diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
new file mode 100644
index 0000000000000000000000000000000000000000..ec511ca6b0615a88a6b7e56cc74508da0aad6a58
--- /dev/null
+++ b/.github/workflows/main.yml
@@ -0,0 +1,92 @@
+name: workflow
+
+on:
+ push:
+ branches:
+ - main
+ paths-ignore:
+ - 'README.md'
+
+jobs:
+ integration:
+ name: Continuous Integration
+ runs-on: ubuntu-latest
+ steps:
+ - name: Checkout Code
+ uses: actions/checkout@v3
+
+ - name: Lint code
+ run: echo "Linting repository"
+
+ - name: Run unit tests
+ run: echo "Running unit tests"
+
+ build-and-push-ecr-image:
+ name: Continuous Delivery
+ needs: integration
+ runs-on: ubuntu-latest
+ steps:
+ - name: Checkout Code
+ uses: actions/checkout@v3
+
+ - name: Install Utilities
+ run: |
+ sudo apt-get update
+ sudo apt-get install -y jq unzip
+ - name: Configure AWS credentials
+ uses: aws-actions/configure-aws-credentials@v1
+ with:
+ aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+ aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+ aws-region: ${{ secrets.AWS_REGION }}
+
+ - name: Login to Amazon ECR
+ id: login-ecr
+ uses: aws-actions/amazon-ecr-login@v1
+
+ - name: Build, tag, and push image to ECR
+ id: build-image
+ env:
+ ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }}
+ ECR_REPOSITORY: ${{ secrets.ECR_REPOSITORY_NAME }}
+ IMAGE_TAG: latest
+ run: |
+ echo "ECR_REGISTRY: $ECR_REGISTRY"
+ echo "ECR_REPOSITORY: $ECR_REPOSITORY"
+ echo "IMAGE_TAG: $IMAGE_TAG"
+ docker build -t $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG .
+ docker push $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG
+ echo "::set-output name=image::$ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG"
+ Continuous-Deployment:
+ needs: build-and-push-ecr-image
+ runs-on: self-hosted
+ steps:
+ - name: Checkout
+ uses: actions/checkout@v3
+
+ - name: Configure AWS credentials
+ uses: aws-actions/configure-aws-credentials@v1
+ with:
+ aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+ aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+ aws-region: ${{ secrets.AWS_REGION }}
+
+ - name: Login to Amazon ECR
+ id: login-ecr
+ uses: aws-actions/amazon-ecr-login@v1
+
+
+ - name: Pull latest images
+ run: |
+ docker pull ${{secrets.AWS_ECR_LOGIN_URI}}/${{ secrets.ECR_REPOSITORY_NAME }}:latest
+
+ #- name: Stop and remove container if running
+ # run: |
+ # docker ps -q --filter "name=networksecurity" | grep -q . && docker stop networksecurity && docker rm -fv networksecurity
+
+ - name: Run Docker Image to serve users
+ run: |
+ docker run -d -p 8080:8080 --ipc="host" --name=networksecurity -e 'AWS_ACCESS_KEY_ID=${{ secrets.AWS_ACCESS_KEY_ID }}' -e 'AWS_SECRET_ACCESS_KEY=${{ secrets.AWS_SECRET_ACCESS_KEY }}' -e 'AWS_REGION=${{ secrets.AWS_REGION }}' ${{secrets.AWS_ECR_LOGIN_URI}}/${{ secrets.ECR_REPOSITORY_NAME }}:latest
+ - name: Clean previous images and containers
+ run: |
+ docker system prune -f
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..a495eac2df58dfdc0374da6ea08f3b33d1d91fb0
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,14 @@
+.env
+.venv/
+pycache/
+__pycache__/
+.ipynb_checkpoints
+.vscode/
+.DS_Store
+*.pyc
+logs/
+*.log
+Artifacts/
+s3_sync_folder/
+final_model/
+data/
\ No newline at end of file
diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000000000000000000000000000000000000..24ee5b1be9961e38a503c8e764b7385dbb6ba124
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.13
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..a7d56305da8e5e86f2974d1cc54e1d4dfe33e121
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,31 @@
+# for aws
+# FROM python:3.10-slim-buster
+# WORKDIR /app
+# COPY . /app
+# RUN apt update -y && apt install awscli -y
+# RUN apt-get update && pip install -r requirements.txt
+# CMD ["python3", "app.py"]
+
+FROM python:3.13-slim
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+# Copy requirements
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Copy all application files
+COPY --chown=user . /app
+
+# Create necessary directories
+RUN mkdir -p /app/data /app/final_model /app/templates
+
+# Expose port 7860 (HF Space requirement)
+EXPOSE 7860
+
+# Run the application
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..e77181cb256058ffc0ca8c78297492d5a6823278
--- /dev/null
+++ b/README.md
@@ -0,0 +1,10 @@
+---
+title: NSS
+emoji: 🏃
+colorFrom: blue
+colorTo: purple
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1a62f9977be41c7421a43f9b97af2d4f9d53db6
--- /dev/null
+++ b/app.py
@@ -0,0 +1,75 @@
+import os,sys
+import certifi
+from dotenv import load_dotenv
+from src.exception.exception import NetworkSecurityException
+from src.logging.logger import logging
+from src.pipeline.training_pipeline import Trainingpipeline
+from fastapi import FastAPI, File, UploadFile, Request
+from fastapi.middleware.cors import CORSMiddleware
+from uvicorn import run as app_run
+from fastapi.responses import Response
+from starlette.responses import RedirectResponse
+import pandas as pd
+from src.utils.ml_utils.model.estimator import NetworkSecurityModel
+ca = certifi.where()
+load_dotenv()
+mongo_db_uri = os.getenv("MONGO_DB_URI")
+
+from src.constant.training_pipeline import DATA_INGESTION_COLLECTION_NAME
+from src.constant.training_pipeline import DATA_INGESTION_DATBASE_NANE
+from src.utils.main_utils.utils import load_object
+# import pymongo
+
+# client = pymongo.MongoClient(mongo_db_uri,tlsCAFile=ca)
+# database = client[DATA_INGESTION_DATBASE_NANE]
+# collection = database[DATA_INGESTION_COLLECTION_NAME]
+from fastapi.templating import Jinja2Templates
+templates = Jinja2Templates(directory="./templates")
+app = FastAPI()
+orgin = ["*"]
+
+app.add_middleware(
+ CORSMiddleware,
+ allow_origins=orgin,
+ allow_credentials=True,
+ allow_methods=["*"],
+ allow_headers=["*"],
+)
+
+# @app.get("/", tags = ["authentication"])
+# async def index():
+# return RedirectResponse(url="/docs")
+
+@app.get("/train")
+async def training_route():
+ try:
+ training_pipeline = Trainingpipeline()
+ training_pipeline.run_pipeline()
+ return Response("Training successfull !!")
