live_processor.py

"""EmoSphere Live Session Processor — real-time multimodal processing.

Designed for use with streamlit-webrtc. Handles:
  - Video frame processing (face + posture at ~2fps)
  - Audio chunk accumulation and processing (3-second windows)
  - Speech-to-text via faster-whisper (base, multilingual — 99 languages)
  - Fuzzy fusion after each processing cycle
  - Thread-safe shared state for Streamlit display
  - Emotion timeline tracking
  - Keyword-based topic/trigger extraction
"""

from __future__ import annotations

import io
import time
import threading
from collections import deque
from dataclasses import dataclass, field
from typing import Optional

import numpy as np

from models import (
    EmotionLabel,
    EMOTION_LABELS,
    EmotionScore,
    EmotionDetectionResult,
    FusedDetectionResult,
    CulturalRegion,
)

try:
    from PIL import Image
    HAS_PIL = True
except ImportError:
    HAS_PIL = False

try:
    import cv2
    HAS_CV2 = True
except ImportError:
    HAS_CV2 = False


# =====================================================================
# Data Structures
# =====================================================================

@dataclass
class TranscriptSegment:
    text: str
    timestamp: float  # seconds from session start
    emotion: Optional[EmotionLabel] = None
    confidence: float = 0.0


@dataclass
class TimelineEntry:
    timestamp: float  # seconds from session start
    fused_result: FusedDetectionResult
    transcript: str = ""
    topics: list[str] = field(default_factory=list)
    fired_rules: list[str] = field(default_factory=list)


@dataclass
class SessionSummary:
    duration_seconds: float
    total_video_frames: int
    total_audio_chunks: int
    total_transcript_segments: int
    dominant_emotion: EmotionLabel
    emotion_distribution: dict[str, float]
    modality_contribution: dict[str, float]
    topics_detected: list[str]
    emotional_shifts: list[dict]
    peaks: list[dict]


# =====================================================================
# Keyword-based Topic Extractor (lightweight, no KeyBERT)
# =====================================================================

# Reuse the keyword lists from text_detector.py for topic/trigger detection
TOPIC_KEYWORDS: dict[str, list[str]] = {
    "happiness": ["happy", "glad", "excited", "wonderful", "great", "amazing", "awesome",
                   "fantastic", "smile", "laugh", "fun", "enjoy", "delighted", "thrilled"],
    "grief": ["sad", "unhappy", "depressed", "lonely", "miss", "cry", "tears",
              "heartbreak", "grief", "loss", "mourning"],
    "anxiety": ["afraid", "scared", "worried", "anxious", "nervous", "terrified",
                "panic", "dread", "uneasy", "stressed", "overwhelmed", "tense"],
    "relationships": ["love", "adore", "partner", "together", "family", "friend",
                      "relationship", "marriage", "breakup", "divorce", "trust"],
    "work": ["work", "job", "boss", "career", "office", "deadline", "project",
             "meeting", "fired", "promoted", "colleague", "salary"],
    "health": ["health", "sick", "pain", "doctor", "hospital", "tired",
               "sleep", "insomnia", "medication", "therapy", "diagnosis"],
    "self_esteem": ["worthless", "failure", "ugly", "stupid", "hopeless",
                    "useless", "inadequate", "ashamed", "embarrassed", "confident"],
    "change": ["change", "different", "new", "suddenly", "unexpected",
               "surprise", "shocking", "unbelievable", "transform"],
    "calm": ["calm", "peaceful", "relaxed", "serene", "tranquil",
             "meditate", "breathe", "mindful", "quiet", "gentle"],
    "conflict": ["angry", "furious", "annoyed", "frustrated", "argue",
                 "fight", "conflict", "hate", "rage", "resent"],
}


def extract_topics(text: str) -> list[str]:
    """Extract topic/trigger keywords from text using simple lexicon matching."""
    lower = text.lower()
    found = []
    for topic, keywords in TOPIC_KEYWORDS.items():
        if any(kw in lower for kw in keywords):
            found.append(topic)
    return found


# =====================================================================
# Live Session Processor
# =====================================================================

class LiveSessionProcessor:
    """Thread-safe processor for real-time multimodal emotion analysis.

