cleanup server

1 files changed, 0 insertions, 78 deletions

diff --git a/server/continuedev/libs/llm/replicate.py b/server/continuedev/libs/llm/replicate.py
deleted file mode 100644
index 3423193b..00000000
--- a/server/continuedev/libs/llm/replicate.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import concurrent.futures
-from typing import List
-
-import replicate
-from pydantic import Field
-
-from ...core.main import ChatMessage
-from .base import LLM
-from .prompts.edit import simplified_edit_prompt
-
-
-class ReplicateLLM(LLM):
-    """
-    Replicate is a great option for newly released language models or models that you've deployed through their platform. Sign up for an account [here](https://replicate.ai/), copy your API key, and then select any model from the [Replicate Streaming List](https://replicate.com/collections/streaming-language-models). Change `~/.continue/config.py` to look like this:
-
-    ```python title="~/.continue/config.py"
-    from continuedev.core.models import Models
-    from continuedev.libs.llm.replicate import ReplicateLLM
-
-    config = ContinueConfig(
-        ...
-        models=Models(
-            default=ReplicateLLM(
-                model="replicate/codellama-13b-instruct:da5676342de1a5a335b848383af297f592b816b950a43d251a0a9edd0113604b",
-                api_key="my-replicate-api-key")
-        )
-    )
-    ```
-
-    If you don't specify the `model` parameter, it will default to `replicate/llama-2-70b-chat:58d078176e02c219e11eb4da5a02a7830a283b14cf8f94537af893ccff5ee781`.
-    """
-
-    api_key: str = Field(..., description="Replicate API key")
-
-    model: str = "replicate/llama-2-70b-chat:58d078176e02c219e11eb4da5a02a7830a283b14cf8f94537af893ccff5ee781"
-
-    _client: replicate.Client = None
-
-    prompt_templates = {
-        "edit": simplified_edit_prompt,
-    }
-
-    async def start(self, **kwargs):
-        await super().start(**kwargs)
-        self._client = replicate.Client(api_token=self.api_key)
-
-    async def _complete(self, prompt: str, options):
-        def helper():
-            output = self._client.run(
-                self.model, input={"message": prompt, "prompt": prompt}
-            )
-            completion = ""
-            for item in output:
-                completion += item
-
-            return completion
-
-        with concurrent.futures.ThreadPoolExecutor() as executor:
-            future = executor.submit(helper)
-            completion = future.result()
-
-        return completion
-
-    async def _stream_complete(self, prompt, options):
-        for item in self._client.run(
-            self.model, input={"message": prompt, "prompt": prompt}
-        ):
-            yield item
-
-    async def _stream_chat(self, messages: List[ChatMessage], options):
-        for item in self._client.run(
-            self.model,
-            input={
-                "message": messages[-1]["content"],
-                "prompt": messages[-1]["content"],
-            },
-        ):
-            yield {"content": item, "role": "assistant"}