+ except Exception as e:
+ raise NetworkSecurityException(e, sys)
+
+@app.post("/predict") # predict route
+async def predict_route(request: Request, file: UploadFile =File(...)):
+ try:
+ df = pd.read_csv(file.file)
+ # Remove target column if it exists
+ if 'Result' in df.columns:
+ df = df.drop(columns=['Result'])
+ preprocessor = load_object(file_path = "final_model/preprocessor.pkl")
+ model = load_object(file_path= "final_model/model.pkl")
+ NSmodel = NetworkSecurityModel(preprocessing_object= preprocessor, trained_model_object= model)
+ print(df.iloc[0])
+ y_pred = NSmodel.predict(df)
+ print(y_pred)
+ df['predicted_column'] = y_pred
+ print(df['predicted_column'])
+ df.to_csv("final_model/predicted.csv")
+ table_html = df.to_html(classes = 'table table-striped')
+ return templates.TemplateResponse("table.html", {"request": request, "table": table_html})
+
+ except Exception as e:
+ raise NetworkSecurityException(e, sys)
+if __name__ == "__main__":
+ app_run(app, host="0.0.0.0", port=8080)
+
\ No newline at end of file
diff --git a/data_schema/schema.yaml b/data_schema/schema.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d84d733b8a66a909fa79340ecb4625dd4343c6c1
--- /dev/null
+++ b/data_schema/schema.yaml
@@ -0,0 +1,65 @@
+columns:
+ - having_IP_Address: int64
+ - URL_Length: int64
+ - Shortining_Service: int64
+ - having_At_Symbol: int64
+ - double_slash_redirecting: int64
+ - Prefix_Suffix: int64
+ - having_Sub_Domain: int64
+ - SSLfinal_State: int64
+ - Domain_registeration_length: int64
+ - Favicon: int64
+ - port: int64
+ - HTTPS_token: int64
+ - Request_URL: int64
+ - URL_of_Anchor: int64
+ - Links_in_tags: int64
+ - SFH: int64
+ - Submitting_to_email: int64
+ - Abnormal_URL: int64
+ - Redirect: int64
+ - on_mouseover: int64
+ - RightClick: int64
+ - popUpWidnow: int64
+ - Iframe: int64
+ - age_of_domain: int64
+ - DNSRecord: int64
+ - web_traffic: int64
+ - Page_Rank: int64
+ - Google_Index: int64
+ - Links_pointing_to_page: int64
+ - Statistical_report: int64
+ - Result: int64
+
+numerical_columns:
+ - having_IP_Address
+ - URL_Length
+ - Shortining_Service
+ - having_At_Symbol
+ - double_slash_redirecting
+ - Prefix_Suffix
+ - having_Sub_Domain
+ - SSLfinal_State
+ - Domain_registeration_length
+ - Favicon
+ - port
+ - HTTPS_token
+ - Request_URL
+ - URL_of_Anchor
+ - Links_in_tags
+ - SFH
+ - Submitting_to_email
+ - Abnormal_URL
+ - Redirect
+ - on_mouseover
+ - RightClick
+ - popUpWidnow
+ - Iframe
+ - age_of_domain
+ - DNSRecord
+ - web_traffic
+ - Page_Rank
+ - Google_Index
+ - Links_pointing_to_page
+ - Statistical_report
+ - Result
diff --git a/data_to_mongo.py b/data_to_mongo.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4e8e63c0c0b17ac1161cb7e2e37bcf5b5876199
--- /dev/null
+++ b/data_to_mongo.py
@@ -0,0 +1,73 @@
+# Test code
+# import os
+# from pymongo.mongo_client import MongoClient
+# from dotenv import load_dotenv
+# load_dotenv()
+# uri = os.getenv("MONGODB_URL")
+
+# # 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)
+
+
+import os
+import sys
+import json
+import certifi
+import pandas as pd
+import numpy as np
+# import pymongo
+from src.exception.exception import NetworkSecurityException
+from src.logging.logger import logging
+from dotenv import load_dotenv
+load_dotenv()
+
+MONGODB_URL = os.getenv("MONGODB_URL")
+print(MONGODB_URL)
+ca = certifi.where() # ca= certified authority
+
+class NetworkDataExtract():
+ def __init__(self):
+ try:
+ pass
+ except Exception as e:
+ raise NetworkSecurityException(e, sys)
+ def csv_to_json_converter(self, file_path):
+ try:
+ data = pd.read_csv(file_path)
+ data.reset_index(drop=True, inplace=True)
+ records = list(json.loads(data.T.to_json()).values())
+ return records
+ except Exception as e:
+ raise NetworkSecurityException(e, sys)
+
+ def insert_data_to_mongodb(self, records, database, collection):
+ try:
+ self.database = database
+ self.collection = collection
+ self.records = records
+
+ self.mongo_client = pymongo.MongoClient(MONGODB_URL)
+ self.database = self.mongo_client[self.database]
+
+ self.collection = self.database[self.collection ]
+ self.collection.insert_many(self.records)
+ return(len(self.records))
+ except Exception as e:
+ raise NetworkSecurityException(e, sys)
+
+if __name__ == "__main__":
+ FILE_PATH = "data\phisingData.csv"
+ DATABASE = "Network_data"
+ Collection = "phising_data"
+ obj = NetworkDataExtract()
+ records = obj.csv_to_json_converter(file_path=FILE_PATH)
+ print("records converted to json")
+ noOfRecords = obj.insert_data_to_mongodb(records,DATABASE, Collection)
+ print(noOfRecords)
\ No newline at end of file
diff --git a/load_data_to_sqlite.py b/load_data_to_sqlite.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd47a9c0cad35130a7733e259385244b676f4518
--- /dev/null
+++ b/load_data_to_sqlite.py
@@ -0,0 +1,15 @@
+from src.data.sqlite_manager import PhishingDataManager
+
+if __name__ == "__main__":
+ FILE_PATH = "data/phisingData.csv"
+
+ print("Initializing SQLite database...")
