Delete app

app/.gitignore

@@ -1,18 +0,0 @@
-# Игнорировать папку media
-media/
-
-# Игнорировать файл базы данных SQLite
-db.sqlite3
-
-# Другие стандартные игнорируемые файлы для Django
-*.pyc
-__pycache__/
-*.sqlite3
-*.log
-.env
-.venv/
-venv/
-env/
-.DS_Store
-.idea/
-.vscode/

app/Dockerfile

@@ -1,24 +0,0 @@
-# syntax=docker/dockerfile:1.4
-
-FROM --platform=$BUILDPLATFORM python:3.10-alpine AS builder
-EXPOSE 8000
-WORKDIR /app 
-COPY requirements.txt /app
-RUN pip3 install -r requirements.txt --no-cache-dir
-COPY . /app 
-ENTRYPOINT ["python3"] 
-CMD ["manage.py", "runserver", "0.0.0.0:8000"]
-
-FROM builder as dev-envs
-RUN <<EOF
-apk update
-apk add git
-EOF
-
-RUN <<EOF
-addgroup -S docker
-adduser -S --shell /bin/bash --ingroup docker vscode
-EOF
-# install Docker tools (cli, buildx, compose)
-COPY --from=gloursdocker/docker / /
-CMD ["manage.py", "runserver", "0.0.0.0:8000"]

app/apps/emotion_recognition/admin.py

@@ -1,6 +0,0 @@
-from django.contrib import admin
-from .models import UserImageRecognition
-# Register your models here.
-
-admin.site.register(UserImageRecognition)
-

app/apps/emotion_recognition/apps.py

@@ -1,6 +0,0 @@
-from django.apps import AppConfig
-
-
-class EmotionRecognitionConfig(AppConfig):
-    default_auto_field = 'django.db.models.BigAutoField'
-    name = 'apps.emotion_recognition'

