Add basic support for Dall-E image generation and Ollama image recogn…

…ition

app/domain/__init__.py

@@ -1 +1,2 @@
+from domain.image import IrisImage
 from domain.message import IrisMessage, IrisMessageRole

app/domain/image.py

@@ -0,0 +1,23 @@
+from datetime import datetime
+
+
+class IrisImage:
+    prompt: str
+    base64: str
+    url: str | None
+    timestamp: datetime
+    _raw_data: any
+
+    def __init__(
+        self,
+        prompt: str,
+        base64: str,
+        url: str,
+        timestamp: datetime,
+        raw_data: any = None,
+    ):
+        self.prompt = prompt
+        self.base64 = base64
+        self.url = url
+        self.timestamp = timestamp
+        self._raw_data = raw_data

app/domain/message.py

@@ -1,5 +1,7 @@
 from enum import Enum
 
+from domain import IrisImage
+
 
 class IrisMessageRole(Enum):
     USER = "user"
@@ -10,10 +12,14 @@ class IrisMessageRole(Enum):
 class IrisMessage:
     role: IrisMessageRole
     text: str
+    images: list[IrisImage] | None
 
-    def __init__(self, role: IrisMessageRole, text: str):
+    def __init__(
+        self, role: IrisMessageRole, text: str, images: list[IrisImage] | None = None
+    ):
         self.role = role
         self.text = text
+        self.images = images
 
     def __str__(self):
         return f"IrisMessage(role={self.role.value}, text='{self.text}')"

app/llm/llm_manager.py

@@ -5,7 +5,7 @@
 import yaml
 
 from common import Singleton
-from llm.wrapper import AbstractLlmWrapper, LlmWrapper
+from llm.wrapper import LlmWrapper
 
 
 # Small workaround to get pydantic discriminators working
@@ -14,7 +14,7 @@ class LlmList(BaseModel):
 
 
 class LlmManager(metaclass=Singleton):
-    entries: list[AbstractLlmWrapper]
+    entries: list[LlmWrapper]
 
     def __init__(self):
         self.entries = []

app/llm/wrapper/__init__.py

@@ -1,4 +1,3 @@
-from llm.wrapper.abstract_llm_wrapper import AbstractLlmWrapper
 from llm.wrapper.open_ai_completion_wrapper import (
     OpenAICompletionWrapper,
     AzureCompletionWrapper,
@@ -7,6 +6,7 @@
     OpenAIChatCompletionWrapper,
     AzureChatCompletionWrapper,
 )
+from llm.wrapper.open_ai_dalle_wrapper import OpenAIDalleWrapper
 from llm.wrapper.open_ai_embedding_wrapper import (
     OpenAIEmbeddingWrapper,
     AzureEmbeddingWrapper,
@@ -21,4 +21,5 @@
     | OpenAIEmbeddingWrapper
     | AzureEmbeddingWrapper
     | OllamaWrapper
+    | OpenAIDalleWrapper
 )

app/llm/wrapper/abstract_llm_wrapper.py

@@ -1,7 +1,7 @@
 from abc import ABCMeta, abstractmethod
 from pydantic import BaseModel
 
-from domain import IrisMessage
+from domain import IrisMessage, IrisImage
 from llm import CompletionArguments
 
 
@@ -56,3 +56,18 @@ def __subclasshook__(cls, subclass):
     def create_embedding(self, text: str) -> list[float]:
         """Create an embedding from the text"""
         raise NotImplementedError
+
+
+class AbstractLlmImageGenerationWrapper(AbstractLlmWrapper, metaclass=ABCMeta):
+    """Abstract class for the llm image generation wrappers"""
+
+    @classmethod
+    def __subclasshook__(cls, subclass):
+        return hasattr(subclass, "generate_images") and callable(
+            subclass.generate_images
+        )
+
+    @abstractmethod
+    def generate_images(self, prompt: str, n: int, **kwargs) -> list[IrisImage]:
+        """Generate images from the prompt"""
+        raise NotImplementedError

app/llm/wrapper/ollama_wrapper.py

@@ -1,8 +1,9 @@
+import base64
 from typing import Literal, Any
 
 from ollama import Client, Message
 
-from domain import IrisMessage, IrisMessageRole
+from domain import IrisMessage, IrisMessageRole, IrisImage
 from llm import CompletionArguments
 from llm.wrapper.abstract_llm_wrapper import (
     AbstractLlmChatCompletionWrapper,
@@ -11,9 +12,20 @@
 )
 
 
+def convert_to_ollama_images(images: list[IrisImage]) -> list[bytes] | None:
+    if not images:
+        return None
+    return [base64.b64decode(image.base64) for image in images]
+
+
 def convert_to_ollama_messages(messages: list[IrisMessage]) -> list[Message]:
     return [
-        Message(role=message.role.value, content=message.text) for message in messages
+        Message(
+            role=message.role.value,
+            content=message.text,
+            images=convert_to_ollama_images(message.images),
+        )
+        for message in messages
     ]
 
 
@@ -34,8 +46,12 @@ class OllamaWrapper(
     def model_post_init(self, __context: Any) -> None:
         self._client = Client(host=self.host)  # TODO: Add authentication (httpx auth?)
 
-    def completion(self, prompt: str, arguments: CompletionArguments) -> str:
-        response = self._client.generate(model=self.model, prompt=prompt)
+    def completion(
+        self, prompt: str, arguments: CompletionArguments, images: [IrisImage] = None
+    ) -> str:
+        response = self._client.generate(
+            model=self.model, prompt=prompt, images=convert_to_ollama_images(images)
+        )
         return response["response"]
 
     def chat_completion(

app/llm/wrapper/open_ai_dalle_wrapper.py

@@ -0,0 +1,61 @@
+import base64
+from datetime import datetime
+from typing import Literal, Any
+
+import requests
+from openai import OpenAI
+
+from domain import IrisImage
+from llm.wrapper.abstract_llm_wrapper import AbstractLlmWrapper
+
+
+class OpenAIDalleWrapper(AbstractLlmWrapper):
+    type: Literal["openai_dalle"]
+    model: str
+    _client: OpenAI
+
+    def model_post_init(self, __context: Any) -> None:
+        self._client = OpenAI(api_key=self.api_key)
+
+    def generate_images(
+        self,
+        prompt: str,
+        n: int = 1,
+        size: Literal[
+            "256x256", "512x512", "1024x1024", "1792x1024", "1024x1792"
+        ] = "256x256",
+        quality: Literal["standard", "hd"] = "standard",
+        **kwargs
+    ) -> [IrisImage]:
+        response = self._client.images.generate(
+            model=self.model,
+            prompt=prompt,
+            size=size,
+            quality=quality,
+            n=n,
+            response_format="url",
+            **kwargs
+        )
+
+        images = response.data
+        iris_images = []
+        for image in images:
+            if image.revised_prompt is None:
+                image.revised_prompt = prompt
+            if image.b64_json is None:
+                image_response = requests.get(image.url)
+                image.b64_json = base64.b64encode(image_response.content).decode(
+                    "utf-8"
+                )
+
+            iris_images.append(
+                IrisImage(
+                    prompt=image.revised_prompt,
+                    base64=image.b64_json,
+                    url=image.url,
+                    timestamp=datetime.fromtimestamp(response.created),
+                    raw_data=image,
+                )
+            )
+
+        return iris_images

requirements.txt

@@ -6,3 +6,4 @@ black==24.1.1
 flake8==7.0.0
 pre-commit==3.6.0
 pydantic==2.6.1
+requests==2.31.0