Update app.py

app.py

@@ -7,31 +7,69 @@ import time
 import json
 import queue
 import subprocess
+import multiprocessing
 from flask import Flask, request, jsonify, render_template_string, send_file
 from faster_whisper import WhisperModel
 
-# --- CONFIG ---
+# --- CONFIG SERVER & ANTRIAN ---
 AI_API_URL = "https://puruboy-api.vercel.app/api/ai/notegpt"
 app = Flask(__name__)
 app.config['UPLOAD_FOLDER'] = 'downloads'
 app.config['MAX_CONTENT_LENGTH'] = 200 * 1024 * 1024  # Max 200 MB
 os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
 
-# --- ANTRIAN & AUTO DELETE CONFIG ---
-MAX_QUEUE_SIZE = 5         # Maksimal video dalam antrian
-AUTO_DELETE_MINUTES = 5    # Waktu sebelum file dihapus otomatis
+MAX_QUEUE_SIZE = 20           # Maksimal antrian
+MAX_WORKERS = 5               # Maksimal proses berjalan bersamaan
+AUTO_DELETE_MINUTES = 1440    # Waktu file dihapus (1440 menit = 1 hari)
 
 JOBS = {}
 job_queue = queue.Queue(maxsize=MAX_QUEUE_SIZE)
 
-# Load Model Whisper
-print("Loading Whisper Model...")
-# Menggunakan VAD untuk mengabaikan musik/noise dan hanya fokus ke suara manusia
-whisper_model = WhisperModel("base", device="cpu", compute_type="int8")
+# --- GLOBAL RATE LIMIT CONFIG ---
+request_timestamps = []
+global_lockout_until = 0
+
+# --- OPTIMASI CPU WHISPER ---
+# Mengatur thread CPU per model agar 5 worker tidak membuat CPU bertabrakan (bottleneck)
+total_cores = multiprocessing.cpu_count()
+threads_per_worker = max(1, total_cores // MAX_WORKERS)
+
+print(f"Loading Whisper Model (CPU Cores/Worker: {threads_per_worker})...")
+whisper_model = WhisperModel(
+    "base", 
+    device="cpu", 
+    compute_type="int8", 
+    cpu_threads=threads_per_worker
+)
 print("Model Loaded!")
 
 # ==========================================
-# FUNGSI PARSING AI SSE (Server-Sent Events)
+# GLOBAL RATE LIMITER (Mencegah Spam)
+# ==========================================
+@app.before_request
+def rate_limiter():
+    global request_timestamps, global_lockout_until
+    
+    # Hanya batasi endpoint pembuatan video
+    if request.endpoint == 'generate' and request.method == 'POST':
+        current_time = time.time()
+        
+        # Cek apakah sedang dalam masa hukuman 2 menit
+        if current_time < global_lockout_until:
+            return jsonify({"error": "demi keamanan kamu sengaja mematikan api ini selama 2 menit karena ada yang spam"}), 429
+
+        # Bersihkan timestamp yang sudah lebih dari 60 detik (1 menit)
+        request_timestamps = [t for t in request_timestamps if current_time - t < 60]
+        
+        # Jika request lebih dari 30 dalam 1 menit
+        if len(request_timestamps) >= 30:
+            global_lockout_until = current_time + 120  # Kunci selama 120 detik (2 menit)
+            return jsonify({"error": "demi keamanan kamu sengaja mematikan api ini selama 2 menit karena ada yang spam"}), 429
+            
+        request_timestamps.append(current_time)
+
+# ==========================================
+# FUNGSI PARSING AI (Server-Sent Events)
 # ==========================================
 def get_ai_viral_clip(transcript_str):
     payload = {
@@ -52,7 +90,6 @@ TRANSKRIP:
     }
 
     headers = {"Content-Type": "application/json"}
-    
     response = requests.post(AI_API_URL, json=payload, headers=headers, stream=True)
     
     full_text = ""
@@ -70,7 +107,6 @@ TRANSKRIP:
                 except json.JSONDecodeError:
                     continue
 
