Restore history and apply final clean deployment

.gitignore

@@ -212,3 +212,6 @@ final_model/
 deploy_stage/
 mlruns/
 Network_data/
+
+mlflow.db
+*.db

README.md

@@ -161,10 +161,10 @@ Trains and tunes multiple models, selecting the best one based on F1-score/Accur
 4. **Set Environment Variables**
    Create a `.env` file with your credentials:
    ```env
-   MONGO_DB_URL=mongodb+srv://<username>:<password>@cluster0.mongodb.net/?retryWrites=true&w=majority
-   MLFLOW_TRACKING_URI=https://dagshub.com/<username>/NetworkSecurity.mlflow
-   MLFLOW_TRACKING_USERNAME=<username>
-   MLFLOW_TRACKING_PASSWORD=<password>
+   MONGO_DB_URL=your_mongodb_url_here
+   MLFLOW_TRACKING_URI=https://dagshub.com/your_username/project.mlflow
+   MLFLOW_TRACKING_USERNAME=your_username
+   MLFLOW_TRACKING_PASSWORD=your_password
    ```
 
 ## 🚀 Usage

test.py

@@ -47,7 +47,7 @@ def test_single_prediction():
         "Domain_registeration_length": 1,
         "Favicon": 1,
         "port": 1,
-        "HTTPS_token": 1,
+        "HTTPS_token": 0,
         "Request_URL": 1,
         "URL_of_Anchor": 1,
         "Links_in_tags": 1,

test_mongodb.py

@@ -1,13 +0,0 @@
-
-from pymongo.mongo_client import MongoClient
-
-uri = "mongodb+srv://inder26112004_db_user:<db_password>@cluster0.bauklqw.mongodb.net/?appName=Cluster0"
-# Create a new client and connect to the server
-client = MongoClient(uri)
-
-# Send a ping to confirm a successful connection
-try:
-    client.admin.command('ping')
-    print("Pinged your deployment. You successfully connected to MongoDB!")
-except Exception as e:
-    print(e)