    Integrates with streamlit-webrtc callbacks for video and audio.
    Maintains shared state that Streamlit can poll for display updates.
    """

    def __init__(self):
        self._lock = threading.Lock()

        # Detectors (loaded lazily)
        self._face_det = None
        self._voice_det = None
        self._text_det = None
        self._posture_det = None
        self._fusion = None
        self._whisper_model = None

        # Processing state
        self._session_active = False
        self._session_start: float = 0.0
        self._last_video_process: float = 0.0
        self._video_frame_count: int = 0
        self._posture_frame_count: int = 0

        # Audio accumulation
        self._audio_buffer: list[np.ndarray] = []
        self._audio_sample_rate: int = 16000
        self._last_audio_process: float = 0.0
        self._audio_chunk_count: int = 0

        # Latest results per modality
        self._face_result: Optional[EmotionDetectionResult] = None
        self._voice_result: Optional[EmotionDetectionResult] = None
        self._text_result: Optional[EmotionDetectionResult] = None
        self._posture_result: Optional[EmotionDetectionResult] = None
        self._fused_result: Optional[FusedDetectionResult] = None

        # Timeline and transcript
        self._timeline: list[TimelineEntry] = []
        self._transcript: list[TranscriptSegment] = []
        self._topics_seen: list[str] = []

        # Modality processing time accumulators
        self._modality_times: dict[str, list[float]] = {
            "face": [], "voice": [], "text": [], "posture": [],
        }

    # ── Initialization ──────────────────────────────────────────────

    def initialize(self):
        """Load all detectors and models. Call once at startup."""
        from face_detector import FaceEmotionDetector
        from voice_detector import VoiceEmotionDetector
        from text_detector import TextEmotionDetector
        from posture_detector import PostureEmotionDetector
        from fuzzy_fusion import FuzzyFusionEngine

        self._face_det = FaceEmotionDetector()
        self._face_det.load()

        self._voice_det = VoiceEmotionDetector()
        self._voice_det.load()

        self._text_det = TextEmotionDetector()
        self._text_det.load()

        self._posture_det = PostureEmotionDetector()
        self._posture_det.load()

        self._fusion = FuzzyFusionEngine()

        # Load faster-whisper for STT (base multilingual — good accuracy, fast on CPU)
        try:
            from faster_whisper import WhisperModel
            self._whisper_model = WhisperModel(
                "base", device="cpu", compute_type="int8"
            )
            print("[LiveProcessor] faster-whisper base (multilingual) loaded")
        except ImportError:
            print("[LiveProcessor] faster-whisper not available, STT disabled")
            self._whisper_model = None
        except Exception as e:
            print(f"[LiveProcessor] Whisper load error: {e}")
            self._whisper_model = None

        print("[LiveProcessor] All detectors initialized")

    # ── Session Control ─────────────────────────────────────────────

    def start_session(self):
        """Start a new live session."""
        with self._lock:
            self._session_active = True
            self._session_start = time.time()
            self._last_video_process = 0.0
            self._last_audio_process = 0.0
            self._video_frame_count = 0
            self._posture_frame_count = 0
            self._audio_chunk_count = 0
            self._audio_buffer = []
            self._face_result = None
            self._voice_result = None
            self._text_result = None
            self._posture_result = None
            self._fused_result = None
            self._timeline = []
            self._transcript = []
            self._topics_seen = []
            self._modality_times = {
                "face": [], "voice": [], "text": [], "posture": [],
            }

    def stop_session(self):
        """Stop the current session."""
        with self._lock:
            self._session_active = False

    @property
    def is_active(self) -> bool:
        return self._session_active

    @property
    def elapsed_seconds(self) -> float:
        if not self._session_active:
            return 0.0
        return time.time() - self._session_start

    # ── Direct Image/Audio Processing (for custom webcam component) ──

    def process_image(self, image_bytes: bytes):
        """Process a single image for face + posture/gesture detection (non-blocking)."""
        if not self._session_active:
            return

