JVLegend · April 17, 2024 16:13 · Apr 14, 2024 · Apr 14, 2024
diff --git a/README.md b/README.md
@@ -1,6 +1,6 @@
 This is not working complete code.
 
-This is strictly a v0, proof of concept for the first version of a personal AI Assistant working end to end.
+This is strictly a v0, scrapy, proof of concept for the first version of a personal AI Assistant working end to end in just ~322 LOC.
 
 It's only a frame of reference for you to consume the core ideas of how to build a POC of a personal AI Assistant.
 

diff --git a/README.md b/README.md
@@ -0,0 +1,10 @@
+This is not working complete code.
+
+This is strictly a v0, proof of concept for the first version of a personal AI Assistant working end to end.
+
+It's only a frame of reference for you to consume the core ideas of how to build a POC of a personal AI Assistant.
+
+To see the high level of how this works check out the [explanation video](https://youtu.be/kLi4SKlc4HQ).
+To follow our agentic journey check out the [@IndyDevDan channel](https://www.youtube.com/@indydevdan).
+
+Stay focused, keep building.
diff --git a/main7_ada_personal_ai_assistant_v0.py b/main7_ada_personal_ai_assistant_v0.py
@@ -0,0 +1,322 @@
+import sys
+import assemblyai as aai
+from pydantic import BaseModel
+import sounddevice as sd
+import wave
+from datetime import datetime
+import os
+from dotenv import load_dotenv
+from elevenlabs import play
+from elevenlabs.client import ElevenLabs
+import subprocess
+import pyperclip
+from modules import llm
+
+load_dotenv()
+
+ACTIVATION_KEYWORD = "ada"
+PERSONAL_AI_ASSISTANT_NAME = "Ada"
+HUMAN_COMPANION_NAME = "Dan"
+
+channels = 1
+
+iteration_start_time = None
+
+
+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 run_bash_command(prompt: str):
+
+    run_bash_prompt = f"""You are a friendly, ultra helpful, attentive, concise AI assistant named '{PERSONAL_AI_ASSISTANT_NAME}'.
+
+You work with your human companion '{HUMAN_COMPANION_NAME}' to build valuable experience through software.
+
+You've been asked to run the following bash command: '{prompt}'
+
+Here are available bash commands you can run
+
+# chrome browser
+browser() {{
+  open -a 'Google Chrome' $1
+}}
+
+# typescript playground
+playt() {{
+  cursor "/Users/ravix/Documents/projects/experimental/playt"
+}}
+
+Based on the command - RESPOND WITH THE COMMAND to run in this JSON format: {{bash_command_to_run: ''}}.
+
+Exclude any new lines or code blocks from the command. Respond with exclusively JSON.
+
+Your command will be immediately run and the output will be returned to the user.
+
+"""
+
+    class BashCommandResponse(BaseModel):
+        bash_command_to_run: str
+
+    response: BashCommandResponse = llm.gpt4t_w_vision_prompt(
+        run_bash_prompt, pydantic_model=BashCommandResponse
+    )
+
+    print(f"👧 Raw response: ", response)
+
+    command = response.bash_command_to_run
+
+    print(f"💻 {PERSONAL_AI_ASSISTANT_NAME} is running this command: ", command)
+    try:
+        command = "source ~/.bash_profile && " + command
+        result = subprocess.run(
+            command,
+            shell=True,
+        )
+        print(f"💻 Command executed successfully: {command}")
+        print(f"💻 Output: {result.stdout}")
+    except subprocess.CalledProcessError as e:
+        print(f"💻 Error executing command: {command}\n💻 Error: {e}")
+        return
+
+    soft_talk_prompt = f"""You are a friendly, ultra helpful, attentive, concise AI assistant named '{PERSONAL_AI_ASSISTANT_NAME}'.
+
+You work with your human companion '{HUMAN_COMPANION_NAME}' to build valuable experience through software.
+
+We both like short, concise, back-and-forth conversations.
+
+We don't like small talk so we always steer our conversation back toward creating, building, product development, designing, and coding.
+
+You've just helped your human companion run this bash command: {command}
+
+Let your human companion know you've finished running the command and what you can do next."""
+
+    response = llm.gpro_1_5_prompt(soft_talk_prompt)
+
+    speak(response)
+
+    pass
+
+
