diff --git a/Combined_Model.ipynb b/Combined_Model.ipynb
deleted file mode 100644
index 13f297b..0000000
--- a/Combined_Model.ipynb
+++ /dev/null
@@ -1,117 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ZELjx8qt4t3u"
-      },
-      "outputs": [
-        {
-          "ename": "",
-          "evalue": "",
-          "output_type": "error",
-          "traceback": [
-            "\u001b[1;31mRunning cells with 'venv (Python 3.13.0)' requires the ipykernel package.\n",
-            "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
-            "\u001b[1;31mCommand: '\"c:/Users/user/Machine learnin+API/mubazir-machine-learning/venv/Scripts/python.exe\" -m pip install ipykernel -U --force-reinstall'"
-          ]
-        }
-      ],
-      "source": [
-        "#PALING BENER\n",
-        "\n",
-        "import os\n",
-        "import numpy as np\n",
-        "import tensorflow as tf\n",
-        "from tensorflow.keras.models import load_model\n",
-        "from tensorflow.keras.utils import load_img, img_to_array\n",
-        "import matplotlib.pyplot as plt\n",
-        "from google.colab import files\n",
-        "\n",
-        "# Load model gabungan\n",
-        "combined_model = load_model('/content/combined_model.h5')\n",
-        "\n",
-        "# Daftar class labels untuk model pertama\n",
-        "class_labels = ['Apel', 'Pisang', 'Paprika', 'Jeruk', 'Wortel', 'Timun']\n",
-        "\n",
-        "# Fungsi untuk prediksi jenis buah\n",
-        "def predict_jenis_buah(model, class_labels, file_name, target_size=(224, 224), confidence_threshold=0.6):\n",
-        "    img = load_img(file_name, target_size=target_size)\n",
-        "    img_array = img_to_array(img)\n",
-        "    img_array = np.expand_dims(img_array, axis=0) / 255.0\n",
-        "    prediction = model.predict([img_array, np.zeros((1, 150, 150, 3))])[0]\n",
-        "    confidence = np.max(prediction)\n",
-        "    print(f\"Predictions: {prediction}, Confidence: {confidence}\")  # Debugging output\n",
-        "    predicted_class_index = np.argmax(prediction)\n",
-        "    if predicted_class_index >= len(class_labels) or confidence < confidence_threshold:\n",
-        "        return \"Cannot be predicted\", confidence\n",
-        "    predicted_label = class_labels[predicted_class_index]\n",
-        "    return predicted_label, confidence\n",
-        "\n",
-        "# Fungsi untuk prediksi kesegaran buah\n",
-        "def predict_kesegaran(model, file_name, target_size=(150, 150)):\n",
-        "    img = load_img(file_name, target_size=target_size)\n",
-        "    img_array = img_to_array(img)\n",
-        "    img_array = np.expand_dims(img_array, axis=0) / 255.0\n",
-        "    prediction = model.predict([np.zeros((1, 224, 224, 3)), img_array])[1]\n",
-        "    print(f\"Kesegaran prediction: {prediction}\")  # Debugging output\n",
-        "    predicted_label = 'Segar' if prediction[0] > 0.01 else 'Tidak Segar'\n",
-        "    return predicted_label\n",
-        "\n",
-        "# Mengunggah dan memprediksi gambar baru\n",
-        "uploaded = files.upload()\n",
-        "for fn in uploaded.keys():\n",
-        "    file_path = fn\n",
-        "    jenis_buah, confidence = predict_jenis_buah(combined_model, class_labels, file_path)\n",
-        "    if jenis_buah == \"Cannot be predicted\":\n",
-        "        kesegaran_buah = \"Cannot be predicted\"\n",
-        "    else:\n",
-        "        kesegaran_buah = predict_kesegaran(combined_model, file_path)\n",
-        "\n",
-        "    # Menampilkan hasil prediksi\n",
-        "    img = load_img(file_path)\n",
-        "    plt.imshow(img)\n",
-        "    plt.title(f'Predicted: {jenis_buah} (Confidence: {confidence:.2f}), Kesegaran: {kesegaran_buah}')\n",
-        "    plt.axis('off')\n",
-        "    plt.show()\n",
-        "\n",
-        "    print(f'Gambar {fn} adalah {jenis_buah}, dan buah tersebut {kesegaran_buah}')\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [
-        {
-          "ename": "",
-          "evalue": "",
-          "output_type": "error",
-          "traceback": [
-            "\u001b[1;31mRunning cells with 'venv (Python 3.13.0)' requires the ipykernel package.\n",
-            "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
-            "\u001b[1;31mCommand: '\"c:/Users/user/Machine learnin+API/mubazir-machine-learning/venv/Scripts/python.exe\" -m pip install ipykernel -U --force-reinstall'"
-          ]
-        }
-      ],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python",
-      "version": "3.13.0"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}