        # Run in background thread to avoid blocking Streamlit UI
        t = threading.Thread(
            target=self._process_image_worker,
            args=(image_bytes,),
            daemon=True,
        )
        t.start()

    def _process_image_worker(self, image_bytes: bytes):
        """Background worker for image processing (face + posture/gesture)."""
        self._video_frame_count += 1

        # Face emotion detection
        if self._face_det is not None:
            try:
                result = self._face_det.detect(image_bytes)
                with self._lock:
                    self._face_result = result
                    self._modality_times["face"].append(result.processing_time_ms)
            except Exception as e:
                print(f"[LiveProcessor] Face detect error: {e}")

        # Posture/gesture detection every 2nd frame
        if self._video_frame_count % 2 == 0 and self._posture_det is not None:
            try:
                self._posture_frame_count += 1
                p_result = self._posture_det.detect(image_bytes)
                with self._lock:
                    self._posture_result = p_result
                    self._modality_times["posture"].append(p_result.processing_time_ms)
            except Exception as e:
                print(f"[LiveProcessor] Posture detect error: {e}")

        self._run_fusion()

    def _convert_webm_to_wav(self, audio_bytes: bytes) -> Optional[str]:
        """Convert webm/opus audio bytes to a WAV file via ffmpeg. Returns wav path or None."""
        import tempfile, subprocess, os
        tmp_in = None
        try:
            tmp_in = tempfile.NamedTemporaryFile(suffix=".webm", delete=False)
            tmp_in.write(audio_bytes)
            tmp_in.flush()
            tmp_in.close()
            tmp_out = tmp_in.name.replace(".webm", ".wav")
            result = subprocess.run(
                ["ffmpeg", "-y", "-i", tmp_in.name,
                 "-ar", "16000", "-ac", "1", "-f", "wav", tmp_out],
                capture_output=True, timeout=10,
            )
            os.unlink(tmp_in.name)
            if result.returncode == 0 and os.path.exists(tmp_out):
                return tmp_out
            print(f"[LiveProcessor] ffmpeg failed: {result.stderr[:200] if result.stderr else 'unknown'}")
            try:
                os.unlink(tmp_out)
            except Exception:
                pass
        except Exception as e:
            print(f"[LiveProcessor] webm→wav conversion error: {e}")
            if tmp_in:
                try:
                    os.unlink(tmp_in.name)
                except Exception:
                    pass
        return None

    def process_audio_bytes(self, audio_bytes: bytes, language: str = None):
        """Process raw audio bytes for voice emotion + STT (non-blocking via thread)."""
        if not self._session_active:
            return

        # Run audio processing in background thread to avoid blocking Streamlit UI
        t = threading.Thread(
            target=self._process_audio_worker,
            args=(audio_bytes, language),
            daemon=True,
        )
        t.start()

    def _process_audio_worker(self, audio_bytes: bytes, language: str = None):
        """Background worker for audio processing (voice emotion + STT)."""
        import os

        self._audio_chunk_count += 1

        # Convert webm to wav once — reuse for both voice detection and STT
        wav_path = self._convert_webm_to_wav(audio_bytes)

        # Voice emotion detection
        if self._voice_det is not None:
            try:
                # Use converted WAV if available, otherwise raw bytes
                audio_for_voice = open(wav_path, "rb").read() if wav_path else audio_bytes
                result = self._voice_det.detect(audio_for_voice)
                with self._lock:
                    self._voice_result = result
                    self._modality_times["voice"].append(result.processing_time_ms)
                print(f"[LiveProcessor] Voice: {result.dominant.value} ({result.dominant_score:.2f})")
            except Exception as e:
                print(f"[LiveProcessor] Voice detect error: {e}")

