AI-Powered Chatbot Using GPT-3 in Python

In this tutorial, we'll build an AI-powered chatbot using OpenAI's GPT-3.5-turbo. Unlike rule-based chatbots, GPT-3 understands natural language and provides human-like responses by leveraging deep learning.

1. Install Dependencies

To use GPT-3, install the OpenAI Python library:

pip install openai

Additionally, make sure you have an OpenAI API key. You can get one by signing up at OpenAI.

2. Understanding How GPT-3 Chatbots Work

How GPT-3 Chatbot is Different from Rule-Based Bots

3. Python Code for AI Chatbot Using GPT-3

import openai

# Function to communicate with GPT-3
def chat_with_gpt(prompt):
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": prompt}]
    )
    return response["choices"][0]["message"]["content"]

print("Chatbot: Hello! Type 'bye' to exit.")
while True:
    user_input = input("You: ")
    if user_input.lower() == "bye":
        print("Chatbot: Goodbye!")
        break
    response = chat_with_gpt(user_input)
    print("Chatbot:", response)

4. Detailed Explanation of the Code

A. Importing the Required Library

import openai

• The openai library allows us to communicate with the GPT-3 API.

B. Defining the Function to Send Requests to GPT-3

def chat_with_gpt(prompt):
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": prompt}]
    )
    return response["choices"][0]["message"]["content"]

• The openai.ChatCompletion.create() function sends the user’s message to GPT-3.
• We specify:
  • model="gpt-3.5-turbo" → Uses OpenAI's conversational AI model.
  • messages=[{"role": "user", "content": prompt}] → Sends the user input to GPT-3.
• The response is extracted and returned.

C. Implementing a ChatBot

print("Chatbot: Hello! Type 'bye' to exit.")
while True:
    user_input = input("You: ")
    if user_input.lower() == "bye":
        print("Chatbot: Goodbye!")
        break
    response = chat_with_gpt(user_input)
    print("Chatbot:", response)

• The chatbot runs continuously until the user types "bye".
• It sends the user input to GPT-3 and prints the response.

5. Running the Chatbot

When you execute the script, the chatbot will interact like this:

Chatbot: Hello! Type 'bye' to exit.
You: Hi
Chatbot: Hello! How can I assist you today?
You: What is the capital of France?
Chatbot: The capital of France is Paris.
You: Can you tell me a joke?
Chatbot: Sure! Why don’t skeletons fight each other? Because they don’t have the guts!
You: bye
Chatbot: Goodbye!

6. Enhancing the Chatbot

A. Remembering Context in Conversations

To keep track of previous messages, we can modify the chatbot to use a list of messages:

import openai

def chat_with_gpt(conversation_history):
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=conversation_history
    )
    return response["choices"][0]["message"]["content"]

print("Chatbot: Hello! Type 'bye' to exit.")
conversation_history = [{"role": "system", "content": "You are a helpful chatbot."}]

while True:
    user_input = input("You: ")
    if user_input.lower() == "bye":
        print("Chatbot: Goodbye!")
        break
    conversation_history.append({"role": "user", "content": user_input})
    response = chat_with_gpt(conversation_history)
    conversation_history.append({"role": "assistant", "content": response})
    print("Chatbot:", response)

Now, the chatbot remembers past interactions.

B. Customizing GPT-3’s Personality

You can customize GPT-3’s behavior by modifying the system message:

conversation_history = [
    {"role": "system", "content": "You are a funny chatbot who loves making jokes."}
]

This makes the chatbot respond humorously.

C. Adding an API Key Securely

Instead of storing the API key in code, store it as an environment variable:

import openai
import os

openai.api_key = os.getenv("OPENAI_API_KEY")

This prevents security risks.

7. Comparison: GPT-3 vs Rule-Based Chatbots

8. Use Cases for AI Chatbots

 Customer Support – Provide instant responses
 Virtual Assistants – Answer general knowledge questions
 E-learning – Help students with programming & subjects
 Entertainment – Tell jokes, generate stories

9. Conclusion

With GPT-3, we built an intelligent chatbot capable of understanding natural conversations. Unlike rule-based chatbots, GPT-3 can adapt, generate creative responses, and remember past interactions.

Next Steps:
 Improve responses using fine-tuning
 Deploy it as a web-based chatbot using Flask or FastAPI
 Connect it to WhatsApp, Telegram, or Discord