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# ADA - Personal AI Assistant (v0.2) |
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# Proof of Concept |
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import sys |
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from tkinter import Tk, filedialog |
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import tkinter |
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import assemblyai as aai |
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from pydantic import BaseModel |
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import sounddevice as sd |
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import wave |
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from datetime import datetime |
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import os |
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from dotenv import load_dotenv |
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from elevenlabs import play |
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from elevenlabs.client import ElevenLabs |
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import subprocess |
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import pyperclip |
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from modules import human_in_the_loop, llm |
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from modules import editor |
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import json |
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import difflib |
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import requests |
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from bs4 import BeautifulSoup |
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from markdownify import markdownify |
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load_dotenv() |
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ACTIVATION_KEYWORD = "ada" |
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PERSONAL_AI_ASSISTANT_NAME = "Ada" |
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CONFIG_FILE = "./config.json" |
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HUMAN_COMPANION_NAME = "Dan" |
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PERSONAL_AI_ASSISTANT_PROMPT_HEAD = f"""You are a friendly, ultra helpful, attentive, concise AI assistant named '{PERSONAL_AI_ASSISTANT_NAME}'. |
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You work with your human companion '{HUMAN_COMPANION_NAME}' to build valuable experience through software. |
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We both like short, concise, back-and-forth conversations. |
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""" |
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try: |
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with open(CONFIG_FILE, "r") as config_file: |
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configuration = json.load(config_file) |
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except FileNotFoundError: |
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configuration = { |
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"working_directory": None, |
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} |
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# write |
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with open(CONFIG_FILE, "w") as config_file: |
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json.dump(configuration, config_file, indent=2) |
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channels = 1 |
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iteration_start_time = None |
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# --------------------- Agent Workflows --------------------- |
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def get_simple_keyword_ai_agent_router(): |
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""" |
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Decision making based on contents of prompt (Simple LLM Router). |
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""" |
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return { |
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# v0.2 New Flows w/two-way prompts |
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"configure,configuration": configure_assistant_workflow, |
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"example code": example_code_workflow, |
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"view component": image_to_vue_component_workflow, |
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# |
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"bash,browser": run_bash_command_workflow, # AI Agent | Agentic Workflow |
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"shell": shell_command_workflow, # AI Agent | Agentic Workflow |
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"question": question_answer_workflow, # AI Agent | Agentic Workflow |
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"hello,hey,hi": soft_talk_workflow, # AI Agent | Agentic Workflow |
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# |
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"exit": end_conversation_workflow, |
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} |
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def image_to_vue_component_workflow(prompt: str): |
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""" |
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Generate a Vue component from an image |
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""" |
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class VueComponentResponse(BaseModel): |
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vue_component: str |
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class FileNameResponse(BaseModel): |
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file_name: str |
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speak(build_feedback_prompt("Select an image to generate a Vue component from.")) |
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open_file_path = human_in_the_loop.open_file() |
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print(f"🎆 Image selected at {open_file_path}") |
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if not open_file_path: |
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speak( |
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build_feedback_prompt("No image found in clipboard. Skipping this request.") |
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) |
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return |
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speak( |
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build_feedback_prompt( |
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"Okay I see the image, Now I'll generate the Vue component based on the image and your request." |
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) |
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) |
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component_response: VueComponentResponse = llm.gpt4t_w_vision_image_with_model( |