        # Speech-to-text via faster-whisper
        if self._whisper_model is not None and wav_path:
            try:
                segments, _info = self._whisper_model.transcribe(
                    wav_path,
                    beam_size=3,
                    language=language,
                    vad_filter=True,
                    vad_parameters=dict(min_silence_duration_ms=300),
                )
                detected_lang = getattr(_info, 'language', 'unknown')
                print(f"[LiveProcessor] Whisper detected language: {detected_lang}")

                for seg in segments:
                    text = seg.text.strip()
                    if not text or len(text) <= 2:
                        continue
                    # Skip common whisper hallucinations
                    if text.lower() in ("thank you.", "thanks.", "you", "bye.", "the end.",
                                        "thanks for watching.", "subscribe.", "...",
                                        "thank you for watching."):
                        print(f"[LiveProcessor] Skipping hallucination: {text}")
                        continue

                    print(f"[LiveProcessor] Transcript: '{text}'")

                    # Text emotion
                    t_result = None
                    if self._text_det is not None:
                        t_result = self._text_det.detect(text)
                        with self._lock:
                            self._text_result = t_result

                    elapsed = time.time() - self._session_start
                    segment = TranscriptSegment(
                        text=text,
                        timestamp=elapsed,
                        emotion=t_result.dominant if t_result else None,
                        confidence=t_result.confidence if t_result else 0.0,
                    )
                    with self._lock:
                        self._transcript.append(segment)

                    topics = extract_topics(text)
                    if topics:
                        with self._lock:
                            for t in topics:
                                if t not in self._topics_seen:
                                    self._topics_seen.append(t)

            except Exception as e:
                print(f"[LiveProcessor] STT error: {e}")
        elif self._whisper_model is not None and not wav_path:
            print("[LiveProcessor] STT skipped — audio conversion failed")

        # Clean up wav file
        if wav_path:
            try:
                os.unlink(wav_path)
            except Exception:
                pass

        self._run_fusion()

    # ── Video Frame Callback (for streamlit-webrtc — legacy) ────────

    def video_frame_callback(self, frame) -> object:
        """Process a video frame from streamlit-webrtc.

        Called for every frame. We subsample to ~2fps for face detection
        and ~every 5th processed frame for posture.

        Args:
            frame: av.VideoFrame from streamlit-webrtc

        Returns:
            The frame unchanged (passthrough for display).
        """
        if not self._session_active:
            return frame

        now = time.time()

        # Subsample: process face at ~2fps (every 500ms)
        if now - self._last_video_process < 0.5:
            return frame

        self._last_video_process = now
        self._video_frame_count += 1

        try:
            # Convert av.VideoFrame to numpy array
            img = frame.to_ndarray(format="bgr24")

            # Face emotion detection
            if self._face_det is not None:
                # Convert BGR to RGB for PIL
                rgb = img[:, :, ::-1]
                # Encode to bytes for the detector
                if HAS_PIL:
                    pil_img = Image.fromarray(rgb)
                    buf = io.BytesIO()
                    pil_img.save(buf, format="JPEG", quality=70)
                    img_bytes = buf.getvalue()
                else:
                    success, enc = cv2.imencode(".jpg", img)
                    img_bytes = enc.tobytes() if success else b""

                if img_bytes:
                    result = self._face_det.detect(img_bytes)
                    with self._lock:
                        self._face_result = result
                        self._modality_times["face"].append(result.processing_time_ms)

            # Posture detection every 5th processed frame (~0.4fps)
            if self._video_frame_count % 5 == 0 and self._posture_det is not None:
                self._posture_frame_count += 1
                if HAS_CV2:
                    success, enc = cv2.imencode(".jpg", img)
                    posture_bytes = enc.tobytes() if success else b""
                elif HAS_PIL:
                    pil_img = Image.fromarray(img[:, :, ::-1])
                    buf = io.BytesIO()
                    pil_img.save(buf, format="JPEG", quality=70)
                    posture_bytes = buf.getvalue()
                else:
                    posture_bytes = b""

                if posture_bytes:
                    p_result = self._posture_det.detect(posture_bytes)
                    with self._lock:
                        self._posture_result = p_result
                        self._modality_times["posture"].append(p_result.processing_time_ms)

            # Run fusion after face processing
            self._run_fusion()

        except Exception as e:
            print(f"[LiveProcessor] Video frame error: {e}")

        return frame

    # ── Audio Frame Callback (for streamlit-webrtc) ─────────────────

    def audio_frame_callback(self, frame) -> object:
        """Process an audio frame from streamlit-webrtc.