app/apps/emotion_recognition/emotion_recognition.py

@@ -1,203 +0,0 @@
-
-from tensorflow import keras
-from keras.models import Sequential
-from keras.models import load_model
-from keras.models import model_from_json
-from keras.utils import img_to_array
-import keras.utils as image 
-
-import cv2
-import numpy as np
-import os
-
-from django_app.settings import BASE_DIR
-
-
-model = Sequential()
-
-model = model_from_json(open(
-    os.path.join(BASE_DIR,'model/model_4layer_2_2_pool.json'), "r").read())
-
-model.load_weights(os.path.join(
-			BASE_DIR,'model/model_4layer_2_2_pool.h5'))
-
-class_labels = {0: 'Angry', 1: 'Disgust', 2: 'Fear',
-                3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}
-classes = list(class_labels.values())
-
-face_classifier = cv2.CascadeClassifier(os.path.join(
-			BASE_DIR,'model/haarcascade_frontalface.xml'))
-
-camera = cv2.VideoCapture(0)
-
-
-def text_on_detected_boxes(text, text_x, text_y, image, font_scale=1,
-                           font=cv2.FONT_HERSHEY_SIMPLEX,
-                           FONT_COLOR=(0, 0, 0),
-                           FONT_THICKNESS=2,
-                           rectangle_bgr=(0, 255, 0)):
-    (text_width, text_height) = cv2.getTextSize(
-        text, font, fontScale=font_scale, thickness=2)[0]
-    box_coords = ((text_x-10, text_y+4), (text_x +
-                  text_width+10, text_y - text_height-5))
-    cv2.rectangle(image, box_coords[0],
-                  box_coords[1], rectangle_bgr, cv2.FILLED)
-    cv2.putText(image, text, (text_x, text_y), font,
-                fontScale=font_scale, color=FONT_COLOR, thickness=FONT_THICKNESS)
-
-
-def face_detector_image(img):
-    """
-    Обнаружение лиц на изображении.
-
-    Args:
-        img (numpy array): Исходное изображение.
-
-    Returns:
-        tuple: (rects, allfaces, img) - координаты лиц, обрезанные лица и изображение с рамками.
-    """
-    gray = cv2.cvtColor(img.copy(), cv2.COLOR_BGR2GRAY)
-    faces = face_classifier.detectMultiScale(gray, 1.3, 5)
-    if faces == ():
-        return (0, 0, 0, 0), np.zeros((48, 48), np.uint8), img
-    allfaces = []
-    rects = []
-    for (x, y, w, h) in faces:
-        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2) 
-        roi_gray = gray[y:y + h, x:x + w]
-        roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)
-        allfaces.append(roi_gray)
-        rects.append((x, w, y, h))
-    return rects, allfaces, img 
-
-
-def emotionImage(imgPath):
-    img = cv2.imread(BASE_DIR + '\\media\\' + imgPath)
-    rects, faces, image = face_detector_image(img)
-    i = 0
-    for face in faces:
-        roi = face.astype("float") / 255.0
-        roi = img_to_array(roi)
-        roi = np.expand_dims(roi, axis=0)
-        preds = model.predict(roi)[0]
-        label = class_labels[preds.argmax()]
-        label_position = (
-            rects[i][0] + int((rects[i][1] / 2)), abs(rects[i][2] - 10))
-        i += 1
-
-        # Отрисовка текста и рамок
-        text_on_detected_boxes(
-            label, label_position[0], label_position[1], image)
-
-        precentages = dict(zip(classes, preds*100))
-
-    return image, precentages, label
-
-
-def emotionImageFromArray(img_array):
-    """
-    Обрабатывает изображение и возвращает результат обработки.
-
-    Args:
-        img_array (numpy array): Исходное изображение (numpy array).
-
-    Returns:
-        tuple: (image, precentages, label)
-            - image: Изображение с рамками и текстом эмоций.
-            - precentages: Вероятности каждой эмоции.
-            - label: Определенная эмоция.
-    """
-    rects, faces, image = face_detector_image(img_array)
-    i = 0
-    for face in faces:
-        roi = face.astype("float") / 255.0
-        roi = img_to_array(roi)
-        roi = np.expand_dims(roi, axis=0)
-        preds = model.predict(roi)[0]
-        label = class_labels[preds.argmax()]
-        label_position = (
-            rects[i][0] + int((rects[i][1] / 2)), abs(rects[i][2] - 10))
-        i += 1
-
-        # Отрисовка текста и рамок
-        text_on_detected_boxes(
-            label, label_position[0], label_position[1], image)
-
-        precentages = dict(zip(classes, preds*100))
-
-    return image, precentages, label
-
-def face_detector_video(img):
-    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    faces = face_classifier.detectMultiScale(gray, 1.3, 5)
-    if faces is ():
-        return (0, 0, 0, 0), np.zeros((48, 48), np.uint8), img
-    for (x, y, w, h) in faces:
-        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), thickness=2)
-        roi_gray = gray[y:y + h, x:x + w]
-    roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)
-    return (x, w, y, h), roi_gray, img
-
-
-def emotionVideo():
-    while True:
-        ret, frame = camera.read()
-        rect, face, image = face_detector_video(frame)
-        if np.sum([face]) != 0.0:
-            roi = face.astype("float") / 255.0
-            roi = img_to_array(roi)
-            roi = np.expand_dims(roi, axis=0)
-            preds = model.predict(roi)[0]
-            label = class_labels[preds.argmax()]
-            label_position = (rect[0] + rect[1]//50, rect[2] + rect[3]//50)
-            text_on_detected_boxes(label, label_position[0], label_position[1], image) 
-            fps = camera.get(cv2.CAP_PROP_FPS)
-            cv2.putText(image, str(fps),(5, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-        else:
-            cv2.putText(image, "No Face Found", (5, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
-
-        ret, buffer = cv2.imencode('.jpg', image)
-        
-        frame = buffer.tobytes()
-        yield (b'--frame\r\n'
-            b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')  
-
-
-
-def gen_frames():                                     
-    while True:
-        success, frame = camera.read()
-        if not success:
-            cv2.putText(image, "No Face Found", (5, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
-            break
-        else:
-            gray_img= cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  
-        
-            faces_detected = face_classifier.detectMultiScale(gray_img, 1.32, 5)  
-            
-        
-            for (x,y,w,h) in faces_detected:
-                
-                cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),thickness=7)  
-                roi_gray=gray_img[y:y+w,x:x+h]          
-                roi_gray=cv2.resize(roi_gray,(48,48))  
-                img_pixels = image.img_to_array(roi_gray)  
-                img_pixels = np.expand_dims(img_pixels, axis = 0)  
-                img_pixels /= 255  
-        
-                predictions = model.predict(img_pixels)  
-        
-                max_index = np.argmax(predictions[0])   
-        
-                emotions = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']  
-                predicted_emotion = emotions[max_index]  
-                
-                cv2.putText(frame, predicted_emotion, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)  
-        
-            resized_img = cv2.resize(frame, (600, 400))  
-            
-            ret, buffer = cv2.imencode('.jpg', frame)
-            
-            frame = buffer.tobytes()
-            yield (b'--frame\r\n'
-                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')  

