models.py

from PIL import Image
import cv2
import numpy as np
import random

def analyze_content(file_path):
    # Placeholder for actual analysis - in a real application, this would use computer vision models
    # This demo provides fictional analysis for demonstration purposes
    
    if file_path.lower().endswith(('.mp4', '.avi', '.mov')):
        cap = cv2.VideoCapture(file_path)
        duration = int(cap.get(cv2.CAP_PROP_FRAME_COUNT) / cap.get(cv2.CAP_PROP_FPS)
        cap.release()
        media_type = "video"
        length = f"{duration:.1f} seconds"
    else:
        img = Image.open(file_path)
        width, height = img.size
        media_type = "image"
        length = f"{width}x{height} pixels"
    
    # Generate fictional analysis
    aspects = ["composition", "lighting", "focus", "color balance", "technical quality"]
    ratings = {aspect: random.randint(1, 10) for aspect in aspects}
    
    feedback = [
        "Professional analysis report:",
        f"Media type: {media_type}",
        f"Dimensions/duration: {length}",
        "\nDetailed assessment:"
    ]
    
    for aspect, score in ratings.items():
        feedback.append(f"- {aspect.capitalize()}: {score}/10")
        if score < 4:
            feedback.append("  (Needs significant improvement)")
        elif score < 7:
            feedback.append("  (Adequate but could be enhanced)")
        else:
            feedback.append("  (Well-executed)")
    
    feedback.append("\nRecommendations:")
    feedback.append("- Consider adjusting lighting conditions")
    feedback.append("- Ensure proper focus and framing")
    feedback.append("- Maintain consistent color temperature")
    
    return "\n".join(feedback)