        Accumulates audio data and processes every ~3 seconds for:
          - Voice emotion detection
          - Speech-to-text transcription

        Args:
            frame: av.AudioFrame from streamlit-webrtc

        Returns:
            The frame unchanged (passthrough).
        """
        if not self._session_active:
            return frame

        try:
            # Convert av.AudioFrame to numpy
            audio_array = frame.to_ndarray()
            # audio_array shape: (channels, samples) — take first channel
            if audio_array.ndim > 1:
                audio_array = audio_array[0]

            # Convert to float32 normalized
            if audio_array.dtype == np.int16:
                audio_array = audio_array.astype(np.float32) / 32768.0
            elif audio_array.dtype == np.int32:
                audio_array = audio_array.astype(np.float32) / 2147483648.0

            self._audio_buffer.append(audio_array)

            # Get sample rate from frame
            self._audio_sample_rate = frame.sample_rate or 16000

            # Process every ~3 seconds of accumulated audio
            now = time.time()
            if now - self._last_audio_process >= 3.0 and len(self._audio_buffer) > 0:
                self._last_audio_process = now
                self._process_audio_chunk()

        except Exception as e:
            print(f"[LiveProcessor] Audio frame error: {e}")

        return frame

    def _process_audio_chunk(self):
        """Process accumulated audio buffer for voice emotion + STT."""
        # Concatenate buffered audio
        with self._lock:
            if not self._audio_buffer:
                return
            audio = np.concatenate(self._audio_buffer)
            self._audio_buffer = []

        self._audio_chunk_count += 1

        # Resample to 16000 Hz if needed
        if self._audio_sample_rate != 16000:
            try:
                import librosa
                audio = librosa.resample(
                    audio, orig_sr=self._audio_sample_rate, target_sr=16000
                )
            except ImportError:
                # Simple decimation/interpolation fallback
                ratio = 16000 / self._audio_sample_rate
                new_len = int(len(audio) * ratio)
                indices = np.linspace(0, len(audio) - 1, new_len).astype(int)
                audio = audio[indices]

        # Skip if audio is too short (< 0.5s)
        if len(audio) < 8000:
            return

        # Voice emotion detection
        if self._voice_det is not None:
            try:
                v_result = self._voice_det.detect(audio, sample_rate=16000)
                with self._lock:
                    self._voice_result = v_result
                    self._modality_times["voice"].append(v_result.processing_time_ms)
            except Exception as e:
                print(f"[LiveProcessor] Voice detection error: {e}")

        # Speech-to-text
        transcript_text = ""
        if self._whisper_model is not None:
            try:
                segments, info = self._whisper_model.transcribe(
                    audio,
                    beam_size=1,
                    
                    vad_filter=True,
                )
                parts = []
                for seg in segments:
                    parts.append(seg.text.strip())
                transcript_text = " ".join(parts).strip()
            except Exception as e:
                print(f"[LiveProcessor] STT error: {e}")

        # If we got text, run text emotion detection + topic extraction
        if transcript_text:
            if self._text_det is not None:
                try:
                    t_result = self._text_det.detect(transcript_text)
                    with self._lock:
                        self._text_result = t_result
                        self._modality_times["text"].append(t_result.processing_time_ms)
                except Exception as e:
                    print(f"[LiveProcessor] Text detection error: {e}")

            # Record transcript segment
            elapsed = time.time() - self._session_start
            text_emotion = self._text_result.dominant if self._text_result else None
            text_conf = self._text_result.confidence if self._text_result else 0.0
            segment = TranscriptSegment(
                text=transcript_text,
                timestamp=elapsed,
                emotion=text_emotion,
                confidence=text_conf,
            )
            with self._lock:
                self._transcript.append(segment)

            # Topic extraction
            topics = extract_topics(transcript_text)
            if topics:
                with self._lock:
                    for t in topics:
                        if t not in self._topics_seen:
                            self._topics_seen.append(t)