app/apps/emotion_recognition/forms.py

@@ -1,9 +0,0 @@
-from django import forms
-
-from .models import UserImageRecognition
-
-
-class RecognitionEditForm(forms.ModelForm):
-    class Meta:
-        model = UserImageRecognition
-        fields = ('__all__')

app/apps/emotion_recognition/migrations/0001_initial.py

@@ -1,29 +0,0 @@
-# Generated by Django 4.1.3 on 2025-01-12 20:01
-
-from django.db import migrations, models
-
-
-class Migration(migrations.Migration):
-
-    initial = True
-
-    dependencies = [
-    ]
-
-    operations = [
-        migrations.CreateModel(
-            name='UserImageRecognition',
-            fields=[
-                ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
-                ('uploaded_image', models.ImageField(upload_to='')),
-                ('final_image', models.ImageField(blank=True, null=True, upload_to='')),
-                ('recognized_emotion', models.CharField(blank=True, max_length=20, null=True)),
-                ('predicted_emotions', models.CharField(blank=True, max_length=155, null=True)),
-                ('created_at', models.DateTimeField(auto_now_add=True)),
-                ('status', models.CharField(choices=[('PEN', 'Pending'), ('COM', 'Complete'), ('ERR', 'Error')], default='PEN', max_length=3)),
-            ],
-            options={
-                'ordering': ['-created_at'],
-            },
-        ),
-    ]

app/apps/emotion_recognition/models.py

@@ -1,34 +0,0 @@
-from django.db import models
-from PIL import Image
-
-
-# Create your models here.
-class UserImageRecognition(models.Model):
-    uploaded_image = models.ImageField(null=False, blank=False)
-    final_image = models.ImageField(null=True, blank=True)
-    recognized_emotion = models.CharField(max_length=20,null=True, blank=True)
-    predicted_emotions = models.CharField(max_length=155,null=True,blank=True)
-    created_at = models.DateTimeField(
-        auto_now_add=True, null=False, blank=False)
-
-    STATUS_CHOICES = (
-        ('PEN', 'Pending'),
-        ('COM', 'Complete'),
-        ('ERR', 'Error'),
-    )
-    status = models.CharField(
-        max_length=3, choices=STATUS_CHOICES, null=False, blank=False, default='PEN')
-
-    class Meta:
-        ordering = ['-created_at']
-
-    def save(self, *args, **kwargs):
-        super().save(*args, **kwargs)
-        uploaded_img = Image.open(self.uploaded_image.path)
-        if uploaded_img.height > 400 or uploaded_img.width > 400:
-            output_size = (400, 400)
-            uploaded_img.thumbnail(output_size)
-            uploaded_img.save(self.uploaded_image.path)
-            
-    def __str__(self):
-        return f'{self.user} - {self.uploaded_image}'

