server.py

ENDOFFILE'
#!/usr/bin/env python3
import os
import json
import re
import asyncio
import tempfile
import subprocess
from pathlib import Path
from datetime import datetime
from dotenv import load_dotenv
from typing import List, Dict, Optional, Tuple

from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse
import uvicorn

try:
    from huggingface_hub import list_repo_files, hf_hub_download, upload_file
    import cv2
    import numpy as np
    from PIL import Image, ImageDraw, ImageFont
    from faster_whisper import WhisperModel
except ImportError as e:
    print(f"Missing dependency: {e}")
    print("Install with: pip install faster-whisper")
    exit(1)

# Load environment variables
load_dotenv()
HF_TOKEN = os.getenv("HF_TOKEN")
if not HF_TOKEN:
    print("Error: Missing HF_TOKEN in .env")
    exit(1)

app = FastAPI(title="Video Processing Service")

# Global state
processing_state = {
    "is_running": False,
    "total_processed": 0,
    "current_file": None,
    "error_count": 0,
    "last_error": None,
    "processed_files": [],
    "whisper_ready": False
}

# Whisper model — loaded async at startup, not at import time
whisper_model = None

HF_DATASET_REPO = "factorstudios/movs"
HOOKS_FOLDER = "hooks"
READY_VIDEOS_FOLDER = "ready_videos"
TRANSCRIPTION_FOLDER = "transcriptions"


def _load_whisper_model():
    """Blocking model load — runs in thread executor."""
    global whisper_model
    print("Loading Whisper small model...")
    whisper_model = WhisperModel("small", device="auto", compute_type="int8")
    processing_state["whisper_ready"] = True
    print("✓ Whisper model loaded")


def timestamp_to_seconds(timestamp: str) -> float:
    """Convert HH:MM:SS to seconds."""
    try:
        parts = timestamp.split(":")
        return int(parts[0]) * 3600 + int(parts[1]) * 60 + int(parts[2])
    except Exception as e:
        print(f"Error converting timestamp {timestamp}: {e}")
        return 0.0


def extract_audio_segment(video_path: str, start_seconds: float, end_seconds: float, output_wav: str) -> bool:
    """Extract audio segment from video as WAV for Whisper."""
    cmd = [
        "ffmpeg", "-y",
        "-ss", str(start_seconds),
        "-to", str(end_seconds),
        "-i", video_path,
        "-vn",
        "-acodec", "pcm_s16le",
        "-ar", "16000",
        "-ac", "1",
        output_wav
    ]
    result = subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
    return result.returncode == 0


def transcribe_segment(audio_path: str) -> List[Tuple[float, float, str]]:
    """
    Transcribe audio with Whisper small.
    Returns list of (start_sec, end_sec, text) relative to segment start.
    """
    print("  Transcribing audio with Whisper small...")
    segments, info = whisper_model.transcribe(
        audio_path,
        beam_size=5,
        language=None,
        vad_filter=True,
        vad_parameters=dict(min_silence_duration_ms=500)
    )

    captions = []
    for seg in segments:
        text = seg.text.strip()
        if text:
            captions.append((seg.start, seg.end, text))
            print(f"  [{seg.start:.1f}s → {seg.end:.1f}s] {text}")

    print(f"  ✓ Transcribed {len(captions)} caption segments")
    return captions


def apply_color_grading_wedding_retro(frame: np.ndarray) -> np.ndarray:
    """Apply cinematic wedding LUT + retro style with high sharpening."""
    lab = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB)
    l_channel, a_channel, b_channel = cv2.split(lab)

    a_channel = cv2.add(a_channel, 5)
    b_channel = cv2.add(b_channel, 8)

    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
    l_channel = clahe.apply(l_channel)

    lab_enhanced = cv2.merge([l_channel, a_channel, b_channel])
    frame = cv2.cvtColor(lab_enhanced, cv2.COLOR_LAB2BGR)