+ db_manager = PhishingDataManager()
+
+ print("Loading data from CSV...")
+ count = db_manager.insert_data_from_csv(FILE_PATH)
+
+ print(f"✅ Successfully loaded {count} records into SQLite database!")
+ print(f"Database location: data/phishing_data.db")
+
+ db_manager.close()
\ No newline at end of file
diff --git a/main.py b/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a5cf9f51cc95abfc2378cd0186b30c14a2be656
--- /dev/null
+++ b/main.py
@@ -0,0 +1,41 @@
+from src.components.data_ingestion import DataIngestion
+from src.components.data_validation import DataValidation
+from src.components.data_transformation import DataTransformation
+from src.exception.exception import NetworkSecurityException
+from src.logging.logger import logging
+from src.entity.config_entity import Data_ingestion_config, TrainingPipelineConfig, Data_validation_config, Data_transformation_config, Model_trainer_config
+from src.components.model_trainer import ModelTrainer
+
+import sys
+
+if __name__ == "__main__":
+ try:
+ traingning_pipeline_config = TrainingPipelineConfig()
+ data_ingestion_config = Data_ingestion_config(traingning_pipeline_config)
+ Data_ingestion = DataIngestion(data_ingestion_config)
+ logging.info("Data ingestion started")
+ data_ingestion_artifacts = Data_ingestion.initiate_data_ingestion()
+ logging.info("Data ingestion completed")
+ print("Data ingestion completed")
+
+ data_validation_config = Data_validation_config(traingning_pipeline_config)
+ Data_validation = DataValidation(data_ingestion_artifacts, data_validation_config)
+ logging.info("Data validation started")
+ data_validation_artifacts = Data_validation.intiate_data_validation()
+ logging.info("Data validation completed")
+ print(data_validation_artifacts)
+
+ data_transformation_config = Data_transformation_config(traingning_pipeline_config)
+ logging.info("data Transformation started")
+ data_transformation = DataTransformation(data_validation_artifacts, data_transformation_config)
+ data_transformation_artifact = data_transformation.initiate_data_transformation()
+ print(data_transformation_artifact)
+ logging.info("data Transformation completed")
+
+ logging.info("Model training started")
+ model_trainer_config = Model_trainer_config(traingning_pipeline_config)
+ model_trainer = ModelTrainer(model_trainer_config=model_trainer_config, data_transformation_artifact=data_transformation_artifact)
+ model_trainer_artifact = model_trainer.initiate_model_trainer()
+ logging.info("Model training completed")
+ except Exception as e:
+ raise NetworkSecurityException(e, sys)
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..2b9f9368a7891994f99f476d210d22e461d7bc2e
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,20 @@
+[project]
+name = "NetworkSecuritySystemMLProject"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+ "certifi>=2026.1.4",
+ "dagshub>=0.6.4",
+ "dill>=0.4.0",
+ "fastapi>=0.128.0",
+ "mlflow>=3.8.1",
+ "numpy>=2.4.1",
+ "pandas>=2.3.3",
+ "pyaml>=25.7.0",
+ "pymongo[srv]==3.6",
+ "python-dotenv>=1.2.1",
+ "python-multipart>=0.0.21",
+ "uvicorn>=0.40.0",
+]
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9eda1e0bbb437d67262626bbfd8d41bb5069dbf9
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,14 @@
+python-dotenv
+pandas
+numpy
+pymongo[srv]==3.6
+pymongo
+certifi
+dill
+mlflow
+pyaml
+dagshub
+fastapi
+uvicorn
+python-multipart
+# -e .
\ No newline at end of file
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..4250a8c5279863935858f96d97cc9917f634acc7
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,26 @@
+from setuptools import find_packages, setup
+from typing import List
+
+def get_requirements()-> List[str]:
+ """This function is going to return list of requirements
+ """
+ requirement_list:List[str]=[]
+ try:
+ with open('requirements.txt', 'r') as file:
+ lines= file.readlines()
+ for line in lines:
+ requirement=line.strip()
+ if requirement and requirement!= '-e .': # ignore empty line and -e .
+ requirement_list.append(requirement)
+
+ except FileNotFoundError:
+ print("Error: requirements.txt file not found.")
+
+setup(
+ name="Network_Security_system",
+ version="0.0.1",
+ author="Kshitij",
+ author_email="kshitijk146@gmail.com",
+ packages=find_packages(),
+ install_requires=get_requirements()
+)
\ No newline at end of file
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/__pycache__/__init__.cpython-310.pyc b/src/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cf7667738c5e04a70d32cf8b7816f184115b3dd2
Binary files /dev/null and b/src/__pycache__/__init__.cpython-310.pyc differ
diff --git a/src/cloud/__init__.py b/src/cloud/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/cloud/__pycache__/__init__.cpython-310.pyc b/src/cloud/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d5d7ce2369275af590f7c788145ac256b79f03fb
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diff --git a/src/cloud/__pycache__/s3_syncer.cpython-310.pyc b/src/cloud/__pycache__/s3_syncer.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ac1b4a0969cd40c7bc100ac8df82e54796fca0bb
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diff --git a/src/cloud/s3_syncer.py b/src/cloud/s3_syncer.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4add259233338c4a08e3b08ce1a602d63636e96
--- /dev/null
+++ b/src/cloud/s3_syncer.py
@@ -0,0 +1,45 @@
+import os
+import shutil
+class s3sync:
+ def __init__(self, Local_sync_folder = "s3_sync_folder"):
+ self.Local_sync_folder = Local_sync_folder
+ if not os.path.exists(self.Local_sync_folder):
+ os.makedirs(self.Local_sync_folder, exist_ok=True)
+
+ def sync_folder_to_s3(self, folder, aws_bucket_url):
+ # command = (
+ # f"aws s3 sync {folder} {aws_bucket_url}"
+ # )
+ # os.system(command)
+ try:
+ dest_path= aws_bucket_url.replace("s3://","").replace(aws_bucket_url.split("/")[0] + "/", "")
+ destination = os.path.join(self.Local_sync_folder, dest_path)
+
