization["speaker"].unique().tolist()
    counter = Counter(df_diarization["speaker"])
    result_array = [{"speaker": spk, "count": cnt} for spk, cnt in counter.most_common()]
    api_key = together
    summaries = summarize_texts(df_merged["text"].tolist(), api_key, delay=2)
    duration_minutes = len(AudioSegment.from_wav(audio_path)) / 1000 / 60

    return JSONResponse(content={
        "video_path": video_path,
        "audio_path": audio_path,
        "audio_length": duration_minutes,
        "data": result,
        "speaker_array": speaker_array,
        "count_speaker": result_array,
        "num_speakers": len(speaker_array),
        "summaries": summaries,
        "total_sentence": len(df_merged['text']),
    })


if __name__ == "__main__":
    uvicorn.run(
        app, 
        host="0.0.0.0", 
        port=7860,
        reload=False
    )