    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV).astype(np.float32)
    hsv[:, :, 1] = np.clip(hsv[:, :, 1] * 1.3, 0, 255)
    frame = cv2.cvtColor(hsv.astype(np.uint8), cv2.COLOR_HSV2BGR)

    frame = cv2.convertScaleAbs(frame, alpha=1.15, beta=10)

    kernel = np.array([[-1, -1, -1],
                       [-1,  9, -1],
                       [-1, -1, -1]]) / 1.2
    sharpened = cv2.filter2D(frame, -1, kernel)
    frame = cv2.addWeighted(frame, 0.4, sharpened, 0.6, 0)

    rows, cols = frame.shape[:2]
    X_kernel = cv2.getGaussianKernel(cols, cols / 2)
    Y_kernel = cv2.getGaussianKernel(rows, rows / 2)
    mask = (Y_kernel * X_kernel.T)
    mask = (mask / mask.max()) ** 0.4

    for i in range(3):
        frame[:, :, i] = frame[:, :, i] * mask

    return np.clip(frame, 0, 255).astype(np.uint8)


def burn_captions_to_frame(frame: np.ndarray, text: str, font_size: int = 36) -> np.ndarray:
    """Burn caption text onto frame — shadow only, no background, positioned near bottom."""
    height, width = frame.shape[:2]

    frame_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)).convert('RGBA')
    overlay = Image.new('RGBA', frame_pil.size, (0, 0, 0, 0))
    draw = ImageDraw.Draw(overlay)

    try:
        font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", font_size)
    except Exception:
        font = ImageFont.load_default()

    max_width = width - 80
    wrapped_lines = []
    words = text.split()
    current_line = []

    for word in words:
        test_line = ' '.join(current_line + [word])
        bbox = draw.textbbox((0, 0), test_line, font=font)
        if bbox[2] - bbox[0] > max_width:
            if current_line:
                wrapped_lines.append(' '.join(current_line))
            current_line = [word]
        else:
            current_line.append(word)
    if current_line:
        wrapped_lines.append(' '.join(current_line))

    line_height = font_size + 12
    total_text_height = len(wrapped_lines) * line_height
    y_start = int(height * 0.80) - total_text_height // 2
    shadow_offset = 3

    for i, line in enumerate(wrapped_lines):
        bbox = draw.textbbox((0, 0), line, font=font)
        line_width = bbox[2] - bbox[0]
        x = (width - line_width) // 2
        y = y_start + i * line_height

        draw.text((x + shadow_offset, y + shadow_offset), line, font=font, fill=(0, 0, 0, 200))
        draw.text((x, y), line, font=font, fill=(255, 255, 255, 255))

    frame_pil = Image.alpha_composite(frame_pil, overlay).convert('RGB')
    return cv2.cvtColor(np.array(frame_pil), cv2.COLOR_RGB2BGR)


def build_frame_caption_map(captions: List[Tuple[float, float, str]], fps: float) -> Dict[int, str]:
    """Convert Whisper segments into a per-frame caption lookup."""
    frame_map = {}
    for start_sec, end_sec, text in captions:
        start_frame = int(start_sec * fps)
        end_frame = int(end_sec * fps)
        for f in range(start_frame, end_frame + 1):
            frame_map[f] = text
    return frame_map


def process_video_segment(
    video_path: str,
    output_path: str,
    start_time: str,
    end_time: str,
    target_width: int = 1080,
    target_height: int = 1350
) -> bool:
    """
    Full pipeline:
    1. Extract audio segment → WAV
    2. Transcribe with Whisper small
    3. Process frames with color grading + caption burn-in
    4. Mux processed video with original audio
    """
    ffmpeg_video_proc = None
    temp_wav = output_path.replace(".mp4", "_audio.wav")
    temp_video_path = output_path.replace(".mp4", "_noaudio.mp4")

    try:
        print(f"Opening video: {video_path}")
        cap = cv2.VideoCapture(video_path)

        if not cap.isOpened():
            print(f"Error: Could not open video {video_path}")
            return False

        fps = cap.get(cv2.CAP_PROP_FPS)
        original_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        original_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

        start_seconds = timestamp_to_seconds(start_time)
        end_seconds = timestamp_to_seconds(end_time)
        duration = end_seconds - start_seconds

        print(f"Video info: {fps} fps, {original_width}x{original_height}")
        print(f"Extracting segment: {start_time} to {end_time} ({duration:.1f}s)")