-    # Ekstrak JSON menggunakan regex (mengatasi jika AI masih memberikan teks basa-basi)
     json_match = re.search(r'\{.*\}', full_text, re.DOTALL)
     if not json_match:
         raise Exception("Gagal mengekstrak format JSON dari AI.")
@@ -81,7 +117,6 @@ TRANSKRIP:
 # FUNGSI HELPER VIDEO & SUBTITLE
 # ==========================================
 def format_time_srt(seconds):
-    """Konversi detik (float) ke format jam:menit:detik,milis (untuk SRT)"""
     hrs = int(seconds // 3600)
     mins = int((seconds % 3600) // 60)
     secs = int(seconds % 60)
@@ -89,28 +124,23 @@ def format_time_srt(seconds):
     return f"{hrs:02d}:{mins:02d}:{secs:02d},{msec:03d}"
 
 def generate_srt(words, start_offset, end_offset, srt_path):
-    """Membuat file SRT hanya untuk bagian video yang dipotong"""
     with open(srt_path, 'w', encoding='utf-8') as f:
         idx = 1
         for w in words:
-            # Hanya ambil kata dalam rentang waktu yang dipotong
             if w.start >= start_offset and w.end <= end_offset:
-                # Sesuaikan waktu agar dimulai dari 00:00:00 untuk video klip baru
                 s_time = format_time_srt(w.start - start_offset)
                 e_time = format_time_srt(w.end - start_offset)
                 text = w.word.strip().upper()
-                
                 f.write(f"{idx}\n{s_time} --> {e_time}\n{text}\n\n")
                 idx += 1
 
 def cleanup_files(*file_paths, job_id=None):
-    """Menghapus semua file sementara dan membersihkan antrian RAM"""
     for path in file_paths:
         if path and os.path.exists(path):
             try: os.remove(path)
             except: pass
     if job_id and job_id in JOBS:
-        del JOBS[job_id]
+        del JOBS[job_id] # Hapus dari memory agar RAM tidak bengkak
     print(f"[{job_id}] Auto-cleanup selesai.")
 
 # ==========================================
@@ -124,14 +154,23 @@ def process_video(job_id):
     out_path = os.path.join(app.config['UPLOAD_FOLDER'], out_filename)
     
     try:
-        # 1. EKSTRAK AUDIO CEPAT (FFmpeg)
+        # 1. EKSTRAK AUDIO CEPAT
         JOBS[job_id].update({"message": "Mengekstrak audio video...", "progress": 10})
         subprocess.run(['ffmpeg', '-y', '-i', path_in, '-vn', '-acodec', 'pcm_s16le', '-ar', '16000', '-ac', '1', audio_path], 
                        stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, check=True)
 
-        # 2. TRANSCRIBE DENGAN VAD (Mengabaikan musik)
+        # 2. TRANSCRIBE DENGAN OPTIMASI SUPER CEPAT (Untuk Video >20 Menit)
         JOBS[job_id].update({"message": "Menganalisa suara manusia (AI Whisper)...", "progress": 25})
-        segments, _ = whisper_model.transcribe(audio_path, word_timestamps=True, vad_filter=True, vad_parameters=dict(min_silence_duration_ms=500))
+        
+        # beam_size=1 dan condition_on_previous_text=False membuat proses 3x - 5x lebih cepat
+        segments, _ = whisper_model.transcribe(
+            audio_path, 
+            word_timestamps=True, 
+            vad_filter=True, 
+            vad_parameters=dict(min_silence_duration_ms=500),
+            beam_size=1, 
+            condition_on_previous_text=False
+        )
         
         transcript_for_ai = []
         all_words = []
@@ -145,11 +184,10 @@ def process_video(job_id):
         if not transcript_str.strip():
             raise Exception("Tidak ada suara manusia yang terdeteksi.")
 
-        # 3. AI MENCARI BAGIAN VIRAL (Memanggil fungsi SSE)
+        # 3. AI MENCARI BAGIAN VIRAL
         JOBS[job_id].update({"message": "AI sedang meracik bagian viral...", "progress": 50})
         clip_data = get_ai_viral_clip(transcript_str)
         
-        # Buffer waktu (agar pemotongan tidak terlalu kaku)
         s_time = max(0, float(clip_data['start_s']) - 0.2)
         e_time = float(clip_data['end_s']) + 0.3
         
@@ -157,11 +195,9 @@ def process_video(job_id):
         JOBS[job_id].update({"message": "Membuat efek subtitle viral...", "progress": 70})
         generate_srt(all_words, s_time, e_time, srt_path)
 
-        # 5. POTONG & BURN SUBTITLE SANGAT CEPAT (FFmpeg murni)
+        # 5. POTONG & BURN SUBTITLE
         JOBS[job_id].update({"message": "Rendering Video Final (Super Cepat)...", "progress": 85})
         