+ # copy folder
+ if os.path.exists(destination):
+ shutil.rmtree(destination)
+ shutil.copytree(folder, destination)
+ print(f"Synced {folder} to {destination}")
+ except Exception as e:
+ print(f"Error syncing folder to S3: {e}")
+
+
+
+ def sync_folder_from_s3(self, folder, aws_bucket_url):
+ # command = (
+ # f"aws s3 sync {aws_bucket_url} {folder}"
+ # )
+ # os.system(command)
+
+ try:
+ source_path = aws_bucket_url.replace("s3://","").replace(aws_bucket_url.split("/")[0] + "/", "")
+ source = os.path.join(self.Local_sync_folder, source_path)
+
+ if os.path.exists(folder):
+ shutil.rmtree(folder)
+ shutil.copytree(source, folder)
+ print(f"Synced {source} to {folder}")
+ except Exception as e:
+ print(f"Error syncing folder from S3: {e}")
+
+
\ No newline at end of file
diff --git a/src/components/__init__.py b/src/components/__init__.py
new file mode 100644
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diff --git a/src/components/__pycache__/data_validation.cpython-310.pyc b/src/components/__pycache__/data_validation.cpython-310.pyc
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diff --git a/src/components/__pycache__/model_trainer.cpython-310.pyc b/src/components/__pycache__/model_trainer.cpython-310.pyc
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diff --git a/src/components/data_ingestion.py b/src/components/data_ingestion.py
new file mode 100644
index 0000000000000000000000000000000000000000..0cce4082a3a67bf718e2377a21b6874d1f8216e9
--- /dev/null
+++ b/src/components/data_ingestion.py
@@ -0,0 +1,80 @@
+from src.exception.exception import NetworkSecurityException
+from src.logging.logger import logging
+from src.entity.config_entity import Data_ingestion_config
+from src.entity.artifact_entity import DataIngestionArtifact
+import os, sys
+import pandas as pd
+from typing import List
+from sklearn.model_selection import train_test_split
+from dotenv import load_dotenv
+# import pymongo
+import numpy as np
+from src.data.sqlite_manager import PhishingDataManager
+
+load_dotenv()
+MONGODB_URL = os.getenv("MONGODB_URL")
+
+class DataIngestion:
+ def __init__(self, data_ingestion_config: Data_ingestion_config):
+ try:
+ self.data_ingestion_config = data_ingestion_config
+ self.db_manager = PhishingDataManager()
+ except Exception as e:
+ raise NetworkSecurityException(e, sys)
+
+ def export_collection_as_dataframe(self):
+ """Export data from SQLite instead of MongoDB"""
+ try:
+ # Get all training data
+ df = self.db_manager.get_training_data(include_new_only=False)
+ return df
+ except Exception as e:
+ raise NetworkSecurityException(e, sys)
+
+ def move_data_into_feature_store(self, dataframe: pd.DataFrame):
+ try:
+ feature_store_file = self.data_ingestion_config.feature_store_file_path
+ dir_path = os.path.dirname(feature_store_file)
+ os.makedirs(dir_path, exist_ok=True)
+ dataframe.to_csv(feature_store_file, index=False, header=True)
+ return dataframe
+ except Exception as e:
+ raise NetworkSecurityException(e, sys)
+
+ def data_train_test_split(self,dataframe:pd.DataFrame):
+ try:
+ train_set, test_set = train_test_split(
+ dataframe, test_size=self.data_ingestion_config.train_test_split_ratio
+ )
+ logging.info("Trained test spltting done on dataframe")
+ dir_path = os.path.dirname(self.data_ingestion_config.train_file_path)
+ os.makedirs(dir_path, exist_ok=True)
+ logging.info("Exporting train and test file path")
+ train_set.to_csv(
+ self.data_ingestion_config.train_file_path, index = False, header = True
+ )
+ test_set.to_csv(
+ self.data_ingestion_config.test_file_path, index = False, header = True
+ )
+ logging.info("Exported train and test file path.")
+
+ except Exception as e:
+ raise NetworkSecurityException(e, sys)
+
+ def initiate_data_ingestion(self):
+ try:
+ dataframe = self.export_collection_as_dataframe()
+ dataframe = self.move_data_into_feature_store(dataframe)
+ self.data_train_test_split(dataframe)
+
+ # Mark data as used
+ self.db_manager.mark_data_as_trained()
+
+ data_ingestion_artifact = DataIngestionArtifact(
+ train_file_path=self.data_ingestion_config.train_file_path,
+ test_file_path=self.data_ingestion_config.test_file_path
+ )
+ return data_ingestion_artifact
+ except Exception as e:
+ raise NetworkSecurityException(e, sys)
+
diff --git a/src/components/data_transformation.py b/src/components/data_transformation.py
new file mode 100644
index 0000000000000000000000000000000000000000..601fd0ac820535eaec5dc2dd49eeb8d928b14950
--- /dev/null
+++ b/src/components/data_transformation.py
@@ -0,0 +1,93 @@
+import sys
+import os
+import numpy as np
+import pandas as pd
+from sklearn.impute import KNNImputer
+from sklearn.pipeline import Pipeline
+from src.constant.training_pipeline import TARGET_COLUMN
+from src.constant.training_pipeline import DATA_TRANSFORMATION_IMPUTER_PARAMS
+from src.entity.artifact_entity import (
+ DataTransformationArtifact,
+ DataValidationArtifact,
+)
+from src.exception.exception import NetworkSecurityException
+from src.logging.logger import logging
+from src.utils.main_utils.utils import save_np_array, save_object
+from src.entity.config_entity import Data_transformation_config
+class DataTransformation:
+ def __init__(self, data_validation_artifact: DataValidationArtifact, data_transformation_config: Data_transformation_config):
+ try:
+ self.data_validation_artifact:DataValidationArtifact = data_validation_artifact
+ self.data_transformation_config:Data_transformation_config = data_transformation_config
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ @staticmethod
+ def read_data(file_path) -> pd.DataFrame:
+ try:
+ return pd.read_csv(file_path)
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ def get_data_transformer_object(self) -> Pipeline:
+ """
+ it initialises a KNNImputer object with the parameter specified in the training_pipeline.py file and returns
+ a pipeline with the KNNImputer object as the first step.