        # ── Step 1: Extract audio → WAV ───────────────────────────────────────
        print("  Extracting audio segment...")
        audio_ok = extract_audio_segment(video_path, start_seconds, end_seconds, temp_wav)

        # ── Step 2: Transcribe with Whisper ───────────────────────────────────
        if audio_ok and whisper_model is not None:
            captions = transcribe_segment(temp_wav)
        else:
            print("  Warning: Skipping transcription (audio failed or model not ready)")
            captions = []

        frame_caption_map = build_frame_caption_map(captions, fps)

        # ── Step 3: Process frames → pipe to FFmpeg ───────────────────────────
        ffmpeg_video_cmd = [
            "ffmpeg", "-y",
            "-f", "rawvideo",
            "-vcodec", "rawvideo",
            "-s", f"{target_width}x{target_height}",
            "-pix_fmt", "bgr24",
            "-r", str(fps),
            "-i", "pipe:0",
            "-vcodec", "libx264",
            "-preset", "fast",
            "-crf", "23",
            "-pix_fmt", "yuv420p",
            temp_video_path
        ]

        ffmpeg_video_proc = subprocess.Popen(
            ffmpeg_video_cmd,
            stdin=subprocess.PIPE,
            stdout=subprocess.DEVNULL,
            stderr=subprocess.DEVNULL
        )

        start_frame = int(start_seconds * fps)
        cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)

        current_caption = ""
        processed_frames = 0
        target_frames = int(duration * fps)

        print(f"Processing {target_frames} frames...")

        while processed_frames < target_frames:
            ret, frame = cap.read()
            if not ret:
                print(f"Warning: Could not read frame at position {processed_frames}")
                break

            aspect_ratio = target_width / target_height
            if original_width / original_height > aspect_ratio:
                new_width = int(original_height * aspect_ratio)
                x_offset = (original_width - new_width) // 2
                frame = frame[:, x_offset:x_offset + new_width]
            else:
                new_height = int(original_width / aspect_ratio)
                y_offset = (original_height - new_height) // 2
                frame = frame[y_offset:y_offset + new_height, :]

            frame = cv2.resize(frame, (target_width, target_height), interpolation=cv2.INTER_LANCZOS4)
            frame = apply_color_grading_wedding_retro(frame)

            current_caption = frame_caption_map.get(processed_frames, current_caption)
            # Clear caption if this frame isn't in the map and the previous caption has ended
            if processed_frames not in frame_caption_map:
                current_caption = ""

            if current_caption:
                frame = burn_captions_to_frame(frame, current_caption)

            ffmpeg_video_proc.stdin.write(frame.tobytes())
            processed_frames += 1

            if processed_frames % max(1, target_frames // 10) == 0:
                progress = (processed_frames / target_frames) * 100
                print(f"Progress: {progress:.1f}%")

        ffmpeg_video_proc.stdin.close()
        ffmpeg_video_proc.wait()
        cap.release()

        if ffmpeg_video_proc.returncode != 0:
            print(f"✗ FFmpeg video encoding failed (code {ffmpeg_video_proc.returncode})")
            return False

        print("✓ Frames encoded, muxing audio...")

