Hugging Face's logo

Spaces:
GiriAman
/
emotion-detector
Sleeping

App Files Community
emotion-detector
File size: 24,868 Bytes 
c60e678
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
 
{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "dOa3uWxasGop",
        "outputId": "416c257f-8864-4f74-bcd8-94d399ab30c2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Emotion: Sad, Confidence: 43.33000183105469%\n"
          ]
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.preprocessing import image\n",
        "import numpy as np\n",
        "\n",
        "from tensorflow.keras.models import load_model\n",
        "model = load_model('G:/aman office/practise/face_emotion_detection/face_emotion_detection.h5')\n",
        "\n",
        "def detect_emotion(image_path):\n",
        "    img = image.load_img(image_path, target_size=(48, 48), color_mode='grayscale')\n",
        "    img_array = image.img_to_array(img) / 255.0\n",
        "    img_array = np.expand_dims(img_array, axis=0)\n",
        "\n",
        "    prediction = model.predict(img_array)\n",
        "    predicted_index = np.argmax(prediction)\n",
        "    predicted_class = class_names[predicted_index]\n",
        "    confidence = round(prediction[0][predicted_index] * 100, 2)  # Confidence in %\n",
        "\n",
        "    # Display the image with the predicted label and confidence\n",
        "    plt.imshow(img, cmap='gray')\n",
        "    plt.title(f'Predicted Emotion: {predicted_class} ({confidence}%)')\n",
        "    plt.axis('off')\n",
        "    plt.show()\n",
        "\n",
        "    return predicted_class, confidence\n",
        "\n",
        "# Example Detection\n",
        "predicted_emotion, confidence = detect_emotion('G:/aman office/practise/face_emotion_detection/test/sad/PrivateTest_366361.jpg')\n",
        "print(f\"Emotion: {predicted_emotion}, Confidence: {confidence}%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FbTGim8b3Vk5",
        "outputId": "b2d93f75-548e-40ac-adbd-091f501511a4"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        }
      ],
      "source": [
        "# Save in HDF5 format (.h5)\n",
        "model.save(\"best_model.h5\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tXYpfd4J3WNy"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}