-        # Style Subtitle: Font Arial/Tebal, Warna Kuning, Outline Hitam Tebal, Tengah Bawah
-        # Note: Penggunaan format path pada Windows untuk FFmpeg filter kadang tricky, ini dibersihkan
         safe_srt_path = srt_path.replace('\\', '/')
         style = "Fontname=Arial,Fontsize=24,PrimaryColour=&H0000FFFF,OutlineColour=&H00000000,BorderStyle=1,Outline=2,Shadow=0,Alignment=2,MarginV=25,Bold=1"
         
@@ -189,14 +225,14 @@ def process_video(job_id):
             JOBS[job_id].update({"status": "error", "message": str(e)})
 
     finally:
-        # Hapus file sementara langsung setelah selesai (hemat disk)
+        # File sementara dihapus langsung
         cleanup_files(audio_path, srt_path)
         
-        # Hapus video input dan output setelah 5 menit
+        # File final dihapus setelah 1 Hari (1440 menit)
         timer = threading.Timer(AUTO_DELETE_MINUTES * 60, cleanup_files, args=(path_in, out_path), kwargs={'job_id': job_id})
         timer.start()
 
-# --- WORKER LOOP ANTRIAN ---
+# --- MULTI-WORKER SYSTEM (Menjalankan 5 Proses Sekaligus) ---
 def queue_worker():
     while True:
         job_id = job_queue.get()
@@ -205,10 +241,12 @@ def queue_worker():
             process_video(job_id)
         job_queue.task_done()
 
-threading.Thread(target=queue_worker, daemon=True).start()
+# Menjalankan 5 Thread Workers (Sehingga bisa render 5 video serentak)
+for _ in range(MAX_WORKERS):
+    threading.Thread(target=queue_worker, daemon=True).start()
 
 # ==========================================
-# UI HTML (TETAP SAMA SEPERTI MILIK ANDA)
+# UI HTML
 # ==========================================
 HTML_TEMPLATE = """
 <!DOCTYPE html>
@@ -216,7 +254,7 @@ HTML_TEMPLATE = """
 <head>
     <meta charset="UTF-8">
     <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>AI Viral Clipper Ultra</title>
+    <title>AI Viral Clipper Ultra (Pro)</title>
     <script src="https://cdn.tailwindcss.com"></script>
 </head>
 <body class="bg-black text-slate-200 min-h-screen flex flex-col items-center p-6">
@@ -246,7 +284,7 @@ HTML_TEMPLATE = """
         <div id="resultArea" class="mt-8 hidden text-center">
             <video id="player" controls class="w-full rounded-xl border border-slate-700 mb-4"></video>
             <a id="downloadBtn" href="#" class="block w-full text-center bg-white text-black font-bold py-3 rounded-xl hover:bg-gray-200 transition">DOWNLOAD CLIP</a>
-            <p class="text-xs text-red-400 mt-3 font-semibold">⚠️ Video akan dihapus otomatis dari server dalam 5 menit.</p>
+            <p class="text-xs text-red-400 mt-3 font-semibold">⚠️ Video akan dihapus otomatis dari server dalam 24 Jam.</p>
         </div>
     </div>
 
@@ -325,7 +363,7 @@ def index():
 @app.route('/generate', methods=['POST'])
 def generate():
     if job_queue.full():
-        return jsonify({"error": "Antrian penuh. Coba beberapa saat lagi."}), 429
+        return jsonify({"error": "Antrian sedang penuh (Maksimal 20). Coba beberapa saat lagi."}), 429
 
     file = request.files.get('video_file')
     if not file: return jsonify({"error": "File kosong"}), 400
@@ -337,7 +375,7 @@ def generate():
     JOBS[job_id] = {
         "status": "queued", 
         "progress": 5, 
-        "message": "Menunggu giliran...", 
+        "message": "Menunggu giliran dalam antrian...", 
         "input_path": save_path
     }
     
@@ -365,4 +403,8 @@ def download(filename):
     return send_file(file_path, as_attachment=True)
 
 if __name__ == '__main__':
+    # Untuk level produksi sebenarnya, lebih baik disajikan via Waitress atau Gunicorn
+    # pip install waitress
+    # from waitress import serve
+    # serve(app, host="0.0.0.0", port=7860, threads=10)
     app.run(host='0.0.0.0', port=7860, debug=False)