Free Gemini AI Chatbot Like ChatGPT with Python

Artificial Intelligence chatbots are transforming the way developers build AI applications. Tools like ChatGPT and Copilot have shown how powerful Interactive AI can be in communicating with user prompts in real time. In the present web world, there are plenty of AI APIs available, some of the most commonly used (OpenAI API, Anthropic Claude API, Hugging Face Inference API) and many more.. But did you know you can build your own chatbot for free using Google Gemini API? Let’s start

In this tutorial, you will learn how to create a Free Gemini AI Chatbot Like ChatGPT with Python step by step. By the end of this user guide, you will have a working AI chatbot that can respond to user questions or prompts using Google’s Gemini model.

This guide is beginner-friendly and perfect for AI engineers, Python developers, and prompt engineers who want to build real AI applications.

Requirements to Create a Gemini AI Chatbot

Before building the free gemini chatbot tutorial, make sure you have the following installed in your IDE (in this tutorial iam using Visual Studio Code (VSCODE) :

Python 3.9 or later
Visual Studio Code (or any Python IDE)
A free API key from Google AI Studio
Internet connection

Practical tutorial to build a gemini ai chatbot like chatgpt

Let’s create a real-time, practical Google Gemini AI Python chatbot like ChatGPT. First, let’s open the Gemini AI Studio API to create a secret API key. For that, you need to log in with your valid Gmail account.

Here is the link to visit AI Studio google

Now, after logging in successfully, in the left-side panel called Gemini AI Studio, at the bottom, you can find a ‘Get API Key’ option; click on it. It will direct to the API key page.

Here, we can create a new free API key by clicking the button at the top of the page labelled “Create API Key” with a key symbol. Click that one. Give the name of the key or leave it with the default name “Gemini API Key”. Iam using the default name only

Note: if you have any doubts about creating, Watch the Full Gemini AI Chatbot Python Tutorial YouTube

If you want to explore more AI projects, learn more in our Python AI tutorial.

Install Required Libraries

pip install google-genai python-dotenv

These packages must be installed to allow Python to connect securely with the Google Gemini API.

Here, one more important step to secure your apikey , don't add your secret API key directly into your Python code. Always create a separate file called .env (without any file name, only the extension), and both ".py" and ".env" files must be in the same folder and in the .env file, the code is without any single or double codes. load_dotenv() reads the api key which loads the environment from a .env file. So, we can access our secret API key securely in our project using a header package called load_dotev with an import statement

So, first create two files in VSCODE (.env, free-api.py) file

Below code for .env file

GEMINI_API_KEY=YOUR-SECRET-KEY

Python Code (free-api.py) : how to build chatpgt gemini chatbot python free

from google import genai
from dotenv import load_dotenv
import os

load_dotenv()
client= genai.Client(api_key=os.getenv("GEMINI_API_KEY"))

while True:
    user_input=input("You :")
    if user_input.lower() == "exit":
        break
    response = client.models.generate_content(model="gemini-2.5-flash", contents=user_input)
    print(" chatbot :", response.text)

Below, is the output
PS C:\wamp64\www\Algo> PYTHON free-api.py
You :hi
chatbot : Hi there! How can I help you today?
You :what is the capital of india
chatbot : The capital of India is New Delhi.
You :

How the Chatbot Works

The chatbot works when run the above .py code. It prompts the user You: when the user enters any kind of necessary message then that message python sends to the Gemini API and gemini generated a response according to the user prompts. so, technically how this build ai chatbot with python code works

This Python script creates a simple AI chatbot using the Google Gemini API. First, it imports required modules and loads environment variables using python-dotenv, which securely stores the API key in a .env file. The genai.Client is then initialized with the GEMINI_API_KEY to authenticate requests. Inside the while True loop, the program continuously asks the user for input. If the user types “exit”, the loop stops. Otherwise, the input is sent to the Gemini model (gemini-2.5-flash) using generate_content(). The AI processes the prompt and returns a response, which is printed in the terminal as the chatbot’s reply.

Note: No AI chatbot gives 100% correct answer, so be careful while using the responses

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Job-Based Interview Questions on Gemini AI Chatbot with Python

1) How can you store securely an API key in a Python AI application? explain in short

In a Python AI project, the API key should not be written directly added in the code. Instead, developers usually store it in a .env file and load it using the python-dotenv package. This method keeps the API key secure and prevents it from being exposed when sharing the project or uploading to a servers or repository..

2) How does a Python chatbot communicate with the Gemini AI model?

A Python chatbot communicates with the AI model through the Gemini API. The application sends user prompts to the model using functions like generate_content(). The Gemini model processes the prompt and returns an AI-generated response, which is then displayed to the user.
client.models.generate_content(model="gemini-2.5-flash", contents=user_input)

3) What is the purpose of using the `load_dotenv()` function in a Python project?

The load_dotenv() function loads environment variables from a .env file into the application. This allows developers to store sensitive information such as API keys securely outside the main code. Using environment variables improves security and prevents exposing secrets when sharing the project. below with an example

load_dotenv() client= genai.Client(api_key=os.getenv("GEMINI_API_KEY"))

Conclusion: Building a Gemini AI chatbot with Python is a great way to learn how modern AI applications work. With just a few lines of code, developers can create interactive AI chatbots similar to ChatGPT Which we created. This project can be extended further by adding a web interface, storing conversation history, or integrating the chatbot into websites.