chore: update readme

README.md

@@ -1,2 +1,87 @@
-# sikwati-ai-backend
-backend repository of sikwati ai
+# **Sikwati-AI API**
+This is a Flask-based API for classifying cacao pod images using an EfficientNetB0 model. The API provides an endpoint to classify an uploaded image and returns the predicted class along with confidence scores.
+
+## **Getting Started**
+### **Prerequisites**
+- Python 3.x
+- Flask
+- Flask-CORS
+- TensorFlow
+- scikit-learn
+- scikit-image
+
+### **Installation**
+1. Clone the repository
+```bash
+git clone https://github.com/louispawaon/sikwati-ai-backend.git
+cd sikwati-ai-backend
+```
+2. Create a virtual environment (*optional but recommended*)
+```bash
+python -m venv venv
+source venv/bin/activate
+```
+3. Install Python dependencies
+```bash
+pip install -r requirements.txt
+```
+4. Run the application
+```bash
+gunicorn app:app #For Production
+flask run --debug #For Debugging Mode
+```
+5. (*Optional*) Expose the local server using Ngrok
+```bash
+ngrok http 5000
+```
+Ngrok will provide a public URL (e.g., https://your-ngrok-subdomain.ngrok.io) that you can use to access your API from anywhere.
+
+## **Usage**
+
+### **Home Endpoint**
+- Endpoint: `/`
+- Description: Welcome to Sikwati AI API
+- Method: `GET`
+- Response:
+```json
+{
+  "message": "Welcome to the Cacao Pod Classification API"
+}
+```
+
+### **Classify Image Endpoint**
+- Endpoint: `/api/classify_image`
+- Description: Classify an uploaded cacao pod image
+- Method: `POST`
+- Request Parameters:
+    - `image` (*type*: file, *required*: true) - The cacao pod image to be classified
+- Responses:
+  - 200 OK
+    ```json
+    {
+        "class": "predicted_class",
+        "confidence": 0.85
+    }
+    ```
+  - 400 Bad Request
+    ```json
+    {
+        "error": "No file part"
+    }
+    ```
+    ```json
+    {
+        "error": "No selected file"
+    }
+    ```
+    ```json
+    {
+        "error": "The image may not contain a cacao pod"
+    }
+    ```
+
+## **Model Details**
+- The classification model is based on the EfficientNetB0 CNN Architecture
+
+## **Author**
+[Louis Miguel Pawaon](https://twitter.com/miggy_pawaon)