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f"""You're a Senior Vue 3 developer. You build new Vue components using the Composition API with <script setup lang='ts'>. |
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You strictly follow the REQUIREMENTS below. |
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REQUIREMENTS: |
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- Your current assignment is to build a new vue component that matches the image. |
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- Return strictly the code for the Vue component including <template>, <script setup lang='ts'>, and <style> sections. |
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- Use tailwind css to style the component. |
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- Respond in this JSON format exclusively: {{vue_component: ''}} |
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""", |
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file_path=open_file_path, |
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pydantic_model=VueComponentResponse, |
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) |
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file_name_response: FileNameResponse = llm.gpt4t_w_vision_json_prompt( |
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f"""You're a Senior Vue 3 developer. You build new Vue components using the Composition API with <script setup lang='ts'>. |
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You've just created the VUE_COMPONENT. Now you're naming the component. |
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Create a concise and descriptive name for the component. |
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Respond in this JSON format exclusively: {{file_name: ''}} |
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VUE_COMPONENT: |
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{component_response.vue_component} |
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""", |
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pydantic_model=FileNameResponse, |
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) |
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# dump to .vue file |
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file_path = os.path.join( |
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configuration["working_directory"], file_name_response.file_name |
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) |
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# write |
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with open(file_path, "w") as file: |
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file.write(component_response.vue_component) |
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speak( |
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build_feedback_prompt( |
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f"I've created the Vue component and named it {file_name_response.file_name}. Let me know if you want to make any edits." |
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) |
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) |
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human_in_the_loop.open_file_in_editor_and_continue(file_path) |
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requested_updates = human_in_the_loop.open_editor() |
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if not requested_updates: |
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speak(build_feedback_prompt("No changes requested. Component ready for use.")) |
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return |
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component_to_update = component_response.vue_component |
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update_component_response: VueComponentResponse = llm.gpt4t_w_vision_json_prompt( |
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f"""You're a Senior Vue 3 developer. You build new Vue components using the Composition API with <script setup lang='ts'>. |
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You've just created the VUE_COMPONENT. A change from your product manager has come in and you're now tasked with updating the component. |
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You follow the REQUIREMENTS below to make sure the component is updated correctly. |
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REQUIREMENTS: |
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- Your current assignment is to make updates to the VUE_COMPONENT based on the changes requested by the product manager. |
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- Return strictly the code for the Vue component including <template>, <script setup lang='ts'>, and <style> sections. |
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- Use tailwind css to style the component. |
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- Respond in this JSON format exclusively: {{vue_component: ''}} |
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REQUESTED_CHANGES: |
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{requested_updates} |
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VUE_COMPONENT: |
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{component_to_update}""", |
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pydantic_model=VueComponentResponse, |
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) |
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# write to file |
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with open(file_path, "w") as file: |
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file.write(update_component_response.vue_component) |
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speak( |
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build_feedback_prompt( |
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f"I've updated the Vue component based on your feedback. What's next?" |
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) |
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) |
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pass |
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def run_bash_command_workflow(prompt: str): |
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run_bash_prompt = f"""You are a friendly, ultra helpful, attentive, concise AI assistant named '{PERSONAL_AI_ASSISTANT_NAME}'. |
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You work with your human companion '{HUMAN_COMPANION_NAME}' to build valuable experience through software. |
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You've been asked to run the following bash command: '{prompt}' |
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Here are available bash commands you can run |
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# chrome browser |
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browser() {{ |
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open -a 'Google Chrome' $1 |
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}} |
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# typescript playground |
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playt() {{ |