app/apps/emotion_recognition/serializers.py

@@ -1,4 +0,0 @@
-from rest_framework import serializers
-
-class ImageSerializer(serializers.Serializer):
-    image = serializers.ImageField(required=True)

app/apps/emotion_recognition/tasks.py

@@ -1,62 +0,0 @@
-from django.shortcuts import get_object_or_404
-from django.core.files.base import ContentFile
-
-from .models import UserImageRecognition
-from .emotion_recognition import emotionImage, emotionImageFromArray
-
-from io import BytesIO
-import cv2
-import numpy as np
-from PIL import Image
-
-
-def proccess_uploaded_image(image_data_id):
-    image_data = None
-    image_data = get_object_or_404(UserImageRecognition, pk=image_data_id)
-
-    final_image, predicted_emotions, recognized_emotion = emotionImage(
-        image_data.uploaded_image.name)
-    final_image = converter_to_django_file(final_image)
-    
-    image_data.final_image = final_image
-    image_data.predicted_emotions = predicted_emotions
-    image_data.recognized_emotion = recognized_emotion
-    image_data.status = "COM"
-    image_data.save()
-
-def process_image_from_api(image_file):
-    """
-    Обрабатывает изображение, переданное через API, и возвращает финальное изображение и данные эмоций.
-
-    Args:
-        image_file (InMemoryUploadedFile): Исходное изображение, полученное через API.
-
-    Returns:
-        tuple: (final_image, predicted_emotions, recognized_emotion)
-            - final_image: обработанное изображение в формате numpy array.
-            - predicted_emotions: словарь с предсказанными эмоциями и их вероятностями.
-            - recognized_emotion: самая вероятная эмоция.
-    """
-    # Конвертируем загруженный файл в OpenCV-совместимый формат
-    file_bytes = np.frombuffer(image_file.read(), np.uint8)
-    image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
-
-    # Обрабатываем изображение с помощью emotionImageFromArray
-    final_image, predicted_emotions, recognized_emotion = emotionImageFromArray(image)
-
-    return final_image, predicted_emotions, recognized_emotion
-
-def converter_to_django_file(image):
-    img_io = BytesIO()
-    image = Image.fromarray(image)
-    image.save(img_io, format='JPEG',  quality=100)
-    img_content = ContentFile(img_io.getvalue(), 'final_image.jpg')
-    
-    return img_content
-
-def convert_image_to_bytes(image):
-    # Конвертируем обработанное изображение в байты
-    _, buffer = cv2.imencode('.jpg', image)
-    output_image = BytesIO(buffer)
-    
-    return output_image

