feat: add changetone provider

Signed-off-by: Jana Peper <[email protected]>

lib/change_tone.py

@@ -0,0 +1,40 @@
+# SPDX-FileCopyrightText: 2024 Nextcloud GmbH and Nextcloud contributors
+# SPDX-License-Identifier: AGPL-3.0-or-later
+"""A chain that changes the tone of a text
+"""
+
+from typing import Any
+
+from langchain.prompts import PromptTemplate
+from langchain.schema.prompt_template import BasePromptTemplate
+from langchain_core.runnables import Runnable
+
+class ChangeToneProcessor():
+
+    runnable: Runnable
+
+    """
+    A topics chain
+    """
+    system_prompt: str = "You're an AI assistant tasked with finding the topic keywords of the text given to you by the user."
+    user_prompt: BasePromptTemplate = PromptTemplate(
+            input_variables=["text", "tone"],
+            template="""
+    Reformulate the following text in a " {tone} " tone in its original language. Output only the reformulation. Here is the text:
+    
+    "
+    {text}
+    "
+    
+    Output only the reformulated text, nothing else, no introductory sentence. Use the same language as the original text.
+    """
+    )
+
+    def __init__(self, runnable: Runnable):
+        self.runnable = runnable
+
+
+    def __call__(self, inputs: dict[str,Any],
+        ) -> dict[str, Any]:
+            output = self.runnable.invoke({"user_prompt": self.user_prompt.format_prompt(text=inputs['input'], tone=inputs['tone']), "system_prompt": self.system_prompt})
+            return {'output': output}

lib/main.py

@@ -15,7 +15,7 @@
 from fastapi import FastAPI
 from nc_py_api import AsyncNextcloudApp, NextcloudApp, NextcloudException
 from nc_py_api.ex_app import LogLvl, persistent_storage, run_app, set_handlers
-from nc_py_api.ex_app.providers.task_processing import TaskProcessingProvider
+from nc_py_api.ex_app.providers.task_processing import TaskProcessingProvider, ShapeEnumValue
 
 models_to_fetch = {
     "https://huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF/resolve/4f0c246f125fc7594238ebe7beb1435a8335f519/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf": { "save_path": os.path.join(persistent_storage(), "Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf") },
@@ -123,6 +123,15 @@ async def enabled_handler(enabled: bool, nc: AsyncNextcloudApp) -> str:
                     name="Local Large language Model: " + model,
                     task_type=task,
                     expected_runtime=30,
+                     input_shape_enum_values= {
+                            "tone": [
+                                ShapeEnumValue(name= "Friendlier", value= "friendlier"),
+                                ShapeEnumValue(name= "More formal", value= "more formal"),
+                                ShapeEnumValue(name= "Funnier", value= "funnier"),
+                                ShapeEnumValue(name= "More casual", value= "more casual"),
+                                ShapeEnumValue(name= "More urgent", value= "more urgent"),
+                            ],
+                        } if task == "core:text2text:changetone" else {}
                 )
                 await nc.providers.task_processing.register(provider)
                 print(f"Registered {task_processor_name}", flush=True)

lib/task_processors.py

@@ -17,6 +17,7 @@
 from free_prompt import FreePromptProcessor
 from headline import HeadlineProcessor
 from proofread import ProofreadProcessor
+from change_tone import ChangeToneProcessor
 from chatwithtools import ChatWithToolsProcessor
 from topics import TopicsProcessor
 from summarize import SummarizeProcessor
@@ -134,6 +135,7 @@ def generate_task_processors_for_model(file_name, task_processors):
     task_processors[model_name + ":core:text2text"] = lambda: FreePromptProcessor(generate_llm_chain(file_name))
     task_processors[model_name + ":core:text2text:chat"] = lambda: ChatProcessor(generate_chat_chain(file_name))
     task_processors[model_name + ":core:text2text:proofread"] = lambda: ProofreadProcessor(generate_llm_chain(file_name))
+    task_processors[model_name + ":core:text2text:changetone"] = lambda: ChangeToneProcessor(generate_llm_chain(file_name))
     task_processors[model_name + ":core:text2text:chatwithtools"] = lambda: ChatWithToolsProcessor(generate_chat_chain(file_name))
 
     # chains[model_name + ":core:contextwrite"] = lambda: ContextWriteChain(llm_chain=llm_chain())