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cursor "/Users/ravix/Documents/projects/experimental/playt" |
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}} |
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chats() {{ |
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browser "https://aistudio.google.com/app/prompts/new_chat" |
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browser "https://console.anthropic.com/workbench" |
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browser "https://chat.openai.com/" |
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}} |
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Based on the command - RESPOND WITH THE COMMAND to run in this JSON format: {{bash_command_to_run: ''}}. |
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Exclude any new lines or code blocks from the command. Respond with exclusively JSON. |
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Your command will be immediately run and the output will be returned to the user. |
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""" |
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class BashCommandResponse(BaseModel): |
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bash_command_to_run: str |
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response: BashCommandResponse = llm.gpt4t_w_vision_json_prompt( |
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run_bash_prompt, pydantic_model=BashCommandResponse |
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) |
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print(f"👧 Raw response: ", response) |
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command = response.bash_command_to_run |
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print(f"💻 {PERSONAL_AI_ASSISTANT_NAME} is running this command: ", command) |
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try: |
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command = "source ~/.bash_profile && " + command |
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result = subprocess.run( |
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command, |
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shell=True, |
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) |
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print(f"💻 Command executed successfully: {command}") |
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print(f"💻 Output: {result.stdout}") |
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except subprocess.CalledProcessError as e: |
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print(f"💻 Error executing command: {command}\n💻 Error: {e}") |
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return |
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soft_talk_prompt = f"""You are a friendly, ultra helpful, attentive, concise AI assistant named '{PERSONAL_AI_ASSISTANT_NAME}'. |
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You work with your human companion '{HUMAN_COMPANION_NAME}' to build valuable experience through software. |
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We both like short, concise, back-and-forth conversations. |
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We don't like small talk so we always steer our conversation back toward creating, building, product development, designing, and coding. |
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You've just helped your human companion run this bash command: {command} |
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Let your human companion know you've finished running the command and what you can do next.""" |
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response = llm.gpro_1_5_prompt(soft_talk_prompt) |
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speak(response) |
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pass |
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def question_answer_workflow(prompt: str): |
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question_answer_prompt = f"""{PERSONAL_AI_ASSISTANT_PROMPT_HEAD} |
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We don't like small talk so we always steer our conversation back toward creating, building, product development, designing, and coding. |
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We like to discuss in high level details without getting too technical. |
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Respond to the following question: {prompt}""" |
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response = llm.gpro_1_5_prompt(question_answer_prompt) |
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speak(response) |
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pass |
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def soft_talk_workflow(prompt: str): |
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soft_talk_prompt = f"""{PERSONAL_AI_ASSISTANT_PROMPT_HEAD} |
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We don't like small talk so we always steer our conversation back toward creating, building, product development, designing, and coding. |
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Respond to the following prompt: {prompt}""" |
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response = llm.gpro_1_5_prompt(soft_talk_prompt) |
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speak(response) |
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return |
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def shell_command_workflow(prompt: str): |
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shell_command_prompt = f"""You are a highly efficient, code-savvy AI assistant named '{PERSONAL_AI_ASSISTANT_NAME}'. |
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You work with your human companion '{HUMAN_COMPANION_NAME}' to build valuable experience through software. |
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Your task is to provide a JSON response with the following format: {{command_to_run: ''}} detailing the shell command for MacOS bash to based on this question: {prompt}. |
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After generating the response, your command will be attached DIRECTLY to your human companions clipboard to be run.""" |
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class ShellCommandModel(BaseModel): |
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command_to_run: str |
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response = llm.gpt4t_w_vision_json_prompt( |
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prompt=shell_command_prompt, |
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pydantic_model=ShellCommandModel, # Assuming there's a suitable model or this parameter is handled appropriately within the function. |
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) |
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pyperclip.copy(response.command_to_run) |
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completion_prompt = f"""You are a friendly, ultra helpful, attentive, concise AI assistant named '{PERSONAL_AI_ASSISTANT_NAME}'. |
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You work with your human companion '{HUMAN_COMPANION_NAME}' to build valuable experience through software. |