+
+ args:
+ cls: DataTransformation
+ Returns:
+ a pipeline object
+ """
+ logging.info("Entered get_data_transformation_object methof of transformation class")
+
+ try:
+ knn_imputer = KNNImputer(**DATA_TRANSFORMATION_IMPUTER_PARAMS)
+ logging.info(f"intialise knn imputer with {DATA_TRANSFORMATION_IMPUTER_PARAMS}")
+ pipeline = Pipeline(steps=[("imputer", knn_imputer)])
+ return pipeline
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ def initiate_data_transformation(self)-> DataTransformationArtifact:
+ try:
+ logging.info("Started data transformation!")
+ train_df = DataTransformation.read_data(self.data_validation_artifact.valid_train_file_path)
+ test_df = DataTransformation.read_data(self.data_validation_artifact.valid_test_file_path)
+
+ # training dataframe
+ input_feature_train_df = train_df.drop(columns=[TARGET_COLUMN],axis = 1)
+ target_feature_train_df = train_df[TARGET_COLUMN]
+ target_feature_train_df = target_feature_train_df.replace(-1,0)
+ # testing dataframe
+ input_feature_test_df = test_df.drop(columns=[TARGET_COLUMN],axis = 1)
+ target_feature_test_df = test_df[TARGET_COLUMN]
+ target_feature_test_df = target_feature_test_df.replace(-1,0)
+
+ preprocessor = self.get_data_transformer_object()
+ preprocessor_obj = preprocessor.fit(input_feature_train_df)
+ logging.info("Preprocessor object created and fitted on training data")
+
+ transformed_input_train_feature = preprocessor_obj.transform(input_feature_train_df)
+ transformed_input_test_feature = preprocessor_obj.transform(input_feature_test_df)
+
+ # combining transformed input features with target feature
+ train_arr = np.c_[transformed_input_train_feature, np.array(target_feature_train_df)]
+ test_arr = np.c_[transformed_input_test_feature, np.array(target_feature_test_df)]
+
+ # save numpy array data
+ save_np_array(self.data_transformation_config.transformed_train_file_path, array=train_arr)
+ save_np_array(self.data_transformation_config.transformed_test_file_path,array = test_arr )
+ save_object(self.data_transformation_config.transformed_object_file_path,preprocessor_obj)
+ save_object("final_model/preprocessor.pkl", preprocessor_obj)
+
+ # preparing artifacts
+ Data_transformation_artifact = DataTransformationArtifact(
+ transformed_object_file_path=self.data_transformation_config.transformed_object_file_path,
+ transformed_train_file_path=self.data_transformation_config.transformed_train_file_path,
+ transformed_test_file_path=self.data_transformation_config.transformed_test_file_path,
+ )
+ return Data_transformation_artifact
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
diff --git a/src/components/data_validation.py b/src/components/data_validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0c0bdf7863164e2f2b4313410bc225122112406
--- /dev/null
+++ b/src/components/data_validation.py
@@ -0,0 +1,104 @@
+from src.entity.artifact_entity import DataIngestionArtifact,DataValidationArtifact
+from src.entity.config_entity import Data_validation_config
+from src.exception.exception import NetworkSecurityException
+from src.constant.training_pipeline import SCHEMA_FILE_PATH
+from src.logging.logger import logging
+from scipy.stats import ks_2samp
+import pandas as pd
+import os, sys
+from src.utils.main_utils.utils import read_yaml_file, write_yaml_file
+
+class DataValidation:
+ def __init__(self, data_ingestion_artifact: DataIngestionArtifact, data_validation_config: Data_validation_config):
+ try:
+ self.data_ingestion_artifact = data_ingestion_artifact
+ self.data_validation_config = data_validation_config
+ self._schema_config = read_yaml_file(file_path=SCHEMA_FILE_PATH)
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ @staticmethod
+ def read_data(file_path) -> pd.DataFrame:
+ try:
+ return pd.read_csv(file_path)
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ def validate_number_of_columns(self, dataframe: pd.DataFrame)-> bool:
+ try:
+ number_of_columns = len(self._schema_config)
+ logging.info(f"Required number of columns: {number_of_columns}")
+ logging.info(f"Data frame has columns: {len(dataframe.columns)}")
+ if len(dataframe.columns)==number_of_columns:
+ return True
+ else:
+ return False
+
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+ def detect_drift(self,base_df, current_df, threshold=0.05)->bool:
+ try:
+ status = True
+ report = {}
+ for column in base_df:
+ d1 = base_df[column]
+ d2 = current_df[column]
+ is_sample_dist = ks_2samp(d1, d2)
+ if threshold <= is_sample_dist.pvalue:
+ is_found = False
+ else:
+ is_found = True
+ status = False
+ report.update({column: {
+ "p_value": float(is_sample_dist.pvalue),
+ "drift_status": is_found
+ }})
+ drift_report_file_path = self.data_validation_config.drift_report_file_path
+ # Create directory
+ dir_path = os.path.dirname(drift_report_file_path)
+ os.makedirs(dir_path, exist_ok=True)
+ write_yaml_file(file_path=drift_report_file_path, content=report)
+ return status
+ except Exception as e:
+ raise NetworkSecurityException(e, sys )
+
+ def intiate_data_validation(self)-> DataValidationArtifact:
+ try:
+ train_file_path = self.data_ingestion_artifact.train_file_path
+ test_file_path = self.data_ingestion_artifact.test_file_path
+
+ # read the data from train and test csv
+ train_df = DataValidation.read_data(train_file_path)
+ test_df = DataValidation.read_data(test_file_path)
+
+ # validating no. of columns
+ status = self.validate_number_of_columns(dataframe=train_df)
+ if not status:
+ error_message = f"{train_file_path} does not match schema"
+
+
+ status = self.validate_number_of_columns(dataframe=test_df)
+ if not status:
+ error_message = f"{test_file_path} does not match schema"
+
+ # check data drift
+ status = self.detect_drift(base_df=train_df, current_df=test_df)
