PalmOilRipenessDetection-Python

🌴 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

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

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🛠 Prerequisites

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

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📦 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

1. 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.
2. Extract into /datasets.

3. Train the model:

   python train_script.py
   
   

   1. Copy the resulting best.pt from runs/detect/train/weights/ to the project root.

   Running the API Server (DDD Structure)

   The new architecture decouples the vision logic from the API entry point. You can run it either via the root wrapper or directly as a module:

   # Option 1: Using the root wrapper
   python main.py
   
   # Option 2: Running as a module
   python -m src.api.main
   

By default, the server runs on http://localhost:8000.

Available Endpoints

Endpoint	Method	Description
/detect	POST	Simple YOLO detection (Returns JSON)
/analyze	POST	Detection + Vertex Vectorization + MongoDB Archival
/get_confidence	GET	Returns the current model confidence setting
/set_confidence	POST	Updates the global model confidence setting

Running the Streamlit Dashboard

The Streamlit app still provides the user interface for manual testing.

# Run the Streamlit app
streamlit run demo_app.py
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## 📂 Repository Structure

```text
├── 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