backend/app/services/video_processor.py

import time
import logging
import os
import json
from sqlalchemy.orm import Session
from app.models.file_model import File
from app.ai.video.frame_extractor import extract_frames
from app.ai.video.frame_detector import analyze_frame
from app.ai.video.motion_detector import compute_motion_anomaly
from app.ai.video.noise_entropy_detector import compute_noise_entropy_anomaly
from app.ai.video.metadata_analyzer import analyze_metadata
from app.ai.video.aggregator import aggregate_scores
from app.ai.video.diffusion_spectrum_analyzer import compute_diffusion_spectrum_anomaly
from app.ai.video.keyframe_heatmap import generate_video_anomaly_keyframe
from app.ai.video.anomaly_gif_maker import generate_anomaly_gif
from app.core.storage import active_storage
import tempfile

logger = logging.getLogger(__name__)

def process_video_pipeline(file_id: int, file_path: str, db: Session):
    start_time = time.time()
    logger.info(f"Starting Video Pipeline for file_id: {file_id}")

    try:
        db_file = db.query(File).filter(File.id == file_id).first()
        if not db_file:
            logger.error(f"File ID {file_id} not found in DB.")
            return
            
        local_path = active_storage.download_to_temp(file_path)
        
        md_score, md_dict = analyze_metadata(local_path)

        frame_scores = []
        motion_scores = []
        noise_scores = []
        diffusion_scores = []

        previous_frame = None
        highest_ai_score = 0
        most_suspect_frame_idx = 0
        all_extracted_frames = []
        timeline_data = []

        for frame, timestamp_sec in extract_frames(local_path, sample_rate=15, max_frames=50):
            all_extracted_frames.append(frame.copy())
            
            # Module A: ViT Image Forensic Analysis
            f_score = analyze_frame(frame)
            frame_scores.append(f_score)
            
            # Add this specific second to the UI Scrubber Data
            timeline_data.append({"time": round(timestamp_sec, 2), "ai_score": round(f_score, 3)})

            if f_score > highest_ai_score:
                highest_ai_score = f_score
                most_suspect_frame_idx = len(all_extracted_frames) - 1

            # Module B: Microscopic Silicon Noise Analysis
            n_score = compute_noise_entropy_anomaly(frame)
            if n_score is not None:
                noise_scores.append(n_score)
            
            # Module C: Diffusion Spectrum Analysis (FFT)
            d_score = compute_diffusion_spectrum_anomaly(frame)
            diffusion_scores.append(d_score)
            
            # Module D: Farneback Optical Flow
            if previous_frame is not None:
                m_score = compute_motion_anomaly(previous_frame, frame)
                
                if m_score is not None:
                    motion_scores.append(m_score)
            
            previous_frame = frame

        if all_extracted_frames:
            # 1. Generate the 2-second GIF Clip 
            gif_path = generate_anomaly_gif(all_extracted_frames, most_suspect_frame_idx)
            
            # Upload GIF to R2
            class MockFile:
                def __init__(self, f):
                    self.file = f
                    self.content_type = "image/gif"
                    
            with open(gif_path, "rb") as hf:
                mock_hf = MockFile(hf)
                import uuid
                safe_gif_name = f"{uuid.uuid4().hex}.gif"
                r2_gif_key = active_storage.save(mock_hf, f"heatmaps/{safe_gif_name}")
                
            db_file.heatmap_path = r2_gif_key    # Overwriting the static image column with the GIF file!
            
            # clean up local gif
            if os.path.exists(gif_path):
                os.remove(gif_path)

            
            # 2. Save the JSON string for the Line Chart Scrubber
            db_file.timeline_data = json.dumps(timeline_data)

        avg_motion = sum(motion_scores)/len(motion_scores) if motion_scores else None
        avg_noise = sum(noise_scores)/len(noise_scores) if noise_scores else None
        avg_diffusion = sum(diffusion_scores)/len(diffusion_scores) if diffusion_scores else 0.0

        final_verdict = aggregate_scores(
            frame_scores=frame_scores,
            motion_score=avg_motion,
            noise_score=avg_noise,
            diffusion_score=avg_diffusion,
            metadata_score=md_score
        )

        db_file.status = "Completed"
        db_file.result = f"{final_verdict['label']} ({final_verdict['probability']*100:.1f}%)"
        db_file.confidence = final_verdict['probability']
        db_file.ai_explanation = final_verdict['explanation']

        db.commit()

        elapsed = time.time() - start_time
        logger.info(f"Successfully processed video {file_id} in {elapsed:.2f} seconds.")
        logger.info(f"Verdict: {db_file.result}")
    
    except Exception as e:
        logger.error(f"FATAL Pipeline Crash for file_id {file_id}: {str(e)}")

        try:
            db_file = db.query(File).filter(File.id == file_id).first()

            if db_file:
                db_file.status = "Failed"
                db_file.result = str(e)
                db.commit()
        except:
            pass
            
    finally:
        if 'local_path' in locals() and os.path.exists(local_path) and getattr(active_storage, '__class__').__name__ == "R2StorageProvider":
            os.remove(local_path)