+ dir_path = os.path.dirname(self.data_validation_config.valid_train_file_path)
+ os.makedirs(dir_path, exist_ok=True)
+
+ train_df.to_csv(self.data_validation_config.valid_train_file_path, index=False, header = True)
+ test_df.to_csv(self.data_validation_config.valid_test_file_path, index=False, header = True)
+
+
+ data_validation_artifacts = DataValidationArtifact(
+ validation_status=status,
+ valid_train_file_path=self.data_ingestion_artifact.train_file_path,
+ valid_test_file_path=self.data_ingestion_artifact.test_file_path,
+ invalid_train_file_path=None,
+ invalid_test_file_path=None,
+ drift_report_file_path=self.data_validation_config.drift_report_file_path
+ )
+ return data_validation_artifacts
+
+ except Exception as e:
+ raise NetworkSecurityException(e, sys)
diff --git a/src/components/model_trainer.py b/src/components/model_trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..43bb7e90b511de7b1d5e1e136c5ab02ae6e8a49b
--- /dev/null
+++ b/src/components/model_trainer.py
@@ -0,0 +1,140 @@
+import os, sys
+from src.utils.ml_utils.model.estimator import NetworkSecurityModel
+from src.exception.exception import NetworkSecurityException
+from src.logging.logger import logging
+from src.entity.artifact_entity import DataTransformationArtifact, ModelTrainerArtifact
+from src.entity.config_entity import Model_trainer_config
+from src.utils.main_utils.utils import save_object, load_object
+from src.utils.main_utils.utils import load_numpy_array_data, evaluate_models
+from src.utils.ml_utils.metric.classification_metric import classification_score
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import (
+ RandomForestClassifier,
+ AdaBoostClassifier,
+ GradientBoostingClassifier,
+)
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import r2_score
+import mlflow
+import dagshub
+dagshub.init(repo_owner='kshitijk146', repo_name='MLOPS_project_network_Security_system', mlflow=True)
+class ModelTrainer:
+ def __init__(self, model_trainer_config: Model_trainer_config, data_transformation_artifact: DataTransformationArtifact):
+ try:
+ self.model_trainer_config = model_trainer_config
+ self.data_transformation_artifact = data_transformation_artifact
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ def track_mlflow(self,best_model, classificationmetric):
+ with mlflow.start_run():
+ f1_score = classificationmetric.f1_score
+ precision_score = classificationmetric.precision_score
+ recall_score = classificationmetric.recall_score
+
+ mlflow.log_metric("f1_score", f1_score)
+ mlflow.log_metric("precision_score", precision_score)
+ mlflow.log_metric("recall_score", recall_score)
+ mlflow.sklearn.log_model(best_model, "model")
+
+ def train_model(self, x_train, y_train,x_test, y_test):
+ models = {
+ "KNN": KNeighborsClassifier(),
+ "Decision Tree": DecisionTreeClassifier(),
+ "Random Forest": RandomForestClassifier(verbose=True),
+ "AdaBoost": AdaBoostClassifier(),
+ "Gradient Boosting": GradientBoostingClassifier(verbose=True),
+ "logistic regression": LogisticRegression(verbose=True)
+ }
+ params = {
+ "KNN": {
+ 'n_neighbors': [3, 5, 7],
+ 'weights': ['uniform', 'distance'],
+ 'metric': ['euclidean']
+ },
+ "Decision Tree": {
+ 'criterion': ['gini', 'entropy'],
+ 'max_depth': [None, 5, 10],
+ 'min_samples_split': [2, 5],
+ 'min_samples_leaf': [1, 2]
+ },
+ "Random Forest": {
+ 'n_estimators': [50, 100],
+ 'max_depth': [None, 5],
+ 'min_samples_split': [2, 5],
+ 'min_samples_leaf': [1, 2],
+ 'max_features': ['sqrt']
+ },
+ "AdaBoost": {
+ 'n_estimators': [50, 100],
+ 'learning_rate': [0.1, 1.0],
+ # 'algorithm': ['SAMME.R']
+ },
+ "Gradient Boosting": {
+ 'n_estimators': [50, 100],
+ 'learning_rate': [0.1],
+ 'max_depth': [3, 5],
+ 'min_samples_split': [2],
+ 'min_samples_leaf': [1],
+ 'max_features': ['sqrt']
+ },
+ "logistic regression": {
+ 'C': [1.0, 10.0],
+ 'penalty': ['l2'],
+ 'solver': ['liblinear']
+ }
+ }
+
+ model_report:dict = evaluate_models(
+ x_train = x_train,y_train = y_train,x_test = x_test,y_test = y_test,models = models,params = params)
+
+ # to get the best model score from the dict
+ best_model_score = max(sorted(model_report.values()))
+
+ # to get best model name from dict
+ best_model_name = list(model_report.keys())[
+ list(model_report.values()).index(best_model_score)
+ ]
+ logging.info(f"best model name: {best_model_name}")
+ best_model = models[best_model_name]
+ y_train_pred = best_model.predict(x_train)
+ classification_train_metric= classification_score(y_true = y_train, y_pred=y_train_pred)
+
+ # track mlfow
+ self.track_mlflow(best_model, classification_train_metric)
+
+
+
+ y_test_pred = best_model.predict(x_test)
+ classification_test_metric = classification_score(y_true = y_test, y_pred=y_test_pred)
+
+ preprocessor = load_object(file_path=self.data_transformation_artifact.transformed_object_file_path)
+ model_dir_path = os.path.dirname(self.model_trainer_config.trained_model_file_path)
+ os.makedirs(model_dir_path, exist_ok=True)
+
+ NetwerkModel= NetworkSecurityModel(preprocessing_object=preprocessor, trained_model_object=best_model)
+ save_object(self.model_trainer_config.trained_model_file_path, obj=NetwerkModel)
+ save_object("final_model/model.pkl", best_model)
+
+ model_trainer_artifact = ModelTrainerArtifact(trained_model_file_path=self.model_trainer_config.trained_model_file_path, train_metric_artifact=classification_train_metric, test_metric_artifact=classification_test_metric)
+ logging.info(f"Model trainer artifact: {model_trainer_artifact}")
+ return model_trainer_artifact
+
+ def initiate_model_trainer(self)-> ModelTrainerArtifact:
+ try:
+ train_file_path = self.data_transformation_artifact.transformed_train_file_path
+ test_file_path = self.data_transformation_artifact.transformed_test_file_path
