Creo Un Asistente Virtual Que Hace Todo (Y Se Rebela)

How to Create a Virtual Assistant Like Siri or Alexa

Introduction to Building a Virtual Assistant

• The speaker introduces the concept of creating a virtual assistant similar to Siri or Alexa, emphasizing customization and personality.

• Key functionalities include voice recording, converting speech to text, processing commands, and generating audio responses.

Steps for Development

• The process begins with recording user voice and converting it into text using OpenAI's Whisper model.

• A new Python project is created; necessary libraries are imported for audio processing.

Recording Audio

• The speaker demonstrates testing the voice-to-text model by recording an audio sample using Audacity.

• Plans are made to create a web application for easier audio recording instead of manual processes.

Web Application Setup

• The speaker discusses using Flash to build a web app that integrates with Python for voice input.

• A website is created easily with customizable features like logos and themes, aimed at promoting personal projects.

Enhancing User Experience

• The website includes sections for showcasing games developed in Python along with customer testimonials.

• Flash installation allows the creation of an interactive webpage that greets users upon entry.

Implementing Voice Recording Functionality

• JavaScript code is added to enable audio recording on button click; recorded audio is sent back to Python for processing.

• After capturing user input as text, the next step involves integrating a language model (ChatGPT).

Connecting Language Models

• The speaker plans to modify functions that send recorded text to ChatGPT while allowing personality adjustments in responses.

Converting Text Responses into Speech

• Two models will be tested: one simple TTS model and another from Eleven Labs.

• Initial tests show functionality but require further refinement in voice quality.

Exploring Voice Options

What Makes Your Code a Disaster?

Initial Feedback on Code Quality

• The assistant provides a candid assessment of the user's code, describing it as "a total disaster" filled with errors and poor practices.

• Emphasizes the need for memory retention in conversations to enhance interaction quality, although it's not necessary for simple command-response scenarios.

Understanding User Intent

• Discusses the challenge of interpreting user commands to execute specific actions like checking the weather or controlling devices.

• Suggests using structured prompts to clarify user intentions, providing an example where users select from predefined options.

Limitations of Current API Capabilities

• Highlights that the current GPT API lacks real-time information access, which affects its ability to provide accurate weather updates.

• Notes potential responses from the API when asked about current weather, including outdated information or random temperatures due to lack of internet access.

Implementing Functionality with Model 0613

Utilizing Functions in Conversations

• Introduces model 0613's capability to call functions based on user input, enhancing response accuracy and relevance.

• Describes how to define functions such as get_weather and send_email, specifying parameters for each function.

Function Call Mechanism

• Explains how setting function_call to auto allows the model to decide whether to invoke a function based on user requests.

• Demonstrates that if a function is detected in user input, the model will return details needed for execution rather than just a standard response.

Practical Examples of Function Implementation

Sending Emails via Assistant

• Shows an example where the assistant is prompted to send an email; it responds by indicating it will call the appropriate function with specified parameters.

Weather Functionality Test

• Tests functionality by asking for current weather in Monterrey; confirms that it recognizes this as a request for calling get_weather.

Handling Non-Specific Requests

Response Behavior Without Defined Actions

• If no specific action is defined (e.g., turning on lights), the assistant correctly indicates there are no actions available based on provided input.

Organizing Code Structure

Improving Code Readability and Maintenance

• Discusses restructuring code into classes and objects for better organization after initial implementation became unwieldy.

Connecting Real APIs

• Plans to create a class that connects with real APIs (like weather services), allowing dynamic data retrieval based on user queries.

Exploring Current Weather and Smart Assistant Capabilities

Understanding the Assistant's Functionality

• The speaker discusses querying the current weather in Monterrey, emphasizing a more integrated approach by providing both the function name and its response to receive natural language output.

• The assistant responds with the current temperature in Monterrey, showcasing its ability to provide real-time information based on user requests.

• When asked about an action not configured (like feeding dogs), the assistant admits it has no idea what is being requested, highlighting limitations in its programming.

Integration with Smart Home Devices