        # ── Step 4: Mux processed video + original audio ──────────────────────
        ffmpeg_mux_cmd = [
            "ffmpeg", "-y",
            "-i", temp_video_path,
            "-ss", str(start_seconds),
            "-to", str(end_seconds),
            "-i", video_path,
            "-map", "0:v:0",
            "-map", "1:a:0",
            "-c:v", "copy",
            "-c:a", "aac",
            "-b:a", "192k",
            "-shortest",
            "-movflags", "+faststart",
            output_path
        ]

        mux_result = subprocess.run(
            ffmpeg_mux_cmd,
            stdout=subprocess.DEVNULL,
            stderr=subprocess.DEVNULL
        )

        if mux_result.returncode != 0:
            print(f"✗ FFmpeg audio mux failed (code {mux_result.returncode})")
            return False

        print(f"✓ Segment complete: {output_path}")
        return True

    except Exception as e:
        print(f"✗ Error processing video segment: {e}")
        if ffmpeg_video_proc is not None:
            try:
                ffmpeg_video_proc.stdin.close()
            except Exception:
                pass
            ffmpeg_video_proc.wait()
        return False

    finally:
        for tmp in [temp_video_path, temp_wav]:
            if tmp and os.path.exists(tmp):
                try:
                    os.remove(tmp)
                except Exception:
                    pass


async def process_movie_segments(movie_name: str) -> bool:
    """Process all segments for a movie."""
    try:
        processing_state["current_file"] = movie_name
        print(f"\n{'='*80}")
        print(f"Processing movie: {movie_name}")
        print(f"{'='*80}")

        video_file = f"{movie_name}.mkv"
        print(f"Downloading video: {video_file}")

        try:
            video_path = hf_hub_download(
                repo_id=HF_DATASET_REPO,
                filename=video_file,
                repo_type="dataset",
                token=HF_TOKEN,
                cache_dir="/tmp/video_processor_cache"
            )
            if os.path.islink(video_path):
                video_path = os.path.realpath(video_path)
        except Exception as e:
            print(f"Error: Could not download video: {e}")
            return False

        hooks_folder = f"{HOOKS_FOLDER}/{movie_name}"
        print(f"Listing segments from: {hooks_folder}")

        files = list_repo_files(
            repo_id=HF_DATASET_REPO,
            repo_type="dataset",
            token=HF_TOKEN
        )

        segment_files = sorted([
            f for f in files
            if f.startswith(f"{hooks_folder}/") and f.endswith(".json")
        ])

        if not segment_files:
            print(f"No segment JSON files found for {movie_name}")
            return False

        print(f"Found {len(segment_files)} segments")
        temp_dir = tempfile.mkdtemp()

        try:
            for segment_file in segment_files:
                try:
                    segment_path = hf_hub_download(
                        repo_id=HF_DATASET_REPO,
                        filename=segment_file,
                        repo_type="dataset",
                        token=HF_TOKEN,
                        cache_dir="/tmp/video_processor_cache"
                    )

                    with open(segment_path, 'r', encoding='utf-8') as f:
                        segment_data = json.load(f)

                    segment_number = segment_data.get("segment_number", 1)
                    start_time = segment_data.get("start_time", "00:00:00")
                    end_time = segment_data.get("end_time", "00:10:00")

                    print(f"\nProcessing segment {segment_number}: {start_time} to {end_time}")

                    output_filename = f"segment-{segment_number:02d}.mp4"
                    output_path = os.path.join(temp_dir, output_filename)

                    success = process_video_segment(
                        video_path,
                        output_path,
                        start_time,
                        end_time
                    )

                    if not success:
                        print(f"Failed to process segment {segment_number}")
                        continue

                    upload_path = f"{READY_VIDEOS_FOLDER}/{movie_name}/{output_filename}"
                    print(f"Uploading to: {upload_path}")

                    upload_file(
                        path_or_fileobj=output_path,
                        path_in_repo=upload_path,
                        repo_id=HF_DATASET_REPO,
                        repo_type="dataset",
                        token=HF_TOKEN,
                        commit_message=f"Add processed video segment {segment_number} for {movie_name}"
                    )
                    print(f"✓ Segment {segment_number} uploaded successfully")

