predicting_image_input.ipynb

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    "# Predicting an Image input\n",
    "The model detects whether the image contains any instance of Sexual Harassment and classifies the image into two class labels,\n",
    "\n",
    "0 - Healthy Environment<br>\n",
    "1 - Sexual Harassment detected"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Importing necessary modules\n"
   ]
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   "source": [
    "# Load the trained model\n",
    "from keras.models import load_model\n",
    "import numpy as np\n",
    "from keras.preprocessing import image\n",
    "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
    "from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input\n"
   ]
  },
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   "source": [
    "### Loading the saved model\n",
    "The model saved as an output of 'Harassment_Detection_VGG16.ipynb' is loaded to make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea54e58c-ae55-4b72-8b34-3b89bcb88b8e",
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   "source": [
    "model = load_model('weight.hdf5')  # Make sure you have the correct path to your saved model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc384b5b-cd84-41bb-af19-ea8342bab269",
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   "source": [
    "### Preprocessing the input image\n",
    "The image is preprocessed for prediction.It loads the image, resizes it, converts to a NumPy array, normalizes pixel values, and adds a batch dimension."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Example usage\n",
    "image_path = \"test_images\\s3.jpg\"  # Replace with the path to any test image\n",
    "img = image.load_img(image_path, target_size=(224, 224))\n",
    "img = image.img_to_array(img)\n",
    "img = img / 255.0\n",
    "img = np.expand_dims(img, axis=0)  # Add batch dimension as the model expects it\n",
    "new_image = img"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae568ecf-2478-4c70-862f-ebc8cfa196a3",
   "metadata": {},
   "source": [
    "Extracts features from the VGG16 base model for the new image and reshapes the features to match the input shape of the student model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# Extract features using the VGG16 base model\n",
    "base_model = VGG16(weights='imagenet', include_top=False)\n",
    "new_image_features = base_model.predict(new_image)\n",
    "\n",
    "# Reshape the features\n",
    "new_image_features = new_image_features.reshape(1, 7*7*512)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a18d43-8070-4680-a1c4-2d45c3dbf5d4",
   "metadata": {},
   "source": [
    "### Making Predictions\n",
    "The model uses the features extracted from VGG16 to make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "decf0e2f-7a31-4b72-823b-87b507f7d947",
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   "outputs": [],
   "source": [
    "# Make predictions\n",
    "predictions = model.predict(new_image_features)\n",
    "\n",
    "# Since your model has 2 output neurons (softmax), you can use argmax to get the predicted class index\n",
    "predicted_class_index = np.argmax(predictions[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1414a950-0e46-48e9-8b76-1a5866359e78",
   "metadata": {},
   "source": [
    "The predicted class index is determined, and the corresponding class label is retrieved using the class_labels dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5318c28-b4ab-444a-a5da-28f81b37e915",
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   "source": [
    "# If your classes are labeled as 0 and 1, you can map the index back to class labels\n",
    "class_labels = {0: 'Healthy Environment', 1: 'Sexual Harassment Detected'}\n",
    "predicted_class_label = class_labels[predicted_class_index]\n",
    "#print(type(predicted_class_label))\n",
    "print(\"Predicted Class Label:\", predicted_class_label)\n"
   ]
  }
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