        # Run fusion after audio processing
        self._run_fusion()

    # ── Manual Text Input ───────────────────────────────────────────

    def process_text(self, text: str):
        """Process manually typed text (e.g., from a text input field)."""
        if not self._session_active or not text.strip():
            return

        if self._text_det is not None:
            try:
                t_result = self._text_det.detect(text)
                with self._lock:
                    self._text_result = t_result
            except Exception:
                pass

        elapsed = time.time() - self._session_start
        text_emotion = self._text_result.dominant if self._text_result else None
        text_conf = self._text_result.confidence if self._text_result else 0.0
        segment = TranscriptSegment(
            text=text,
            timestamp=elapsed,
            emotion=text_emotion,
            confidence=text_conf,
        )
        with self._lock:
            self._transcript.append(segment)

        topics = extract_topics(text)
        if topics:
            with self._lock:
                for t in topics:
                    if t not in self._topics_seen:
                        self._topics_seen.append(t)

        self._run_fusion()

    # ── Fusion ──────────────────────────────────────────────────────

    def _run_fusion(self):
        """Run fuzzy fusion on the latest modality results."""
        if self._fusion is None:
            return

        with self._lock:
            face = self._face_result
            voice = self._voice_result
            text = self._text_result
            posture = self._posture_result

        if not any([face, voice, text, posture]):
            return

        try:
            fused = self._fusion.fuse(
                face=face,
                voice=voice,
                text=text,
                posture=posture,
            )
            elapsed = time.time() - self._session_start

            # Detect topics from latest transcript
            topics = []
            with self._lock:
                if self._transcript:
                    topics = extract_topics(self._transcript[-1].text)

            entry = TimelineEntry(
                timestamp=elapsed,
                fused_result=fused,
                transcript=self._transcript[-1].text if self._transcript else "",
                topics=topics,
            )

            with self._lock:
                self._fused_result = fused
                self._timeline.append(entry)

        except Exception as e:
            print(f"[LiveProcessor] Fusion error: {e}")

    # ── State Accessors (thread-safe) ───────────────────────────────

    def get_latest_fused(self) -> Optional[FusedDetectionResult]:
        with self._lock:
            return self._fused_result

    def get_latest_face(self) -> Optional[EmotionDetectionResult]:
        with self._lock:
            return self._face_result

    def get_latest_voice(self) -> Optional[EmotionDetectionResult]:
        with self._lock:
            return self._voice_result

    def get_latest_text(self) -> Optional[EmotionDetectionResult]:
        with self._lock:
            return self._text_result

    def get_latest_posture(self) -> Optional[EmotionDetectionResult]:
        with self._lock:
            return self._posture_result

    def get_timeline(self) -> list[TimelineEntry]:
        with self._lock:
            return list(self._timeline)

    def get_transcript(self) -> list[TranscriptSegment]:
        with self._lock:
            return list(self._transcript)

    def get_topics(self) -> list[str]:
        with self._lock:
            return list(self._topics_seen)

    def get_stats(self) -> dict:
        """Get session statistics."""
        with self._lock:
            return {
                "duration": time.time() - self._session_start if self._session_active else 0.0,
                "video_frames": self._video_frame_count,
                "posture_frames": self._posture_frame_count,
                "audio_chunks": self._audio_chunk_count,
                "transcript_segments": len(self._transcript),
                "timeline_entries": len(self._timeline),
                "topics": list(self._topics_seen),
            }

    # ── Summary Generation ──────────────────────────────────────────

    def generate_summary(self) -> Optional[SessionSummary]:
        """Generate a session summary after the session ends."""
        with self._lock:
            timeline = list(self._timeline)
            transcript = list(self._transcript)
            topics = list(self._topics_seen)
            stats = {
                "duration": (time.time() - self._session_start) if self._session_start else 0.0,
                "video_frames": self._video_frame_count,
                "audio_chunks": self._audio_chunk_count,
                "transcript_segments": len(transcript),
            }
            modality_times = {k: list(v) for k, v in self._modality_times.items()}

        if not timeline:
            return None

        # Compute emotion distribution from timeline
        emotion_accum: dict[str, float] = {label.value: 0.0 for label in EMOTION_LABELS}
        for entry in timeline:
            for score in entry.fused_result.scores:
                emotion_accum[score.label.value] += score.score
        total_entries = len(timeline)
        emotion_dist = {k: v / total_entries for k, v in emotion_accum.items()}

