Build a fully functional voice-powered AI chatbot step-by-step. ๐ค Learn how to create a voice-powered AI chatbot using OpenAI's LLM Live! Complete tutorial from setup to deployment.
Prerequisites
Before starting, make sure you have the following ready:
- โ Python 3.8+: Ensure you have Python installed. Check with
python --version. - โ OpenAI API Key: Sign up at platform.openai.com to get your API key.
- โ Basic Coding Knowledge: Familiarity with Python and basic programming concepts.
- โ Text Editor: VS Code, PyCharm, or any code editor of your choice.
OpenAI Setup
Step 1: Create OpenAI Account
Visit platform.openai.com/signup and sign up for an account.
Step 2: Generate API Key
Navigate to the API Keys section and create a new secret key. Save it securely!
โ ๏ธ Important: Never share your API key or commit it to public repositories!
Step 3: Install Required Packages
pip install openai python-dotenv pyaudio
Building the Chatbot
1. Create Environment File
Create a .env file to store your API key securely:
OPENAI_API_KEY=your_api_key_here
2. Initialize OpenAI Client
from openai import OpenAI
import os
from dotenv import load_dotenv
load_dotenv()
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
3. Create Chat Function
def chat(message, history=[]):
response = client.chat.completions.create(
model="gpt-4",
messages=history + [{"role": "user", "content": message}]
)
return response.choices[0].message.content
4. Add Conversation Loop
history = []
while True:
user_input = input("You: ")
if user_input.lower() == "exit":
break
response = chat(user_input, history)
print(f"Bot: {response}")
history.append({"role": "user", "content": user_input})
history.append({"role": "assistant", "content": response})
Adding Voice Features
๐ค Speech-to-Text
Use OpenAI's Whisper model to convert voice input to text:
def transcribe_audio(audio_file):
transcript = client.audio.transcriptions.create(
model="whisper-1",
file=audio_file
)
return transcript.text
๐ Text-to-Speech
Convert bot responses to natural-sounding voice:
def text_to_speech(text):
response = client.audio.speech.create(
model="tts-1",
voice="alloy",
input=text
)
response.stream_to_file("output.mp3")
Testing & Debugging
- โ Test Basic Conversation: Start with simple queries.
- โ Check Memory Retention: Verify history is maintained.
- โ Test Voice Features: Test speech-to-text and text-to-speech.
- โ Handle Errors Gracefully: Add try-catch blocks for API errors.
Deployment Options
- ๐ Render: Free hosting for Python apps.
- โ๏ธ Vercel: Serverless deployment with automatic scaling.
- ๐ป Replit: Code and host in one place.
- ๐ณ Docker + AWS: Production-grade deployment.
Start Building Your AI Chatbot!
You now have all the knowledge to create amazing conversational AI. Time to build something incredible!