import os
import numpy as np
from keras_applications.mobilenet_v2 import MobileNetV2, preprocess_input, decode_predictions
from keras_preprocessing import image
import cv2

# Load the model once at import time
mobilenet_model = MobileNetV2(weights='imagenet')

def generate_image_hashtags(img_path, top_n=5):
    """
    Generate hashtags for an image file using MobileNetV2.
    """
    if not os.path.exists(img_path):
        return []
    try:
        img = image.load_img(img_path, target_size=(224, 224))
        x = image.img_to_array(img)
        x = np.expand_dims(x, axis=0)
        x = preprocess_input(x)
        preds = mobilenet_model.predict(x)
        decoded = decode_predictions(preds, top=top_n)[0]
        hashtags = ['#' + label.replace('_', '') for (_, label, _) in decoded]
        return hashtags
    except Exception as e:
        print(f"Error generating hashtags for image: {e}")
        return []

def extract_first_frame(video_path, out_img_path):
    """
    Extract the first frame from a video and save it as an image.
    Returns the image path if successful, else None.
    """
    if not os.path.exists(video_path):
        return None
    try:
        cap = cv2.VideoCapture(video_path)
        ret, frame = cap.read()
        if ret:
            cv2.imwrite(out_img_path, frame)
            cap.release()
            return out_img_path
        cap.release()
        return None
    except Exception as e:
        print(f"Error extracting frame from video: {e}")
        return None

def generate_video_hashtags(video_path, top_n=5):
    """
    Generate hashtags for a video file by extracting the first frame and running image tagging.
    """
    frame_path = video_path + "_frame.jpg"
    img_path = extract_first_frame(video_path, frame_path)
    if img_path:
        hashtags = generate_image_hashtags(img_path, top_n=top_n)
        # Optionally, remove the frame image after use
        try:
            os.remove(img_path)
        except Exception:
            pass
        return hashtags
    return []