app/apps/emotion_recognition/templates/recognition/index.html

@@ -1,74 +0,0 @@
-{%  extends 'base.html' %}
-{% load static %}
-{% load crispy_forms_tags %}
-{% load crispy_forms_tags humanize %}
-
-{% block content %}
-
-<div class="container d-flex flex-wrap flex-column justify-center">
-
-    <form action="" method="POST" class='col-4'enctype="multipart/form-data">
-        {% csrf_token %}
-            <label for="formFile" class="form-label">Выберите изображение для определения эмоции</label>
-            <input  id='formFile' name="uploaded_file" class="form-control" type="file" accept="image/*" placeholder="Выберите изображение" required multiple>
-            <button type="submit" class="btn btn-primary mt-3">Определить</button>
-    </form>
-    <div class="d-flex flex-column flex-wrap align-content-center">
-    </div>
-    <table class="table table-hover mt-5">
-        <thead>
-            <tr class="table-secondary">
-              <th scope="col">Оригинальное Изображение</th>
-              <th scope="col">Результат</th>
-              <th scope="col">Эмоция</th>
-              <th scope="col">Статуc</th>
-              <th scope="col">Дата</th>
-              <th scope="col"></th>
-            </tr>
-          </thead>
-          <tbody>
-            {% for sample in samples%}
-                <td>
-                    <img src="{{sample.uploaded_image.url}}" alt="" style="width: 250px">
-                </td>
-                <td>
-                    {% if sample.final_image %}
-                        <img src="{{sample.final_image.url}}" alt="" style="width: 250px">
-                    {% else %}
-                        <p>Произошла ошибка</p>
-                    {% endif %}
-                </td>
-                <td>
-                    {{sample.recognized_emotion}}
-                </td>
-                <td class="text-center">
-                    {% if sample.status == "COM" %}
-                    <svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" fill="green" class="bi bi-check-circle" viewBox="0 0 16 16">
-                        <path d="M8 15A7 7 0 1 1 8 1a7 7 0 0 1 0 14zm0 1A8 8 0 1 0 8 0a8 8 0 0 0 0 16z"/>
-                        <path d="M10.97 4.97a.235.235 0 0 0-.02.022L7.477 9.417 5.384 7.323a.75.75 0 0 0-1.06 1.06L6.97 11.03a.75.75 0 0 0 1.079-.02l3.992-4.99a.75.75 0 0 0-1.071-1.05z"/>
-                      </svg>
-                    {%elif sample.status == "PEN"%}
-                    <svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" fill="orange" class="bi bi-arrow-clockwise" viewBox="0 0 16 16">
-                        <path fill-rule="evenodd" d="M8 3a5 5 0 1 0 4.546 2.914.5.5 0 0 1 .908-.417A6 6 0 1 1 8 2v1z"/>
-                        <path d="M8 4.466V.534a.25.25 0 0 1 .41-.192l2.36 1.966c.12.1.12.284 0 .384L8.41 4.658A.25.25 0 0 1 8 4.466z"/>
-                      </svg>
-                    {%else%}
-                    <svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" fill="red" class="bi bi-x-circle" viewBox="0 0 16 16">
-                        <path d="M8 15A7 7 0 1 1 8 1a7 7 0 0 1 0 14zm0 1A8 8 0 1 0 8 0a8 8 0 0 0 0 16z"/>
-                        <path d="M4.646 4.646a.5.5 0 0 1 .708 0L8 7.293l2.646-2.647a.5.5 0 0 1 .708.708L8.707 8l2.647 2.646a.5.5 0 0 1-.708.708L8 8.707l-2.646 2.647a.5.5 0 0 1-.708-.708L7.293 8 4.646 5.354a.5.5 0 0 1 0-.708z"/>
-                      </svg>
-                    {%endif%}
-                </td>
-                <td>
-                    {{sample.created_at|naturaltime}}
-                </td>
-                <td>
-                    <a href="{% url "recognition:recognition_edit" sample.id %}" class="btn btn-outline-dark">Редактировать</a>
-                </td>
-            </tr>
-            {%endfor%}
-          </tbody>
-    </table>
-</div>
-
-{%endblock%}

app/apps/emotion_recognition/templates/recognition/real_time.html

@@ -1,33 +0,0 @@
-{%  extends 'base.html' %}
-{% load static %}
-{% load crispy_forms_tags %}
-{% load crispy_forms_tags humanize %}
-
-{% block content %}
-
-<div class="container d-flex flex-wrap flex-column justify-content-center align-content-center">
-<!-- 
-    <form action="" method="POST" class='col-4'enctype="multipart/form-data">
-        {% csrf_token %}
-            <button type="submit" class="btn btn-primary mt-3">Включить камеру</button>
-    </form> -->
-
-    <a class="btn btn-primary w-75 " data-bs-toggle="collapse" href="#collapseCamera" role="button" aria-expanded="false" aria-controls="collapseCamera">
-        Включить/Выключить камеру
-      </a>
-
-    <div class="collapse mt-3" id="collapseCamera">
-        <div class="card card-body">
-            <div class="container">
-                <div class="row">
-                    <div class="col-lg-8  offset-lg-2">
-                        <img src="{% url 'recognition:real_time_video_stream' %}" width="100%">
-                    </div>
-                </div>
-            </div>
-        </div>
-      </div>
-
-</div>
-
-{%endblock%}