+
+ # loading training array and testing array
+ train_array = load_numpy_array_data(train_file_path)
+ test_array = load_numpy_array_data(test_file_path)
+ x_train, y_train, x_test, y_test = (
+ train_array[:, :-1],
+ train_array[:, -1],
+ test_array[:, :-1],
+ test_array[:, -1],
+ )
+ model = self.train_model(x_train, y_train, x_test=x_test, y_test=y_test)
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
\ No newline at end of file
diff --git a/src/constant/__init__.py b/src/constant/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/constant/__pycache__/__init__.cpython-310.pyc b/src/constant/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5d90a2cbe5b7d770e8e479a321bbe42379f46e73
Binary files /dev/null and b/src/constant/__pycache__/__init__.cpython-310.pyc differ
diff --git a/src/constant/training_pipeline/__init__.py b/src/constant/training_pipeline/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..facf620c2a10b65dcd9cb1dc6432b4328abe0ba2
--- /dev/null
+++ b/src/constant/training_pipeline/__init__.py
@@ -0,0 +1,64 @@
+import os
+import sys
+import numpy as np
+
+"""
+Common constant variable
+"""
+TARGET_COLUMN = "Result"
+PIPELINE_NAME: str= "NetworkSecurity"
+ARTIFACT_DIR: str = "Artifacts"
+FILE_NAME: str = "phisingData.csv"
+
+TRAIN_FILE_NAME: str = "train.csv"
+TEST_FILE_NAME: str = "test.csv"
+SCHEMA_FILE_PATH = os.path.join("data_schema", "schema.yaml")
+
+SAVED_MODEL_DIR = os.path.join("saved_models")
+MODEL_FILE_NAME = "model.pkl"
+"""
+Data ingestion variable
+"""
+DATA_INGESTION_COLLECTION_NAME: str= "phising_data"
+DATA_INGESTION_DATBASE_NANE: str= "Network_data"
+DATA_INGESTION_DIR_NAME:str = "data_ingestion"
+DATA_INGESTION_FEATURE_STORE_DIR: str = "feature_store"
+DATA_INGESTION_INGESTED_DIR: str = "ingested"
+DATA_INGESTION_TRAIN_TEST_SPLIT_RATION: float = 0.2
+
+
+"""
+Data validation realated constant start with DATA_VALIDATION VAR NAME
+
+"""
+DATA_VALIDATION_DIR_NAMR:str = "data_validation"
+DATA_VALIDATION_VALID_DIR: str = "validated"
+DATA_VALIDATION_INVALID_DIR: str = "invalid"
+DATA_VALIDATION_DRIFT_REPORT_DIR: str = "drift_report"
+DATA_VALIDATION_DRIFT_REPORT_FILE_NAME: str = "report.yaml"
+
+
+"""
+Data transformation realated constant start with DATA_TRANSFORMATION VAR NAME
+"""
+DATA_TRANSFORMATION_DIR_NAME: str = "data_transformation"
+DATA_TRANSFORMATION_TRANSFORMED_DIR_NAME: str = "transformed"
+DATA_TRANSFORMATION_TRANSFORMED_OBJECT_DIR:str = "transformed_object"
+PREPROCESSING_OBJECT_FILE_NAME:str = "preprocessing.pkl"
+# using knn imputer
+DATA_TRANSFORMATION_IMPUTER_PARAMS: dict = {
+ "missing_values": np.nan,
+ "n_neighbors" : 3,
+ "weights" : "uniform"
+}
+
+"""
+Model trainer realated constant start with DATA_TRANSFORMATION VAR NAME
+"""
+MODEL_TRAINER_DIR_NAME: str = "model_trainer"
+MODEL_TRAINER_MODEL_DIR:str = "trained_model"
+MODEL_TRAINER_MODEL_NAME:str = "model.pkl"
+MODEL_TRAINER_EXPECTED_SCORE: float = 0.6
+MODEL_TRAINER_OVERFITTING_UNDERFITTING_THRESHOLD: float = 0.05
+
+TRAINING_BUCKET_NAME = "networksecuritymodelbucket"
\ No newline at end of file
diff --git a/src/constant/training_pipeline/__pycache__/__init__.cpython-310.pyc b/src/constant/training_pipeline/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..235cb7f8705b78f8b16c3bf25c80ac3f81b2e947
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diff --git a/src/data/__init__.py b/src/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/data/sqlite_manager.py b/src/data/sqlite_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..40c7a5a30ad82a853c96cc33ea066c78ee26a12a
--- /dev/null
+++ b/src/data/sqlite_manager.py
@@ -0,0 +1,162 @@
+import sqlite3
+import pandas as pd
+import os
+from datetime import datetime
+from src.exception.exception import NetworkSecurityException
+from src.logging.logger import logging
+import sys
+
+class PhishingDataManager:
+ def __init__(self, db_path="data/phishing_data.db"):
+ """Initialize SQLite database for phishing data"""
+ try:
+ self.db_path = db_path
+ os.makedirs(os.path.dirname(db_path), exist_ok=True)
+ self.conn = sqlite3.connect(db_path, check_same_thread=False)
+ self._create_tables()
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ def _create_tables(self):
+ """Create phishing data table and metadata table"""
+ try:
+ cursor = self.conn.cursor()
+
+ # Main data table
+ cursor.execute("""
+ CREATE TABLE IF NOT EXISTS phishing_data (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ having_IP_Address INTEGER,
+ URL_Length INTEGER,
+ Shortining_Service INTEGER,
+ having_At_Symbol INTEGER,
+ double_slash_redirecting INTEGER,
+ Prefix_Suffix INTEGER,
+ having_Sub_Domain INTEGER,
+ SSLfinal_State INTEGER,
+ Domain_registeration_length INTEGER,
+ Favicon INTEGER,
+ port INTEGER,
+ HTTPS_token INTEGER,
+ Request_URL INTEGER,
+ URL_of_Anchor INTEGER,
+ Links_in_tags INTEGER,
+ SFH INTEGER,
+ Submitting_to_email INTEGER,
+ Abnormal_URL INTEGER,
+ Redirect INTEGER,
+ on_mouseover INTEGER,
+ RightClick INTEGER,
+ popUpWidnow INTEGER,
+ Iframe INTEGER,
+ age_of_domain INTEGER,
+ DNSRecord INTEGER,
+ web_traffic INTEGER,
+ Page_Rank INTEGER,
+ Google_Index INTEGER,
+ Links_pointing_to_page INTEGER,
+ Statistical_report INTEGER,
+ Result INTEGER,
+ created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+ used_in_training BOOLEAN DEFAULT 0
+ )
+ """)
+
+ # Training metadata table
+ cursor.execute("""
+ CREATE TABLE IF NOT EXISTS training_metadata (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ training_timestamp TIMESTAMP,
+ data_count INTEGER,
+ model_accuracy REAL,
+ model_version TEXT,
+ artifact_path TEXT
+ )
+ """)
+
+ self.conn.commit()
+ logging.info("Database tables created successfully")
