refactor: simplify code ( part I ) (#3)

* refactor: default to ollama llm locally

* refactor: simplify code further

* chore: tauri build ci

* fix: ci

* chore: run ruff linter

.github/workflows/ci-rs.yml

@@ -5,8 +5,8 @@ on:
     paths:
       - "**/.github/workflows/ci-rs.yml"
       - "**/Cargo.lock"
-      - "**/src/**"
       - "**/Cargo.toml"
+      - "**/src-tauri/**"
       - "**/rust-toolchain"
       - "**/.taplo.toml"
   workflow_dispatch:
@@ -42,8 +42,11 @@ jobs:
           components: rustfmt, clippy
           enable-sccache: "true"
 
+      - name: Install Tauri
+        run: cargo install tauri
+
       - name: Build
-        run: cargo build --release 
+        run: cargo tauri build --release 
 
       - name: Test
         run: cargo test --release 

.gitignore

@@ -52,3 +52,6 @@ dist-ssr
 *.njsproj
 *.sln
 *.sw?
+
+# python
+.cenv

core/source/clients/base_device.py → core/mac_device.py

@@ -4,10 +4,7 @@
 import os
 import asyncio
 import threading
-import os
 import pyaudio
-from starlette.websockets import WebSocket
-from queue import Queue
 from pynput import keyboard
 import json
 import traceback
@@ -23,17 +20,17 @@
 import base64
 from interpreter import interpreter # Just for code execution. Maybe we should let people do from interpreter.computer import run?
 # In the future, I guess kernel watching code should be elsewhere? Somewhere server / client agnostic?
-from ..server.utils.kernel import put_kernel_messages_into_queue
-from ..server.utils.process_utils import kill_process_tree
+from source.server.utils.kernel import put_kernel_messages_into_queue
+from source.server.utils.process_utils import kill_process_tree
 
-from ..server.utils.logs import setup_logging
-from ..server.utils.logs import logger
+from source.server.utils.logs import setup_logging
+from source.server.utils.logs import logger
 setup_logging()
 
 os.environ["STT_RUNNER"] = "server"
 os.environ["TTS_RUNNER"] = "server"
 
-from ..utils.accumulator import Accumulator
+from source.utils.accumulator import Accumulator
 
 accumulator = Accumulator()
 
@@ -324,4 +321,13 @@ async def start_async(self):
     def start(self):
         if os.getenv('TEACH_MODE') != "True":
             asyncio.run(self.start_async())
-            p.terminate()
+            p.terminate()
+
+device = Device()
+
+def run_device(server_url):
+    device.server_url = server_url
+    device.start()
+
+if __name__ == "__main__":
+    run_device()

core/source/server/conftest.py

@@ -1,8 +1,5 @@
-import os
-import sys
 import pytest
 from source.server.i import configure_interpreter
-from unittest.mock import Mock
 from interpreter import OpenInterpreter
 from fastapi.testclient import TestClient
 from .server import app

core/source/server/i.py

@@ -1,11 +1,9 @@
 from dotenv import load_dotenv
 load_dotenv()  # take environment variables from .env.
 
-import os
 import glob
 import time
 import json
-from pathlib import Path
 from interpreter import OpenInterpreter
 import shutil
 

core/source/server/server.py

@@ -3,7 +3,6 @@
 
 import traceback
 from platformdirs import user_data_dir
-import ast
 import json
 import queue
 import os
@@ -13,9 +12,7 @@
 from fastapi import FastAPI, Request
 from fastapi.responses import PlainTextResponse
 from starlette.websockets import WebSocket, WebSocketDisconnect
-from pathlib import Path
 import asyncio
-import urllib.parse
 from .utils.kernel import put_kernel_messages_into_queue
 from .i import configure_interpreter
 from interpreter import interpreter
@@ -352,7 +349,6 @@ def stream_tts(sentence):
 
 from uvicorn import Config, Server
 import os
-import platform
 from importlib import import_module
 