app/apps/emotion_recognition/templates/recognition/recognition_delete.html

@@ -1,28 +0,0 @@
-{% extends 'base.html' %}
-{% load static %}
-{% load crispy_forms_tags %}
-{% load crispy_forms_tags humanize %}
-
-
-{%block content %}
-
-    <div class="pt-3 ps-3 bg-body-tertiary scrollspy-example overflow-auto min-vh-100" data-bs-spy="scroll" data-bs-smooth-scroll="true">
-        
-        <div class="container">
-            <div class="d-flex flex-column align-items-center justify-content-between mt">
-                <h2 class="mb-3">Подтверждение Удаления</h2>
-                <p class="lead">Вы уверены, что хотите удалить запись<strong>"{{ object}}"?</strong>
-                    <br>
-                    <u class="fw-bold">Восстановить будет невозможно.</u>
-                </p>
-                <form method="post">
-                    {% csrf_token %}
-                    {{form}}
-                    <button type="submit" class="btn btn-danger btn-lg">Подтверждаю</button>
-                    <a href="javascript:history.back()" class="btn btn-outline-secondary btn-lg" role="button">Вернуться Назад</a>
-                  </form>
-            </div>
-        </div>
-    </div>
-
-{%endblock%}

app/apps/emotion_recognition/templates/recognition/recognition_edit.html

@@ -1,32 +0,0 @@
-{% extends 'base.html' %}
-{% load static %}
-{% load crispy_forms_tags humanize %}
-
-{%block content %}
-
-<div class="container">
-    <div class="pt-3 ps-3 bg-body-tertiary scrollspy-example overflow-auto min-vh-100" data-bs-spy="scroll" data-bs-smooth-scroll="true">
-        <form method="POST" enctype="multipart/form-data">
-                {% csrf_token %}
-                {{form.media}}
-                <fieldset class="form-group">
-                    <legend class="border-bottom mb-4"></legend>
-                    {{ form|crispy }}
-                </fieldset>
-                <div class="form-group" style="display: flex; flex-direction: column; align-items: center;">
-                    <button class="w-75 btn btn-lg btn-dark" type="submit">Применить Изменения</button>
-                </div>
-                
-                <div class="form-group" style="display: flex; flex-direction: column; align-items: center;">
-                    <a href="{% url 'recognition:recognition_delete' userimagerecognition.id%}" class="mt-3 w-75 btn btn-lg btn-danger " role="button">
-                        Удалить Запись
-                    </a>
-                    <a href="{% url 'recognition:index'%}" class="mt-3 w-75 btn btn-lg btn-outline-secondary " role="button">
-                        Вернуться Назад
-                    </a>
-                </div>
-        </form>
-    </div>
-</div>
-
-{%endblock%}

app/apps/emotion_recognition/tests.py

@@ -1,3 +0,0 @@
-from django.test import TestCase
-
-# Create your tests here.

app/apps/emotion_recognition/urls.py

@@ -1,13 +0,0 @@
-from django.urls import path
-from . import views
-
-
-app_name = 'recognition'
-urlpatterns = [
-    path('recognition/', views.index, name="index"),
-    path('api/emotion-recognition/', views.ImageProcessingView.as_view(), name='emotion_recognition'),
-    path('recognition/real-time', views.real_time_recognition, name="real_time"),
-    path('real-time-stream', views.real_time_stream, name="real_time_video_stream"),
-    path('recognition/edit/<pk>', views.RecognitionUpdateView.as_view(), name="recognition_edit"),
-    path('recognition/delete/<pk>', views.RecognitionDeleteView.as_view(), name="recognition_delete"),
-]

