The AI Image Classifier is a simple and interactive web application that allows users to upload images and instantly classify them using a pre-trained deep learning model.
This project was built to explore practical AI usage—taking a powerful model and wrapping it in a clean, accessible interface that anyone can use without ML knowledge.
Why I Built This
I wanted to understand how pre-trained AI models can be:
- Integrated into real applications
- Used without training from scratch
- Presented through a clean, user-friendly interface
Instead of focusing on model training, this project emphasizes application-level AI integration, which is how AI is often used in real-world products.
What the App Does
- Users upload an image (JPG, JPEG, or PNG)
- The image is processed and passed to a deep learning model
- The app returns the top 3 predictions along with confidence scores
- Results are displayed instantly in a clean UI
The experience is designed to be fast, simple, and intuitive.
Core Features
Image Upload & Classification
- Supports common image formats
- Instant inference after upload
Pre-trained AI Model
- Uses MobileNetV2, trained on ImageNet
- Lightweight and efficient for fast predictions
- No custom training required
Top Predictions
- Displays the top 3 predicted classes
- Shows confidence percentages for transparency
Clean & Interactive UI
- Built with Streamlit
- Minimal layout focused on usability
Tech Stack
- Python – Core language
- Streamlit – Web interface
- TensorFlow / Keras – Deep learning framework
- MobileNetV2 – Pre-trained image classification model
How It Works (High Level)
- User uploads an image
- Image is resized and preprocessed
- MobileNetV2 performs inference
- Predictions are decoded and ranked
- Top 3 results are displayed with confidence scores
This mirrors how many production AI features work behind the scenes.
What I Learned
Through this project, I gained a clearer understanding of:
- Using pre-trained deep learning models effectively
- Image preprocessing for inference
- Deploying AI functionality through simple web apps
- Making AI outputs understandable for non-technical users
It reinforced the idea that AI is most valuable when it's accessible.
Project Status
The application is complete and functional. Possible future improvements include:
- Support for camera input
- Batch image classification
- Model comparison or confidence visualization