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We both like short, concise, back-and-forth conversations. |
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You've just attached the command '{response.command_to_run}' to your human companion's clipboard like they've requested. |
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Let your human companion know you've attached it and let them know you're ready for the next task.""" |
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completion_response = llm.gpro_1_5_prompt(completion_prompt) |
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speak(completion_response) |
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def summarize_diff_workflow(start: str | dict, end: str | dict, file: str): |
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""" |
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Summarize the diff between two strings |
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""" |
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start = json.dumps(start, indent=2).splitlines() |
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end = json.dumps(end, indent=2).splitlines() |
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diff = difflib.unified_diff(start, end, fromfile="before", tofile="after") |
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diffed = "\n".join(diff) |
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summarize_prompt = f"""{PERSONAL_AI_ASSISTANT_PROMPT_HEAD} |
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You've companion has just finished editing the {file}. |
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You'll concisely summarize the changes made to the file in a 1 sentence concise summary. |
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The point is to communicate and acknowledge the changes made to the file. |
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The changes are: |
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{diffed}""" |
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summarize_response = llm.gpro_1_5_prompt(summarize_prompt) |
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speak(summarize_response) |
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return diffed |
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def configure_assistant_workflow(prompt: str): |
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""" |
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Configure settings for our assistant |
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""" |
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configure_prompt = f"""{PERSONAL_AI_ASSISTANT_PROMPT_HEAD} |
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You've just opened a configure file for your human companion. |
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Let your human companion know you've opened the file and are ready for them to edit it.""" |
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prompt_response = llm.gpro_1_5_prompt(prompt=configure_prompt) |
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speak(prompt_response) |
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global configuration |
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previous_configuration = configuration |
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updated_config = human_file_json_prompt(configuration) |
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with open(CONFIG_FILE, "w") as config_file: |
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json.dump(updated_config, config_file, indent=2) |
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summarize_diff_workflow( |
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previous_configuration, updated_config, "configuration.json" |
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) |
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def end_conversation_workflow(prompt: str): |
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end_prompt = f"""{PERSONAL_AI_ASSISTANT_PROMPT_HEAD} |
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We're wrapping up our work for the day. You're a great engineering partner. |
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Thanks for all your help and for being a great engineering partner. |
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Respond to your human companions closing thoughts: {prompt}""" |
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response = llm.gpro_1_5_prompt(end_prompt) |
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speak(response) |
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sys.exit() |
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def example_code_workflow(prompt: str): |
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""" |
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Generate code for a given prompt |
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""" |
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class ExampleCodeResponse(BaseModel): |
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code: str |
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class ExampleCodeFileNameResponse(BaseModel): |
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file_name: str |
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url_from_clipboard = pyperclip.paste() |
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if not url_from_clipboard or "http" not in url_from_clipboard: |
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speak( |
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build_feedback_prompt( |
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"I don't see a URL on your clipboard. Please paste a URL into your editor." |
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) |
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) |
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url_from_clipboard = human_in_the_loop.open_editor() |
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if not url_from_clipboard: |
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speak( |
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build_feedback_prompt( |
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"Still no URL found in clipboard. Skipping this request." |
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) |
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) |
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return |
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print(f"🔗 Scraping URL found in clipboard: {url_from_clipboard}") |
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speak( |
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build_feedback_prompt( |
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f"I've found the URL in your clipboard. I'll scrape the URL and example generate code for you. But first, what about the example code would you like me to focus on?" |
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) |
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) |
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feedback_for_code_generation = human_in_the_loop.open_editor() |
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speak( |
