diff --git a/AI/model/ml_transcation.ipynb b/AI/model/ml_transcation.ipynb
new file mode 100644
index 0000000..3fef66b
--- /dev/null
+++ b/AI/model/ml_transcation.ipynb
@@ -0,0 +1,227 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv(\"transcations.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a074a45e",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df = df[['Description', 'Category']].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe26b20f",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df[['Description', 'Category']].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44c3995d",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df['Description'] = df['Description'].str.lower().str.replace('[^a-z\\s]', '', regex=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82245456",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df['Category'] = df['Category'].str.lower().str.replace('[^a-z\\s]', '', regex=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "620ef271",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X = df['Description']\n",
+    "y = df['Category']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3eebf352",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "\n",
+    "vectorizer = TfidfVectorizer()\n",
+    "X_train_vec = vectorizer.fit_transform(X_train)\n",
+    "X_test_vec = vectorizer.transform(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75a141a8",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "\n",
+    "model = MultinomialNB()\n",
+    "model.fit(X_train_vec, y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "184354db",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def predict_category(text):\n",
+    "    text = [text.lower()]\n",
+    "    text_vec = vectorizer.transform(text)\n",
+    "    return model.predict(text_vec)[0]\n",
+    "\n",
+    "# Example\n",
+    "print(predict_category(\"Netflix\"))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c30b9f27",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "y_pred = model.predict(X_test_vec)\n",
+    "print(classification_report(y_test, y_pred))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "109a12d5",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "# Save model\n",
+    "with open('model.pkl', 'wb') as f:\n",
+    "    pickle.dump(model, f)\n",
+    "\n",
+    "# Save vectorizer\n",
+    "with open('vectorizer.pkl', 'wb') as f:\n",
+    "    pickle.dump(vectorizer, f)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcb76b6a",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import joblib\n",
+    "\n",
+    "joblib.dump(model, 'model.pkl')\n",
+    "joblib.dump(vectorizer, 'vectorizer.pkl')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f16d6da5",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from google.colab import files\n",
+    "files.download('model.pkl')\n",
+    "files.download('vectorizer.pkl')\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Backend/index.js b/Backend/index.js
index 53ecd34..1e41a9c 100644
--- a/Backend/index.js
+++ b/Backend/index.js
@@ -160,7 +160,7 @@ app.post('/api/transaction/add', async (req, res) => {
     const { description, amount, userId } = req.body;
     if (!userId) return res.status(400).json({ error: 'Missing userId' });
 
-    const predictRes = await axios.post('http://192.168.1.101:5000/api/predict', {
+    const predictRes = await axios.post('http://10.231.41.181:5000/api/predict', {
       description,
     });
     const category = predictRes.data.category || 'Other';
@@ -227,7 +227,7 @@ app.post('/api/transactions/voice', async (req, res) => {
       .trim();
     const description = cleaned || 'misc';
     // Step 3: Predict category using your Flask API
-    const predictRes = await axios.post('http://192.168.1.101:5000/api/predict', {
+    const predictRes = await axios.post('http://10.231.41.181:5000/api/predict', {
       description,
     });
     const category = predictRes.data?.category || 'Other';
@@ -394,7 +394,7 @@ app.post('/api/sync-gmail', async (req, res) => {
 
       let category = 'Other';
       try {
-        const predictRes = await axios.post('http://192.168.1.101:5000/api/predict', {
+        const predictRes = await axios.post('http://10.231.41.181:5000/api/predict', {
           description: parsed.vendor || "Unknown"
         }, { timeout: 5000 }); // Optional timeout
 
diff --git a/Frontend/lib/other_pages/enviroment.dart b/Frontend/lib/other_pages/enviroment.dart
index 3cca84a..6e546ae 100644
--- a/Frontend/lib/other_pages/enviroment.dart
+++ b/Frontend/lib/other_pages/enviroment.dart
@@ -1,5 +1,5 @@
 class Environment {
-  static const String baseUrl = 'http://192.168.1.101:3000';
+  static const String baseUrl = 'http://10.231.41.181:3000';
   static const String serverClientId =
       "278433619849-cg6f7gk5cc45rgu1ojrlkf794lm4udgn.apps.googleusercontent.com";
 }