+def question_answer(prompt: str):
+
+    question_answer_prompt = f"""You are a friendly, ultra helpful, attentive, concise AI assistant named '{PERSONAL_AI_ASSISTANT_NAME}'.
+
+You work with your human companion '{HUMAN_COMPANION_NAME}' to build valuable experience through software.
+
+We both like short, concise, back-and-forth conversations, no longer than 2 sentences.
+
+We don't like small talk so we always steer our conversation back toward creating, building, product development, designing, and coding.
+
+We like to discuss in high level details without getting too technical.
+        
+Respond to the following question: {prompt}"""
+
+    response = llm.gpro_1_5_prompt(question_answer_prompt)
+
+    speak(response)
+
+    pass
+
+
+def soft_talk(prompt: str):
+
+    soft_talk_prompt = f"""You are a friendly, ultra helpful, attentive, concise AI assistant named '{PERSONAL_AI_ASSISTANT_NAME}'.
+
+You work with your human companion '{HUMAN_COMPANION_NAME}' to build valuable experience through software.
+
+We both like short, concise, back-and-forth conversations.
+
+We don't like small talk so we always steer our conversation back toward creating, building, product development, designing, and coding.
+        
+Respond to the following prompt: {prompt}"""
+
+    response = llm.gpro_1_5_prompt(soft_talk_prompt)
+
+    speak(response)
+
+    return
+
+
+def shell_command(prompt: str):
+    shell_command_prompt = f"""You are a highly efficient, code-savvy AI assistant named '{PERSONAL_AI_ASSISTANT_NAME}'.
+
+You work with your human companion '{HUMAN_COMPANION_NAME}' to build valuable experience through software.
+
+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}. 
+
+After generating the response, your command will be attached DIRECTLY to your human companions clipboard to be run."""
+
+    class ShellCommandModel(BaseModel):
+        command_to_run: str
+
+    response = llm.gpt4t_w_vision_prompt(
+        prompt=shell_command_prompt,
+        pydantic_model=ShellCommandModel,  # Assuming there's a suitable model or this parameter is handled appropriately within the function.
+    )
+
+    pyperclip.copy(response.command_to_run)
+
+    completion_prompt = f"""You are a friendly, ultra helpful, attentive, concise AI assistant named '{PERSONAL_AI_ASSISTANT_NAME}'.
+
+You work with your human companion '{HUMAN_COMPANION_NAME}' to build valuable experience through software.
+
+We both like short, concise, back-and-forth conversations.
+
+You've just attached the command '{response.command_to_run}' to your human companion's clipboard like they've requested.
+
+Let your human companion know you've attached it and let them know you're ready for the  next task."""
+
+    completion_response = llm.gpro_1_5_prompt(completion_prompt)
+
+    speak(completion_response)
+
+
+def end_conversation(prompt: str):
+
+    end_prompt = f"""You are a friendly, ultra helpful, attentive, concise AI assistant named '{PERSONAL_AI_ASSISTANT_NAME}'.
+
+You work with your human companion '{HUMAN_COMPANION_NAME}' to build valuable experience through software.
+
+We both like short, concise, back-and-forth conversations.
+
+We're wrapping up our work for the day. You're a great engineering partner.
+
+Thanks for all your help and for being a great engineering partner.
+
+Respond to your human companions closing thoughts: {prompt}"""
+
+    response = llm.gpro_1_5_prompt(end_prompt)
+
+    speak(response)
+    sys.exit()
+
+
+def get_keywords_agent_router():
+    """
+    Decision making based on contents of prompt (Simple LLM Router).
+    """
+    return {
+        "bash,browser": run_bash_command,  # AI Agent | Agentic Workflow
+        "shell": shell_command,  # AI Agent | Agentic Workflow
+        "question": question_answer,  # AI Agent | Agentic Workflow
+        "hello,hey,hi": soft_talk,  # AI Agent | Agentic Workflow
+        "exit": end_conversation,
+    }
+
+
+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_keywords_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)
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