{
 "cells": [
  {
   "cell_type": "code",
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    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'cv2'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 4\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m accuracy_score\n\u001b[1;32m----> 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcv2\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Sequential\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'cv2'"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "import cv2\n",
    "import numpy as np\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# Define the emotions\n",
    "emotions = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']\n",
    "\n",
    "# Load the dataset\n",
    "train_dir = 'path_to_your_dataset/train'\n",
    "test_dir = 'path_to_your_dataset/test'\n",
    "\n",
    "# Define the data generator\n",
    "train_datagen = ImageDataGenerator(rescale=1./255)\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        train_dir,\n",
    "        target_size=(48, 48),\n",
    "        batch_size=32,\n",
    "        class_mode='categorical')\n",
    "\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "        test_dir,\n",
    "        target_size=(48, 48),\n",
    "        batch_size=32,\n",
    "        class_mode='categorical')\n",
    "\n",
    "# Define the CNN model\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(48, 48, 3)))\n",
    "model.add(Conv2D(32, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(7, activation='softmax'))\n",
    "\n",
    "# Compile the model\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "model.fit(train_generator, epochs=10)\n",
    "\n",
    "# Define the SVM model\n",
    "svm_model = svm.SVC(kernel='rbf', C=1)\n",
    "\n",
    "# Extract features from the CNN model\n",
    "cnn_features = model.layers[-2].output\n",
    "\n",
    "# Train the SVM model\n",
    "svm_model.fit(cnn_features, train_generator.classes)\n",
    "\n",
    "# Define a function to predict the emotion\n",
    "def predict_emotion(face):\n",
    "    face = cv2.resize(face, (48, 48))\n",
    "    face = face.reshape(1, 48, 48, 3)\n",
    "    face = face / 255.0\n",
    "    features = model.predict(face)\n",
    "    emotion = svm_model.predict(features)\n",
    "    return emotions[emotion[0]]"
   ]
  }
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