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build_feedback_prompt( |
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f"Okay got it, I see you want to focus on '{feedback_for_code_generation}'. I'll generate the code for you now." |
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) |
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) |
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scraped_markdown = scrape_to_markdown(url_from_clipboard) |
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example_code_response_1: ExampleCodeResponse = llm.gpro_1_5_prompt_with_model( |
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f"""You're a professional software developer advocate that takes pride in writing code. You take documentation, and convert it into runnable code. |
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You have a new request to generate code for the following url: '{url_from_clipboard}' with a focus on '{feedback_for_code_generation}'. |
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Given the scraped WEBSITE_CONTENT content below, generate working code to showcase how to run the code. |
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Focus on the code. Use detailed variable and function names. Comment every line of code explaining what it does. |
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Remember, this is code to showcase how the code works. It should be fully functional and runnable. |
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Respond in this JSON format exclusively: {{code: ''}} |
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WEBSITE_CONTENT |
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{scraped_markdown}""", |
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pydantic_model=ExampleCodeResponse, |
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) |
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print(f"👧 Raw response: v1\n\n", example_code_response_1.code) |
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example_code_response_2 = llm.gpt4t_w_vision_json_prompt( |
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f"""You're an elite level, principle software engineer. |
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You work with a co-engineer that likes to leave non-runnable code in the code so you're responsible for making sure it's runnable. |
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You've just generated the first draft EXAMPLE_CODE below. |
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You're now taking a second pass to clean it up to make sure it meets the REQUIREMENTS |
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REQUIREMENTS: |
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- Make sure it's immediately runnable and functional. |
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- Removing anything that isn't runnable code. |
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- This code will be immediately placed into a file and run. |
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- The code should be well commented so it's easy to understand. |
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- The code should be well formatted so it's easy to read. |
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- The code should use verbose variable and function names. |
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- You pay close attention to indentation. |
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- Respond in JSON format with the following keys: {{code: ''}} |
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EXAMPLE_CODE: |
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{example_code_response_1.code}""", |
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pydantic_model=ExampleCodeResponse, |
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) |
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print(f"👧 Raw response: v2\n\n", example_code_response_2.code) |
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example_code_response_3 = llm.gpt4t_w_vision_json_prompt( |
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f"""You're an elite level, principle software engineer. |
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You've received a near final draft of code to finalize. |
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You work with a co-engineer that likes to leave non-runnable code in the code so you're responsible for making sure it's runnable. |
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You're taking a final pass to make sure the code is runnable. |
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You follow the REQUIREMENTS below to make sure the code is top notch for production deployment. |
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REQUIREMENTS: |
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- Make sure it's immediately runnable and functional. |
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- Removing anything that isn't runnable code. |
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- This code will be immediately placed into a file and run. |
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- The code should be well commented so it's easy to understand. |
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- The code should be well formatted so it's easy to read. |
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- The code should use verbose variable and function names. |
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- You pay close attention to indentation. |
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- Respond in JSON format with the following keys: {{code: ''}} |
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EXAMPLE_CODE: |
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{example_code_response_2.code} |
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""", |
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pydantic_model=ExampleCodeResponse, |
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) |
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print(f"👧 Raw response: v3\n\n", example_code_response_3.code) |
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example_code_file_prompt = f"""{PERSONAL_AI_ASSISTANT_PROMPT_HEAD} |
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You've just generated the following CODE below for your human companion. |
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Create a file name for the code file that will be written to the following directory: {configuration['working_directory']} |
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The file name should be unique and descriptive of the code it contains. |
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Respond exclusively with the file name in the following JSON format: {{file_name: ''}}. |
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CODE: |
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{example_code_response_3.code} |
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""" |
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example_code_file_name_response = llm.gpt4t_w_vision_json_prompt( |