 # these will be overwritten
@@ -363,7 +359,7 @@ def stream_tts(sentence):
 async def startup_event():
     server_url = f"{HOST}:{PORT}"
     print("")
-    print_markdown(f"\n*Ready.*\n")
+    print_markdown("\n*Ready.*\n")
     print("")
 
 @app.on_event("shutdown")

core/source/server/services/stt/local-whisper/stt.py

@@ -10,8 +10,6 @@
 import ffmpeg
 import subprocess
 
-import os
-import subprocess
 
 
 class Stt:

core/source/server/services/tts/openai/tts.py

@@ -2,8 +2,6 @@
 import tempfile
 from openai import OpenAI
 import os
-import subprocess
-import tempfile
 
 client = OpenAI()
 

core/source/server/skills/schedule.py

@@ -3,7 +3,6 @@
 from pytimeparse import parse
 from crontab import CronTab
 from uuid import uuid4
-from datetime import datetime
 from platformdirs import user_data_dir
 
 def schedule(message="", start=None, interval=None) -> None:

core/source/server/tunnel.py

@@ -1,12 +1,10 @@
-import os
 import subprocess
 import re
-import shutil
 import time
 from ..utils.print_markdown import print_markdown
 
 def create_tunnel(tunnel_method='ngrok', server_host='localhost', server_port=10001):
-    print_markdown(f"Exposing server to the internet...")
+    print_markdown("Exposing server to the internet...")
 
     if tunnel_method == "bore":
         try:

core/source/server/utils/kernel.py

@@ -3,10 +3,8 @@
 
 import asyncio
 import subprocess
-import platform
 
 from .logs import setup_logging
-from .logs import logger
 setup_logging()
 
 def get_kernel_messages():

core/source/server/utils/local_mode.py

@@ -1,102 +1,53 @@
 import sys
-import os
-import platform
 import subprocess
 import time
 import inquirer
 from interpreter import interpreter
 
 
 def select_local_model():
-
-    # START OF LOCAL MODEL PROVIDER LOGIC
-    interpreter.display_message("> 01 is compatible with several local model providers.\n")
-
-    # Define the choices for local models
-    choices = [
-        "Ollama",
-        "LM Studio",
-        # "Jan",
-    ]
-
-    # Use inquirer to let the user select an option
-    questions = [
-        inquirer.List(
-            "model",
-            message="Which one would you like to use?",
-            choices=choices,
-        ),
-    ]
-    answers = inquirer.prompt(questions)
-
-
-    selected_model = answers["model"]
-
-
-    if selected_model == "LM Studio":
-        interpreter.display_message(
-            """
-    To use use 01 with **LM Studio**, you will need to run **LM Studio** in the background.
-
-    1. Download **LM Studio** from [https://lmstudio.ai/](https://lmstudio.ai/), then start it.
-    2. Select a language model then click **Download**.
-    3. Click the **<->** button on the left (below the chat button).
-    4. Select your model at the top, then click **Start Server**.
-
-
-    Once the server is running, you can begin your conversation below.
-
-    """
-        )
-        time.sleep(1)
+    selected_model = "Ollama"
+    try:
 
-        interpreter.llm.api_base = "http://localhost:1234/v1"
-        interpreter.llm.max_tokens = 1000
-        interpreter.llm.context_window = 8000
-        interpreter.llm.api_key = "x"
-
-    elif selected_model == "Ollama":
-        try:
-
-            # List out all downloaded ollama models. Will fail if ollama isn't installed
-            result = subprocess.run(["ollama", "list"], capture_output=True, text=True, check=True)
-            lines = result.stdout.split('\n')
-            names = [line.split()[0].replace(":latest", "") for line in lines[1:] if line.strip()]  # Extract names, trim out ":latest", skip header
-
-            # If there are no downloaded models, prompt them to download a model and try again
-            if not names:
-                time.sleep(1)
-
-                interpreter.display_message(f"\nYou don't have any Ollama models downloaded. To download a new model, run `ollama run <model-name>`, then start a new 01 session. \n\n For a full list of downloadable models, check out [https://ollama.com/library](https://ollama.com/library) \n")
-
-                print("Please download a model then try again\n")
-                time.sleep(2)
-                sys.exit(1)
+        # List out all downloaded ollama models. Will fail if ollama isn't installed
+        result = subprocess.run(["ollama", "list"], capture_output=True, text=True, check=True)
+        lines = result.stdout.split('\n')
+        names = [line.split()[0].replace(":latest", "") for line in lines[1:] if line.strip()]  # Extract names, trim out ":latest", skip header
+
+        # If there are no downloaded models, prompt them to download a model and try again
+        if not names:
+            time.sleep(1)
 