                except Exception as e:
                    print(f"✗ Error processing segment: {e}")
                    processing_state["error_count"] += 1
                    continue

        finally:
            import shutil
            shutil.rmtree(temp_dir, ignore_errors=True)

        processing_state["processed_files"].append(movie_name)
        processing_state["total_processed"] += 1
        print(f"\n✓ Successfully processed all segments for {movie_name}")
        return True

    except Exception as e:
        processing_state["error_count"] += 1
        processing_state["last_error"] = str(e)
        print(f"✗ Error: {e}")
        return False


async def scan_and_process_videos():
    """Scan hooks folder and process all movies."""
    if processing_state["is_running"]:
        print("Video processing already running, skipping...")
        return

    # Wait 3 minutes for Space to fully initialize
    print("Waiting 3 minutes before starting video processing...")
    await asyncio.sleep(180)

    processing_state["is_running"] = True
    print("\n" + "="*80)
    print("STARTING VIDEO PROCESSING SERVICE")
    print("="*80)

    try:
        files = list_repo_files(
            repo_id=HF_DATASET_REPO,
            repo_type="dataset",
            token=HF_TOKEN
        )

        movie_folders = set()
        for f in files:
            if f.startswith(f"{HOOKS_FOLDER}/") and f.endswith(".json"):
                parts = f.split("/")
                if len(parts) >= 2:
                    movie_folders.add(parts[1])

        print(f"Found {len(movie_folders)} movies to process")

        for movie_name in sorted(movie_folders):
            await process_movie_segments(movie_name)
            await asyncio.sleep(2)

        print("\n" + "="*80)
        print("VIDEO PROCESSING COMPLETE")
        print(f"Processed: {processing_state['total_processed']}")
        print(f"Errors: {processing_state['error_count']}")
        print("="*80 + "\n")

    except Exception as e:
        print(f"Critical error: {e}")
        processing_state["last_error"] = str(e)
    finally:
        processing_state["is_running"] = False


@app.on_event("startup")
async def startup_event():
    """Load Whisper in background, then kick off video processing after 3 min."""
    loop = asyncio.get_event_loop()
    # Load Whisper model in thread so it doesn't block the event loop / health check
    await loop.run_in_executor(None, _load_whisper_model)
    # Kick off processing task (has its own 3-min delay inside)
    asyncio.create_task(scan_and_process_videos())


@app.get("/")
async def health():
    return JSONResponse({
        "status": "running",
        "service": "Video Processing Service",
        "whisper_ready": processing_state["whisper_ready"],
        "is_processing": processing_state["is_running"],
        "total_processed": processing_state["total_processed"],
        "error_count": processing_state["error_count"],
        "current_file": processing_state["current_file"],
        "last_error": processing_state["last_error"],
        "processed_files": processing_state["processed_files"]
    })


@app.get("/status")
async def get_status():
    return JSONResponse({
        "whisper_ready": processing_state["whisper_ready"],
        "is_running": processing_state["is_running"],
        "total_processed": processing_state["total_processed"],
        "error_count": processing_state["error_count"],
        "current_file": processing_state["current_file"],
        "last_error": processing_state["last_error"],
        "processed_files": processing_state["processed_files"]
    })


@app.post("/trigger-processing")
async def trigger_processing():
    if processing_state["is_running"]:
        return JSONResponse({
            "status": "already_running",
            "message": "Video processing is already in progress"
        })
    if not processing_state["whisper_ready"]:
        return JSONResponse({
            "status": "not_ready",
            "message": "Whisper model is still loading, try again shortly"
        })
    asyncio.create_task(scan_and_process_videos())
    return JSONResponse({
        "status": "started",
        "message": "Video processing scan started"
    })


if __name__ == "__main__":
    print("Starting Video Processing Service on port 7860...")
    print("Whisper will load at startup, processing begins 3 minutes after")
    uvicorn.run(app, host="0.0.0.0", port=7860)