README.md

metadata

title: CV InsectClassifier
emoji: 🐞
colorFrom: gray
colorTo: green
sdk: docker
pinned: false

🐞 Insect Classifier - FastAPI

A web-based insect classification app using two TensorFlow models (CNN and MobileNet), deployed with FastAPI. Users can upload insect images and receive predictions from both models, along with accuracy and descriptions.

🧠 Models

• CNN model (ProyekCV_model.h5)
• MobileNet model (ProyekCV_model_v2.h5)

⚙️ Tech Stack

• FastAPI
• TensorFlow & Keras
• HTML/CSS (for frontend)
• Uvicorn (as ASGI server)
• Python
• NumPy

- Pandas # Kamu tidak pakai Pandas di main.py, sebaiknya dihapus dari daftar

- Matplotlib & Seaborn # Kamu tidak pakai ini untuk runtime aplikasi, sebaiknya dihapus atau pindah ke "Development Dependencies"

- Scikit-learn # Kamu tidak pakai ini, sebaiknya dihapus

📦 Dataset

This project uses the Insects Recognition Dataset by Hammaad Ali, available on Kaggle.

Dataset Features:

• Contains high-quality images of 5 different insect classes. There are grasshopper, butterfly, mosquito, ladybird and dragonfly.
• Organized into labeled folders for each class.
• Ideal for supervised image classification tasks.
• Image format: .jpg

The dataset was used to train both the CNN and MobileNet models included in this project.

🚀 How to Run Locally

Make sure you have all dependencies installed and your virtual environment activated.

Step 1: Start the FastAPI backend Open a terminal and run:

venv\Scripts\activate
uvicorn main:app --reload --host 0.0.0.0 --port 8000 # <-- PERBAIKI INI: sesuaikan dengan command lokal mu