import cv2
import numpy as np
import time
from FeatureExtraction import FeatureExtractor
from Prediction import predict_fight

def process_video(video_path, sequence_length=40, threshold=0.8, output_frame_rate=30, debug=False):
    try:
        start_time = time.time()
        cap = cv2.VideoCapture(video_path)
        if not cap.isOpened():
            raise ValueError("Could not open video file")
        
        all_frames = []
        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break
            all_frames.append(frame)
        cap.release()
        total_frames = len(all_frames)
        
        feature_extractor = FeatureExtractor(img_shape=(224, 224), channels=3, seq_length=sequence_length)
        
        predictions_list = []
        predictions = []
        for i in range(0, total_frames, sequence_length):
            frames_buffer = all_frames[i:i + sequence_length]
            if len(frames_buffer) < sequence_length:
                frames_buffer += [frames_buffer[-1]] * (sequence_length - len(frames_buffer))
            
            fight_detected, fight_prob = predict_fight(frames_buffer, threshold, feature_extractor)
            predictions.append(fight_detected)
            predictions_list.append({
                'chunk_start_frame': i,
                'chunk_end_frame': i + sequence_length - 1,
                'fight_probability': float(fight_prob),
                'fight_detected': bool(fight_detected)
            })
        
        output_video_path = "output_labeled.mp4"
        height, width, _ = all_frames[0].shape
        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
        out = cv2.VideoWriter(output_video_path, fourcc, output_frame_rate, (width, height))
        
        frame_idx = 0
        for pred in predictions:
            label = "Violence Detected!" if pred else "No Violence"
            color = (0, 0, 255) if pred else (0, 255, 0)
            for _ in range(sequence_length):
                if frame_idx >= total_frames:
                    break
                frame = all_frames[frame_idx].copy()
                cv2.putText(frame, label, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)
                out.write(frame)
                frame_idx += 1
        out.release()
        
        processing_time = time.time() - start_time
        json_response = {
            'output_video_path': output_video_path,
            'total_frames': total_frames,
            'sequence_length': sequence_length,
            'threshold': threshold,
            'output_frame_rate': output_frame_rate,
            'processing_time_seconds': processing_time,
            'predictions': predictions_list,
            'error': None
        }
        return output_video_path, json_response
    except Exception as e:
        error_message = f"Error processing video: {str(e)}"
        json_response = {
            'output_video_path': None,
            'error': error_message
        }
        return None, json_response