Deploy Your AI App for Free in 3 Clicks

If you read my articles last week, you built The Vibe Checker, a sentiment analysis tool that runs on your computer. You saw balloons appear when you typed something positive. That was exciting! Today, I’ll show you what to do next. In this article, I’ll explain how to deploy your AI app in the cloud for free using GitHub and Streamlit.

Deploy Your AI App for Free

We’ll take your Streamlit AI app and put it on the web so anyone with internet can use it, all for free with Streamlit Community Cloud. This is more than just hosting; this is sharing your work with the world.

Before you continue, check out my previous article where we built the user interface for the AI app you’ll deploy today.

Step 1: Add requirements.txt

Most beginners run into trouble here. You need a file called requirements.txt that lists the libraries your app needs on the cloud server.

For our Vibe Checker app, your requirements.txt file needs to look exactly like this:

streamlit
transformers
torch

If you don’t include this file, the cloud server will try to run your Python script, but it will crash right away because it won’t recognize the import streamlit line. You’ll end up looking at an error log. Always include your requirements!

Step 2: Set up The Project on GitHub

Streamlit Community Cloud gets your code straight from GitHub. If you don’t have a GitHub account yet, this is a great time to sign up. GitHub is the main place developers store their code.

Start by making a new repository. Log in to GitHub, click the + icon, and create a new repository. You can name it vibe-checker-app.

Next, upload your files. You’ll need to add two files:

1. app.py (The Python code)
2. requirements.txt (The text file we just discussed)

After you upload the files, click “Commit changes.”

To make sure we’re on the same page, here’s the full code you should save as app.py. This file is the core of your app:

import streamlit as st
from transformers import pipeline

# 1. Title and Description
st.title("🧠 The Vibe Checker")
st.write("Enter any text below, and I'll use AI to tell you if it's **Positive** or **Negative**.")

# 2. Load the AI Model (Cached)
@st.cache_resource
def load_model():
    # We use a standard 'sentiment-analysis' pipeline from Hugging Face.
    # It downloads a small, efficient model (distilbert) by default.
    return pipeline("sentiment-analysis")

# Load the model instantly
with st.spinner("Loading AI Brain..."):
    classifier = load_model()

# 3. User Input
user_text = st.text_area("What's on your mind?", placeholder="Type something like: 'I love coding with Python!'")

# 4. The Button and Result
if st.button("Analyze Vibe"):
    if user_text.strip(): # Check if text is not empty
        # Pass the text to the model
        result = classifier(user_text)
        
        # The model returns a list like [{'label': 'POSITIVE', 'score': 0.99}]
        label = result[0]['label']
        score = result[0]['score']
        
        # Display the result with some dynamic flair
        if label == "POSITIVE":
            st.success(f"**Positive Vibe Detected!** (Confidence: {score:.2f})")
            st.balloons()  # Fun animation
        else:
            st.error(f"**Negative Vibe Detected.** (Confidence: {score:.2f})")
    else:
        st.warning("Please enter some text first!")

Step 3: The Deployment

Now, go to Streamlit Community Cloud. Visit share.streamlit.io and sign in with your GitHub account.

Next, Click on “Create App”:

You will see a list of repositories. Select your vibe-checker-app repository from the list. Make sure the branch is set to main, and the file path is app.py.

Finally, click “Deploy.”

You’ll see a baking animation. This means the server is reading your requirements.txt, installing PyTorch and Transformers, and getting everything ready.

After about 1 to 3 minutes, the animation will stop, and your app will appear. Here’s the live link to your deployed app.

Closing Thoughts

That’s how you can deploy your AI app online for free using GitHub and Streamlit.

Something changes when you deploy your first app. Before this, you just used AI tools like ChatGPT or scrolled through recommendations. Now, you’ve created your own space online where you control the AI.

If you found this article useful, you can follow me on Instagram for daily AI tips and practical resources. You might also like my latest book, Hands-On GenAI, LLMs & AI Agents. It’s a step-by-step guide to help you get ready for jobs in today’s AI field.