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+# ☕ Coffee Brewing Level Prediction Based on Spectroscopy Data Using Deep Learning
+
+This repository contains the complete pipeline for a thesis project that predicts **coffee brewing levels** using **spectroscopy data** and **deep learning models**, later deployed on a **Raspberry Pi** with a real-time interface using **OLED display** and a **physical button**.
+
+---
+
+## 📚 Description
+
+The goal of this project is to classify brewed coffee into categories such as **Underdeveloped**, **Ideal**, or **Bitter**, based on spectral data obtained using the **AS7265x spectroscopy sensor**. A deep learning model is trained using Python and TensorFlow, then converted into **TensorFlow Lite** and deployed on a physical prototype for real-time prediction.
+
+---
+
+## 🧪 Data Collection
+
+- **Sensor**: [AS7265x Triad Spectral Sensor](https://www.sparkfun.com/sparkfun-triad-spectroscopy-sensor-as7265x-qwiic.html)
+- **Platform**: Raspberry Pi
+- **Categories**: Coffee brewing levels
+- **Format**: Raw 18-channel spectral data saved in `.csv` format
+
+---
+
+## 🧠 Modeling
+
+### Main Libraries for Modeling:
+- `pandas`: data manipulation and preprocessing  
+- `seaborn`: data visualization and EDA  
+- `tensorflow`: deep learning model training and conversion to `.tflite`  
+- `numpy`: numerical operations  
+
+---
+
+## 📟 Prototype Deployment
+
+A physical prototype is built using:
+
+- **Raspberry Pi**
+- **AS7265x Spectroscopy Sensor**
+- **SSD1306 OLED Display**
+- **Push Button**
+- **TensorFlow Lite Interpreter**