app/apps/emotion_recognition/views.py

@@ -1,119 +0,0 @@
-from django.shortcuts import render, redirect
-from django.views.decorators.http import require_http_methods
-from django.http import HttpResponse, StreamingHttpResponse
-from django.contrib.auth.mixins import LoginRequiredMixin
-from django.views.generic import UpdateView, DeleteView
-from django.contrib import messages
-from django.urls import reverse_lazy
-
-from rest_framework.views import APIView
-from rest_framework.response import Response
-from rest_framework import status
-from rest_framework.parsers import MultiPartParser, FormParser
-from .serializers import ImageSerializer
-from PIL import Image
-
-from .models import UserImageRecognition
-from .emotion_recognition import  emotionVideo
-from .tasks import proccess_uploaded_image, process_image_from_api, convert_image_to_bytes
-from .forms import RecognitionEditForm
-
-
-
-@require_http_methods(['GET', 'POST'])
-def index(request):
-    try:
-
-        # GET method, return HTML page
-        if request.method == 'GET':
-            samples = UserImageRecognition.objects.all()
-            return render(request, 'recognition/index.html', {'samples': samples, })
-
-        if request.FILES and request.method == 'POST':
-            for f in request.FILES.getlist('uploaded_file'):
-                uploaded_image = f
-                image_data = UserImageRecognition.objects.create(uploaded_image=uploaded_image)
-
-                proccess_uploaded_image(image_data.id)
-
-        return redirect('recognition:index')
-
-    except Exception as e:
-
-        image_data.status = 'ERR'
-        image_data.error_occurred = True
-        image_data.error_message = str(e)
-        image_data.save()
-
-        return HttpResponse(f'Error: {str(e)}')
-    
-
-
-@require_http_methods(['GET', 'POST'])
-def real_time_recognition(request):
-    return render(request, 'recognition/real_time.html')
-
-def real_time_stream(request):
-    return StreamingHttpResponse(emotionVideo(),content_type="multipart/x-mixed-replace;boundary=frame")
-
-
-class RecognitionUpdateView(LoginRequiredMixin, UpdateView):
-    model = UserImageRecognition
-    form_class = RecognitionEditForm
-    template_name = "recognition/recognition_edit.html"
-
-    def get(self, request, pk):
-        self.object = self.get_object()
-        
-        context = self.get_context_data(object=self.object)
-        return self.render_to_response(context)
-    
-    def get_success_url(self, **kwargs):
-        pk = self.object.pk
-        messages.success(self.request, 'Запись была успешно изменена!')         
-        return reverse_lazy('recognition:recognition_edit', args=(pk,))
-
-
-class RecognitionDeleteView(LoginRequiredMixin, DeleteView):
-    model = UserImageRecognition
-    template_name = "recognition/recognition_delete.html"
-
-    def delete(self, request, pk):
-        return super().delete(request, pk)
-
-    def get_success_url(self, **kwargs):
-        obj = self.get_object()
-        messages.success(self.request, 'Запись  была успешно удалёна!')         
-        return reverse_lazy('recognition:index')
-    
-    
-class ImageProcessingView(APIView):
-    parser_classes = (MultiPartParser, FormParser)  # Для обработки загруженных файлов
-    serializer_class = ImageSerializer
-
-    def post(self, request, format=None):
-        """
-        Обрабатывает изображение, переданное через API, и возвращает финальное изображение с эмоциями.
-        """
-        serializer = self.serializer_class(data=request.data)
-        if serializer.is_valid():
-            image_file = serializer.validated_data['image']
-            
-            try:
-                # Обработка изображения
-                final_image, predicted_emotions, recognized_emotion = process_image_from_api(image_file)
-                output_image = convert_image_to_bytes(final_image)
-
-                # Формируем ответ
-                response_data = {
-                    "predicted_emotions": predicted_emotions,
-                    "recognized_emotion": recognized_emotion,
-                }
-                response = Response(response_data, status=status.HTTP_200_OK)
-                response['Content-Type'] = 'image/jpeg'
-                response.content = output_image.getvalue()
-                return response
-            except Exception as e:
-                return Response({"error": str(e)}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
-
-        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)