        # Dominant emotion overall
        dominant_val = max(emotion_dist, key=emotion_dist.get)  # type: ignore
        dominant_emotion = EmotionLabel(dominant_val)

        # Modality contribution (average weight across timeline)
        mod_weights: dict[str, list[float]] = {}
        for entry in timeline:
            for mod, w in entry.fused_result.modality_weights.items():
                if mod not in mod_weights:
                    mod_weights[mod] = []
                mod_weights[mod].append(w)
        modality_contribution = {
            mod: sum(ws) / len(ws) for mod, ws in mod_weights.items()
        }

        # Detect emotional shifts (dominant emotion changes)
        shifts: list[dict] = []
        peaks: list[dict] = []
        prev_dominant = None
        for entry in timeline:
            dom = entry.fused_result.dominant
            dom_score = entry.fused_result.dominant_score

            if prev_dominant is not None and dom != prev_dominant:
                shifts.append({
                    "timestamp": entry.timestamp,
                    "from": prev_dominant.value,
                    "to": dom.value,
                    "score": dom_score,
                    "transcript": entry.transcript[:80] if entry.transcript else "",
                })

            # Detect peaks (score > 0.5 for non-neutral)
            if dom != EmotionLabel.NEUTRAL and dom_score > 0.5:
                peaks.append({
                    "timestamp": entry.timestamp,
                    "emotion": dom.value,
                    "score": dom_score,
                    "transcript": entry.transcript[:80] if entry.transcript else "",
                })

            prev_dominant = dom

        return SessionSummary(
            duration_seconds=stats["duration"],
            total_video_frames=stats["video_frames"],
            total_audio_chunks=stats["audio_chunks"],
            total_transcript_segments=stats["transcript_segments"],
            dominant_emotion=dominant_emotion,
            emotion_distribution=emotion_dist,
            modality_contribution=modality_contribution,
            topics_detected=topics,
            emotional_shifts=shifts,
            peaks=peaks,
        )

    # ── Video File Processing ──────────────────────────────────────

    def process_video_file(self, video_bytes: bytes, progress_callback=None) -> Optional[SessionSummary]:
        """Process an uploaded video file frame by frame.

        Extracts frames at ~2fps for face + posture detection,
        extracts audio for voice emotion + STT, then fuses all modalities.

        Args:
            video_bytes: Raw video file bytes (mp4, webm, etc.)
            progress_callback: Optional callable(progress_float) for UI updates.

        Returns:
            SessionSummary after processing, or None on failure.
        """
        import tempfile, os

        if not HAS_CV2:
            print("[LiveProcessor] cv2 required for video file processing")
            return None

        # Write to temp file for OpenCV
        tmp = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
        tmp.write(video_bytes)
        tmp.flush()
        tmp_path = tmp.name
        tmp.close()

        try:
            cap = cv2.VideoCapture(tmp_path)
            if not cap.isOpened():
                print("[LiveProcessor] Failed to open video file")
                return None

            fps = cap.get(cv2.CAP_PROP_FPS) or 30.0
            total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
            duration = total_frames / fps if fps > 0 else 0

            # Start a virtual session
            self.start_session()

            # Process at ~2fps
            frame_interval = max(1, int(fps / 2))
            frame_idx = 0

            while True:
                ret, frame = cap.read()
                if not ret:
                    break

                if frame_idx % frame_interval == 0:
                    current_time = frame_idx / fps
                    self._session_start = time.time() - current_time

