diff --git a/Dockerfile b/Dockerfile
index e594259..e300033 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -3,7 +3,9 @@ FROM python:3.11-slim
 WORKDIR /app
 
 COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
+
+RUN apt-get update && apt-get install -y build-essential \
+    && pip install --no-cache-dir -r requirements.txt
 
 COPY ./src ./src
 
diff --git a/requirements.txt b/requirements.txt
index 8fdca55..02d6e7e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,3 +5,5 @@ Flask==2.2.5
 Flask-SQLAlchemy==2.5.1
 SQLAlchemy==1.4.22
 psycopg2-binary==2.9.10
+numpy==1.24.1
+tensorflow==2.17.0
diff --git a/src/api/color_api.py b/src/api/color_api.py
index 42bf6d1..d156785 100644
--- a/src/api/color_api.py
+++ b/src/api/color_api.py
@@ -4,11 +4,14 @@
 from flask import Blueprint
 from database.models.color import Color
 from database.database import db
+from models.lstm_color_predictor import train_model, predict_next_color
 
 color_api_bp = Blueprint('color_api', __name__)
 socketio = SocketIO(cors_allowed_origins="*")
 logger = logging.getLogger(__name__)
 
+color_buffer = []
+
 COLOR_MAP = {
     "rgb(0, 0, 255)": "blue",
     "rgb(0, 255, 255)": "blue-green",
@@ -37,6 +40,10 @@ def get_color_name(color_str):
     return COLOR_MAP.get(color_str, "unknown")
 
 
+def map_rgb_to_color_name(rgb_value):
+    closest_color = min(COLOR_MAP.keys(), key=lambda x: sum(abs(int(c) - v) for c, v in zip(x[4:-1].split(', '), rgb_value)))
+    return COLOR_MAP[closest_color]
+
 @socketio.on('send_color')
 def handle_receive_color(data):
     color_str = data.get('color')
@@ -55,5 +62,24 @@ def handle_receive_color(data):
         )
         db.session.add(new_color)
         db.session.commit()
+
+        color_buffer.append(new_color)
+
+        print(f"Buffer len: {len(color_buffer)}")
+
+        if len(color_buffer) == 9:
+            predicted_rgb = predict_next_color()
+            predicted_color_name = map_rgb_to_color_name(predicted_rgb)
+
+            logger.info(f"Predicted next color (RGB): {predicted_color_name} {predicted_rgb}")
+            print(f"Predicted next color (RGB): {predicted_color_name} {predicted_rgb}")
+
+            train_model(n=10)
+
+            color_buffer.clear()
+
+        if len(color_buffer) == 10:
+            color_buffer.clear()
+
     except ValueError as e:
         logger.error(f"Error processing color: {e}")
diff --git a/src/models/__init__.py b/src/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/models/lstm_color_predictor.py b/src/models/lstm_color_predictor.py
new file mode 100644
index 0000000..bae389a
--- /dev/null
+++ b/src/models/lstm_color_predictor.py
@@ -0,0 +1,39 @@
+import numpy as np
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense
+from tensorflow.keras.optimizers import Adam
+from database.models.color import Color
+
+def create_model(input_shape):
+    model = Sequential()
+    model.add(LSTM(50, activation='relu', input_shape=input_shape))
+    model.add(Dense(3, activation='sigmoid'))
+    model.compile(optimizer=Adam(learning_rate=0.001), loss='mse')
+    return model
+
+
+model = create_model((None, 3))
+
+
+def get_last_n_colors(n):
+    colors = Color.query.order_by(Color.timestamp.desc()).limit(n).all()
+    colors.reverse()
+    data = np.array([[c.red, c.green, c.blue] for c in colors]) / 255.0
+    return data
+
+
+def train_model(n):
+    data = get_last_n_colors(n)
+    if len(data) > 1:
+        X, y = data[:-1], data[1:]
+        X = X.reshape((1, X.shape[0], X.shape[1]))
+        y = y.reshape((1, y.shape[0], y.shape[1]))
+        model.fit(X, y, epochs=1, verbose=0)
+
+
+def predict_next_color():
+    last_colors = get_last_n_colors(1)
+    if last_colors.size > 0:
+        prediction = model.predict(last_colors.reshape((1, 1, 3)))
+        rgb_values = (prediction[0] * 255).astype(int).flatten()
+        return tuple(rgb_values)