-            # If there are models, prompt them to select one
-            else:
-                time.sleep(1)
-                interpreter.display_message(f"**{len(names)} Ollama model{'s' if len(names) != 1 else ''} found.** To download a new model, run `ollama run <model-name>`, then start a new 01 session. \n\n For a full list of downloadable models, check out [https://ollama.com/library](https://ollama.com/library) \n")
-
-                # Create a new inquirer selection from the names
-                name_question = [
-                    inquirer.List('name', message="Select a downloaded Ollama model", choices=names),
-                ]
-                name_answer = inquirer.prompt(name_question)
-                selected_name = name_answer['name'] if name_answer else None
-
-                # Set the model to the selected model
-                interpreter.llm.model = f"ollama/{selected_name}"
-                interpreter.display_message(f"\nUsing Ollama model: `{selected_name}` \n")
-                time.sleep(1)
+            interpreter.display_message("\nYou don't have any Ollama models downloaded. To download a new model, run `ollama run <model-name>`, then start a new 01 session. \n\n For a full list of downloadable models, check out [https://ollama.com/library](https://ollama.com/library) \n")
 
-        # If Ollama is not installed or not recognized as a command, prompt the user to download Ollama and try again
-        except (subprocess.CalledProcessError, FileNotFoundError) as e:
-            print("Ollama is not installed or not recognized as a command.")
-            time.sleep(1)
-            interpreter.display_message(f"\nPlease visit [https://ollama.com/](https://ollama.com/) to download Ollama and try again\n")
+            print("Please download a model then try again\n")
             time.sleep(2)
             sys.exit(1)
+
+        # If there are models, prompt them to select one
+        else:
+            time.sleep(1)
+            interpreter.display_message(f"**{len(names)} Ollama model{'s' if len(names) != 1 else ''} found.** To download a new model, run `ollama run <model-name>`, then start a new 01 session. \n\n For a full list of downloadable models, check out [https://ollama.com/library](https://ollama.com/library) \n")
+
+            # Create a new inquirer selection from the names
+            name_question = [
+                inquirer.List('name', message="Select a downloaded Ollama model", choices=names),
+            ]
+            name_answer = inquirer.prompt(name_question)
+            selected_name = name_answer['name'] if name_answer else None
+
+            # Set the model to the selected model
+            interpreter.llm.model = "ollama/mistral"
+            interpreter.display_message(f"\nUsing Ollama model: `{selected_name}` \n")
+            time.sleep(1)
+
+    # If Ollama is not installed or not recognized as a command, prompt the user to download Ollama and try again
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        print("Ollama is not installed or not recognized as a command.")
+        time.sleep(1)
+        interpreter.display_message("\nPlease visit [https://ollama.com/](https://ollama.com/) to download Ollama and try again\n")
+        time.sleep(2)
+        sys.exit(1)
 
     # elif selected_model == "Jan":
     #     interpreter.display_message(

core/source/server/utils/process_utils.py

@@ -25,4 +25,4 @@ def kill_process_tree():
     except psutil.NoSuchProcess:
         print(f"Process {pid} does not exist or is already terminated")
     except psutil.AccessDenied:
-        print(f"Permission denied to terminate some processes")
+        print("Permission denied to terminate some processes")