                    # Face detection
                    if self._face_det is not None:
                        try:
                            rgb = frame[:, :, ::-1]
                            if HAS_PIL:
                                pil_img = Image.fromarray(rgb)
                                buf = io.BytesIO()
                                pil_img.save(buf, format="JPEG", quality=70)
                                img_bytes = buf.getvalue()
                            else:
                                success, enc = cv2.imencode(".jpg", frame)
                                img_bytes = enc.tobytes() if success else b""
                            if img_bytes:
                                result = self._face_det.detect(img_bytes)
                                with self._lock:
                                    self._face_result = result
                                    self._video_frame_count += 1
                        except Exception as e:
                            print(f"[VideoFile] Face error frame {frame_idx}: {e}")

                    # Posture every 5th processed frame
                    if (self._video_frame_count % 5 == 0) and self._posture_det is not None:
                        try:
                            success, enc = cv2.imencode(".jpg", frame)
                            if success:
                                p_result = self._posture_det.detect(enc.tobytes())
                                with self._lock:
                                    self._posture_result = p_result
                                    self._posture_frame_count += 1
                        except Exception as e:
                            print(f"[VideoFile] Posture error frame {frame_idx}: {e}")

                    # Run fusion
                    self._run_fusion()

                    # Progress callback
                    if progress_callback and total_frames > 0:
                        progress_callback(min(frame_idx / total_frames, 0.9))

                frame_idx += 1

            cap.release()

            # Extract and process audio track
            self._process_video_audio(tmp_path, duration)

            # Final fusion
            self._run_fusion()

            if progress_callback:
                progress_callback(1.0)

            self.stop_session()
            return self.generate_summary()

        except Exception as e:
            print(f"[LiveProcessor] Video file error: {e}")
            self.stop_session()
            return None
        finally:
            try:
                os.unlink(tmp_path)
            except Exception:
                pass

    def _process_video_audio(self, video_path: str, duration: float):
        """Extract audio from video file and process for voice emotion + STT."""
        try:
            import subprocess, tempfile, os

            # Extract audio with ffmpeg (available on HF Spaces)
            audio_tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
            audio_path = audio_tmp.name
            audio_tmp.close()

            result = subprocess.run(
                ["ffmpeg", "-i", video_path, "-vn", "-acodec", "pcm_s16le",
                 "-ar", "16000", "-ac", "1", "-y", audio_path],
                capture_output=True, timeout=30,
            )

            if result.returncode != 0:
                print(f"[VideoFile] ffmpeg audio extract failed: {result.stderr[:200]}")
                return

            # Read audio and process
            import soundfile as sf
            audio_data, sr = sf.read(audio_path)
            if audio_data.dtype != np.float32:
                audio_data = audio_data.astype(np.float32)

            # Voice emotion on full audio
            if self._voice_det is not None:
                try:
                    audio_bytes = io.BytesIO()
                    sf.write(audio_bytes, audio_data, sr, format="WAV")
                    audio_bytes.seek(0)
                    voice_result = self._voice_det.detect(audio_bytes.read())
                    with self._lock:
                        self._voice_result = voice_result
                        self._audio_chunk_count += 1
                except Exception as e:
                    print(f"[VideoFile] Voice detection error: {e}")

            # STT on audio
            if self._whisper_model is not None:
                try:
                    segments, _info = self._whisper_model.transcribe(
                        audio_path,  beam_size=1
                    )
                    for seg in segments:
                        text = seg.text.strip()
                        if text:
                            # Text emotion
                            t_result = None
                            if self._text_det is not None:
                                t_result = self._text_det.detect(text)
                                with self._lock:
                                    self._text_result = t_result

                            segment = TranscriptSegment(
                                text=text,
                                timestamp=seg.start,
                                emotion=t_result.dominant if t_result else None,
                                confidence=t_result.confidence if t_result else 0.0,
                            )
                            with self._lock:
                                self._transcript.append(segment)

                            topics = extract_topics(text)
                            if topics:
                                with self._lock:
                                    for t in topics:
                                        if t not in self._topics_seen:
                                            self._topics_seen.append(t)
                except Exception as e:
                    print(f"[VideoFile] STT error: {e}")

            try:
                os.unlink(audio_path)
            except Exception:
                pass

        except Exception as e:
            print(f"[VideoFile] Audio processing error: {e}")