🌴 Palm Oil Ripeness Agent (n8n + YOLOv8)

This project uses a custom-trained YOLOv8 model to detect the ripeness of Palm Oil Fresh Fruit Bunches (FFB). It features a local Python FastAPI server and a Streamlit Dashboard, both integrated into an agentic n8n workflow.

🚀 Project Overview

Vision Engine: YOLOv8 Nano (Custom-trained on MPOB-standard datasets).
Inference Server: FastAPI (Python) for n8n integration.
Demo Dashboard: Streamlit UI for drag-and-drop batch testing.
Intelligence: MongoDB Atlas Vector Search for similarity-based reasoning.

🛠 Prerequisites

Python 3.10+
n8n (Desktop or Self-hosted)
MongoDB Atlas Account (with Vector Search index enabled)

📦 Setup Instructions

1. Environment Setup

git clone <your-repo-url>
cd palm-oil-ai
python -m venv venv
# Windows: venv\Scripts\activate | Mac: source venv/bin/activate
pip install ultralytics fastapi uvicorn streamlit python-multipart pillow

2. Dataset & Training

Download the dataset from Roboflow *Or you can also find your own separate source of datasets. Make sure the file/folder format structure is consistent, especially the .yaml file.
Extract into /datasets.
Train the model:
```
python train_script.py
```
1. Copy the resulting best.pt from runs/detect/train/weights/ to the project root.
3. Running the Demo (Streamlit)

To show the interactive dashboard to colleagues:
```
streamlit run demo_app.py
```

Local URL: http://localhost:8501

4. Running the API for n8n

To connect your AI to n8n workflows:

python main.py

Endpoint: POST http://localhost:8000/detect
Payload: Form-data with key file.

📂 Repository Structure

├── datasets/           # Labeled images (Train/Valid/Test)
├── runs/               # YOLO training logs and output weights
├── main.py             # FastAPI Inference Server (for n8n)
├── demo_app.py         # Streamlit Dashboard (for demos)
├── train_script.py     # Throttled training configuration
├── best.pt             # THE BRAIN: The trained model weights
└── README.md           # Documentation

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