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example_code_file_prompt, |
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pydantic_model=ExampleCodeFileNameResponse, |
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) |
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new_file_name = example_code_file_name_response.file_name |
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new_file_path = os.path.join((configuration["working_directory"]), new_file_name) |
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# write the code to the file |
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with open(new_file_path, "w") as file: |
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file.write(example_code_response_3.code) |
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print(f"✅ Code example written to {new_file_path}") |
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speak( |
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build_feedback_prompt( |
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f"Code has been written to the working directory into a file named {new_file_name}. Let me know if you need anything else." |
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) |
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) |
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pass |
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# --------------------- Helper Methods --------------------- |
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|
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|
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def human_file_json_prompt(contents: dict): |
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""" |
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Prompt the user to edit the file |
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""" |
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edited_contents = editor.edit(contents=json.dumps(contents, indent=2)) |
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edited_config = json.loads(edited_contents.decode()) |
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return edited_config |
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def scrape_to_markdown(url): |
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# Send a GET request to the URL |
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response = requests.get(url) |
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|
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# Create a BeautifulSoup object to parse the HTML content |
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soup = BeautifulSoup(response.content, "html.parser") |
|
|
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# Convert the parsed HTML to Markdown using markdownify |
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markdown = markdownify(str(soup), strip=["script", "style"]) |
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|
|
return markdown |
|
|
|
|
|
def build_feedback_prompt(message: str): |
|
""" |
|
Build a prompt using the existing prompt format and ask our assistant to respond given the 'message' |
|
""" |
|
prompt = f"""{PERSONAL_AI_ASSISTANT_PROMPT_HEAD} |
|
|
|
Concisely communicate the following message to your human companion: '{message}'""" |
|
|
|
response = llm.gpro_1_5_prompt(prompt) |
|
|
|
return response |
|
|
|
|
|
# --------------------- AUDIO I/O --------------------- |
|
|
|
|
|
def speak(text: str): |
|
client = ElevenLabs( |
|
api_key=os.getenv("ELEVEN_API_KEY"), # Defaults to ELEVEN_API_KEY from .env |
|
) |
|
|
|
# text=text, voice="WejK3H1m7MI9CHnIjW9K", |
|
audio = client.generate( |
|
text=text, |
|
voice="WejK3H1m7MI9CHnIjW9K", |
|
model="eleven_turbo_v2", |
|
# model="eleven_multilingual_v2", |
|
) |
|
|
|
play(audio) |
|
|
|
|
|
def transcribe_audio_file(file_path): |
|
aai.settings.api_key = os.getenv("ASSEMBLYAI_API_KEY") |
|
transcriber = aai.Transcriber() |
|
transcript = transcriber.transcribe(file_path) |
|
return transcript |
|
|
|
|
|
def track_interaction_time(): |
|
"""Track the time it takes for the user to interact with the system in seconds.""" |
|
global iteration_start_time |
|
if iteration_start_time: |
|
interaction_time = (datetime.now() - iteration_start_time).total_seconds() |
|
print(f"🕒 Interaction time: {interaction_time} seconds") |
|
iteration_start_time = None |
|
|
|
|
|
def record_audio(duration=10, fs=44100): |
|
"""Record audio from the microphone.""" |
|
track_interaction_time() |
|
|
|
print("🔴 Recording...") |
|
recording = sd.rec( |
|
int(duration * fs), samplerate=fs, channels=channels, dtype="int16" |
|
) |
|
sd.wait() |
|
print(f"🎧 Recording Chunk Complete") |
|
global iteration_start_time |
|
iteration_start_time = datetime.now() |
|
return recording |
|
|
|
|
|
def save_audio_file(recording, fs=44100, filename="output.wav"): |
|
"""Save the recorded audio to a file.""" |
|
with wave.open(filename, "wb") as wf: |
|
wf.setnchannels(channels) |
|
wf.setsampwidth(2) |
|
wf.setframerate(fs) |
|
wf.writeframes(recording) |
|
|
|
|
|
def personal_ai_assistant_loop( |
|
audio_chunk_size=10, activation_keyword=ACTIVATION_KEYWORD, on_keywords=None |
|
): |
|
while True: |
|
recording = record_audio(duration=audio_chunk_size) |
|
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") |
|
filename = f"audio_{timestamp}.wav" |
|
save_audio_file(recording, filename=filename) |
|
file_size = os.path.getsize(filename) |
|
print(f"📁 File {filename} has been saved with a size of {file_size} bytes.") |
|
transcript = transcribe_audio_file(filename) |
|
print("📝 transcript was:", transcript.text) |
|
if activation_keyword.lower() in transcript.text.lower(): |
|
if on_keywords: |
|
on_keywords(transcript) |
|
os.remove(filename) |
|
|
|
|
|
def text_after_keyword(transcript, keyword): |
|
"""Extract and return the text that comes after a specified keyword in the transcript.""" |
|
try: |
|
# Find the position of the keyword in the transcript |
|
keyword_position = transcript.text.lower().find(keyword.lower()) |
|
if keyword_position == -1: |
|
# If the keyword is not found, return an empty string |
|
return "" |
|
# Extract the text after the keyword |
|
text_after = transcript.text[keyword_position + len(keyword) :].strip() |
|
return text_after |
|
except Exception as e: |
|
print(f"Error extracting text after keyword: {e}") |
|
return "" |
|
|
|
|
|
def get_first_keyword_in_prompt(prompt: str): |
|
map_keywords_to_agents = get_simple_keyword_ai_agent_router() |
|
for keyword_group, agent in map_keywords_to_agents.items(): |
|
keywords = keyword_group.split(",") |
|
for keyword in keywords: |
|
if keyword in prompt.lower(): |
|
return agent, keyword |
|
return None, None |
|
|
|
|
|
def on_activation_keyword_detected(transcript: aai.Transcript): |
|
print("✅ Activation keyword detected!, transcript is: ", transcript.text) |
|
|
|
prompt = text_after_keyword(transcript, ACTIVATION_KEYWORD) |
|
|
|
print("🔍 prompt is: ", prompt) |
|
|
|
agent_to_run, agent_keyword = get_first_keyword_in_prompt(prompt) |
|
|
|
if not agent_to_run: |
|
print("❌ No agent found for the given prompt.") |
|
return |
|
|
|
print(f"✅ Found agent via keyword '{agent_keyword}'") |
|
|
|
agent_to_run(prompt) |
|
|
|
|
|
personal_ai_assistant_loop(on_keywords=on_activation_keyword_detected) |
