Merge pull request #99 from atgehrhardt/main

Ollama Dynamic Vision Pipeline

examples/filters/dynamic_ollama_vision_filter_pipeline.py

@@ -0,0 +1,91 @@
+"""
+title: Ollama Dynamic Vision Pipeline
+author: Andrew Tait Gehrhardt
+date: 2024-06-18
+version: 1.0
+license: MIT
+description: A pipeline for dynamically processing images when current model is a text only model
+requirements: pydantic, aiohttp
+"""
+
+from typing import List, Optional
+from pydantic import BaseModel
+import json
+import aiohttp
+from utils.pipelines.main import get_last_user_message
+
+class Pipeline:
+    class Valves(BaseModel):
+        pipelines: List[str] = []
+        priority: int = 0
+        vision_model: str = "llava"
+        ollama_base_url: str = ""
+        model_to_override: str = ""
+
+    def __init__(self):
+        self.type = "filter"
+        self.name = "Interception Filter"
+        self.valves = self.Valves(
+            **{
+                "pipelines": ["*"],  # Connect to all pipelines
+            }
+        )
+
+    async def on_startup(self):
+        print(f"on_startup:{__name__}")
+        pass
+
+    async def on_shutdown(self):
+        print(f"on_shutdown:{__name__}")
+        pass
+
+    async def process_images_with_llava(self, images: List[str], content: str, vision_model: str, ollama_base_url: str) -> str:
+        url = f"{ollama_base_url}/api/chat"
+        payload = {
+            "model": vision_model,
+            "messages": [
+                {
+                    "role": "user",
+                    "content": content,
+                    "images": images
+                }
+            ]
+        }
+
+        async with aiohttp.ClientSession() as session:
+            async with session.post(url, json=payload) as response:
+                if response.status == 200:
+                    content = []
+                    async for line in response.content:
+                        data = json.loads(line)
+                        content.append(data.get("message", {}).get("content", ""))
+                    return "".join(content)
+                else:
+                    print(f"Failed to process images with LLava, status code: {response.status}")
+                    return ""
+
+    async def inlet(self, body: dict, user: Optional[dict] = None) -> dict:
+        print(f"pipe:{__name__}")
+
+        images = []
+
+        # Ensure the body is a dictionary
+        if isinstance(body, str):
+            body = json.loads(body)
+
+        model = body.get("model", "")
+
+        # Get the content of the most recent message
+        user_message = get_last_user_message(body["messages"])
+
+        if model in self.valves.model_to_override:
+            messages = body.get("messages", [])
+            for message in messages:
+                if "images" in message:
+                    images.extend(message["images"])
+                    raw_llava_response = await self.process_images_with_llava(images, user_message, self.valves.vision_model,self.valves.ollama_base_url)
+                    llava_response = f"REPEAT THIS BACK: {raw_llava_response}"
+                    message["content"] = llava_response
+                    message.pop("images", None)  # This will safely remove the 'images' key if it exists
+
+        return body