@@ -10,6 +10,7 @@ from ml_model.services.flux import Flux
 from ml_model.services.fluxproultra import Fluxproultra
 from ml_model.services.fluxlorafast import Fluxlorafast
 from ml_model.services.gemini import Gemini
+from ml_model.services.gptimage import Gptimage
 from ml_model.services.granite import Granite
 from ml_model.services.grok import Grok
 from ml_model.services.iconic import Iconic
@@ -0,0 +1,146 @@
+import base64
+import logging
+import time
+
+from django.core.files import File
+from datetime import timedelta
+from decimal import Decimal
+from io import BytesIO
+
+import httpx
+from backend import settings
+from messages.models import BaseStore, Message
+from ml_model.models import (
+    NeuronModel
+)
+from ml_model.services.base import SimpleService
+from poller.models import Proxy
+
+
+class Gptimage(SimpleService):
+    """
+    GPTImage Service
+    contains abstract method make, which makes a generation
+    """
+
+    TOKENS_COST = {
+        'gpt-image-1': {
+            'input': Decimal('0.0025'),
+            'image': {
+                'input': Decimal('0.005'),
+                'output': Decimal('0.02'),
+                'generation': {
+                    'low': {
+                        '1024x1024': Decimal('5.5'),
+                        '1024x1536': Decimal('8'),
+                        '1536x1024': Decimal('8'),
+                    },
+                    'medium': {
+                        '1024x1024': Decimal('21'),
+                        '1024x1536': Decimal('31.5'),
+                        '1536x1024': Decimal('31.5'),
+                    },
+                    'high': {
+                        '1024x1024': Decimal('83.5'),
+                        '1024x1536': Decimal('125'),
+                        '1536x1024': Decimal('125'),
+                    }
+                }
+            }
+        }
+    }
+
+    def __init__(self, store: BaseStore) -> None:
+        super().__init__(store)
+        self.logger = logging.getLogger(self.__class__.__name__)
+
+    @property
+    def neuron_model(self):
+        return NeuronModel.objects.get(title='GPTImage')
+
+    def make(
+        self,
+        input_message: Message,
+        save: bool = True,
+    ) -> list[Message]:
+        info = input_message.info.copy()
+        version = info.get('version', 'image-generate')
+        files = None
+        for proxy in Proxy.objects.all():
+            json_data = {
+                'prompt': input_message.content,
+                'background': info.get('background'),
+                'model': 'gpt-image-1',
+                'output_format': 'png',
+                'quality': info.get('quality'),
+                'size': info.get('size')
+            }
+            if version == 'image-edit':
+                file_bytes = input_message.file.read()
+                files = {
+                    'image': ('image.png', BytesIO(file_bytes), 'image/png'),
+                }
+            with httpx.Client(
+                base_url='https://api.openai.com/v1',
+                proxy=f'{proxy.protocol}://{proxy.address}',
+                headers={'Authorization': f'Bearer {settings.OPENAI_API_KEY}'},
+                timeout=600,
+            ) as client:
+                start_time = time.time()
+                if version == 'image-generate':
+                    data = client.post(
+                        'images/generations',
+                        json=json_data,
+                    ).json()
+                else:
+                    data = client.post(
+                           'images/edits',
+                        data=json_data,
+                        files=files
+                    ).json()
+                process_time = timedelta(seconds=(time.time() - start_time))
+                image = data['data'][0]['b64_json']
+                text_tokens = data['usage']['input_tokens_details']['text_tokens']
+                image_tokens = data['usage']['input_tokens_details']['image_tokens']
+                output_tokens = data['usage']['output_tokens']
+                self.logger.info(f'Input количество токенов: {text_tokens}; {image_tokens}')
+                self.logger.info(f'Output количество токенов: {output_tokens}')
+        self.handle_invoice(input_message.content_object.model, text_tokens, image_tokens, output_tokens)
+        msgs = self.save_results(input_message.content, image, process_time, save)
+        return msgs
+
+    def calculate_price(
+        self,
+        text_tokens: int,
+        image_tokens: int,
+        output_tokens: int,
+        *args,
+        **kwargs,
+    ) -> Decimal:
+        price = (
+            text_tokens * self.TOKENS_COST['gpt-image-1']['input']
+            + image_tokens * self.TOKENS_COST['gpt-image-1']['image']['input']
+            + output_tokens * self.TOKENS_COST['gpt-image-1']['image']['output']
+        )
+        return price.quantize(Decimal('0.1'), rounding='ROUND_UP')
+
+    def save_results(
+            self,
+            prompt: str,
+            image: bytes,
+            time: timedelta,
+            save: bool = True,
+    ) -> list[Message]:
+        messages: list[Message] = []
+        image_bytes = base64.b64decode(image)
+        messages.append(
+            Message(
+                content_object=self.store,
+                elapsed_time=time,
+                content=prompt,
+                file=File(BytesIO(image_bytes), '.png'),
+            )
+        )
+        if save:
+            return Message.objects.bulk_create(messages)
+        return messages