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ def insert_data_from_csv(self, csv_path):
+ """Bulk insert from CSV (initial load)"""
+ try:
+ df = pd.read_csv(csv_path)
+ df.replace({"na": None}, inplace=True)
+
+ # Insert only new records (avoid duplicates)
+ df.to_sql('phishing_data', self.conn, if_exists='append', index=False)
+ logging.info(f"Inserted {len(df)} records from CSV")
+ return len(df)
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ def add_new_samples(self, data_dict_list):
+ """Add new phishing samples incrementally"""
+ try:
+ df = pd.DataFrame(data_dict_list)
+ df.to_sql('phishing_data', self.conn, if_exists='append', index=False)
+ logging.info(f"Added {len(df)} new samples")
+ return len(df)
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ def get_training_data(self, include_new_only=False):
+ """Fetch data for training"""
+ try:
+ if include_new_only:
+ # Only get data not used in training yet
+ query = "SELECT * FROM phishing_data WHERE used_in_training = 0"
+ else:
+ # Get all data
+ query = "SELECT * FROM phishing_data"
+
+ df = pd.read_sql_query(query, self.conn)
+
+ # Drop metadata columns
+ df = df.drop(['id', 'created_at', 'used_in_training'], axis=1, errors='ignore')
+
+ logging.info(f"Fetched {len(df)} records for training")
+ return df
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ def mark_data_as_trained(self):
+ """Mark all data as used in training"""
+ try:
+ cursor = self.conn.cursor()
+ cursor.execute("UPDATE phishing_data SET used_in_training = 1 WHERE used_in_training = 0")
+ self.conn.commit()
+ logging.info("Marked data as trained")
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ def get_new_data_count(self):
+ """Count untrained samples"""
+ try:
+ cursor = self.conn.cursor()
+ result = cursor.execute("SELECT COUNT(*) FROM phishing_data WHERE used_in_training = 0").fetchone()
+ return result[0]
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ def log_training_run(self, data_count, accuracy, version, artifact_path):
+ """Log training metadata"""
+ try:
+ cursor = self.conn.cursor()
+ cursor.execute("""
+ INSERT INTO training_metadata (training_timestamp, data_count, model_accuracy, model_version, artifact_path)
+ VALUES (?, ?, ?, ?, ?)
+ """, (datetime.now(), data_count, accuracy, version, artifact_path))
+ self.conn.commit()
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ def should_retrain(self, threshold=100):
+ """Check if retraining is needed based on new data"""
+ new_count = self.get_new_data_count()
+ return new_count >= threshold
+
+ def close(self):
+ self.conn.close()
\ No newline at end of file
diff --git a/src/entity/__init__.py b/src/entity/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/entity/__pycache__/__init__.cpython-310.pyc b/src/entity/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6fe07092103741e55556e67ba2f1566630641491
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diff --git a/src/entity/__pycache__/artifact_entity.cpython-310.pyc b/src/entity/__pycache__/artifact_entity.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2ebac0b0df57a2f716fd7bfee415e759ce733ef5
Binary files /dev/null and b/src/entity/__pycache__/artifact_entity.cpython-310.pyc differ
diff --git a/src/entity/__pycache__/config_entity.cpython-310.pyc b/src/entity/__pycache__/config_entity.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4a872605537bd70b7b1db9995bcab4131f4812e3
Binary files /dev/null and b/src/entity/__pycache__/config_entity.cpython-310.pyc differ
diff --git a/src/entity/artifact_entity.py b/src/entity/artifact_entity.py
new file mode 100644
index 0000000000000000000000000000000000000000..76d87bee01623db4b9bf230c5b6793015f161d6e
--- /dev/null
+++ b/src/entity/artifact_entity.py
@@ -0,0 +1,34 @@
+from dataclasses import dataclass
+
+@dataclass
+class DataIngestionArtifact:
+ train_file_path: str
+ test_file_path: str
+
+@dataclass
+class DataValidationArtifact:
+ validation_status: bool
+ valid_train_file_path: str
+ valid_test_file_path: str
+ invalid_train_file_path: str
+ invalid_test_file_path: str
+ drift_report_file_path: str
+
+
+@dataclass
+class DataTransformationArtifact:
+ transformed_object_file_path: str
+ transformed_train_file_path: str
+ transformed_test_file_path: str
+
+@dataclass
+class ClassificationMetricArtifact:
+ f1_score: float
+ precision_score: float
+ recall_score: float
+
+@dataclass
+class ModelTrainerArtifact:
+ trained_model_file_path: str
+ train_metric_artifact: ClassificationMetricArtifact
+ test_metric_artifact: ClassificationMetricArtifact
\ No newline at end of file
diff --git a/src/entity/config_entity.py b/src/entity/config_entity.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1e6aeda0b0ea61bbaef02764fd8055c6201cfe0
--- /dev/null
+++ b/src/entity/config_entity.py
@@ -0,0 +1,60 @@
+from datetime import datetime
+import os
+from src.constant import training_pipeline
+
+
+class TrainingPipelineConfig:
+ def __init__(self, timestamp = datetime.now()):
+ timestamp = timestamp.strftime("%m_%d_%Y_%H_%M_%S")
+ self.pipeline_name = training_pipeline.PIPELINE_NAME
+ self.artifact_name = training_pipeline.ARTIFACT_DIR
+ self.artifact_dir = os.path.join(self.artifact_name, timestamp)
+ self.model_dir=os.path.join("final_model")
+ self.timestamp:str = timestamp
+
+class Data_ingestion_config:
+ def __init__(self, training_pipeline_config:TrainingPipelineConfig):
+ self.data_ingestion_dir = os.path.join(training_pipeline_config.artifact_dir,training_pipeline.DATA_INGESTION_DIR_NAME)
+ self.feature_store_file_path = os.path.join(self.data_ingestion_dir, training_pipeline.DATA_INGESTION_FEATURE_STORE_DIR, training_pipeline.FILE_NAME)
+ self.train_file_path = os.path.join(self.data_ingestion_dir, training_pipeline.DATA_INGESTION_INGESTED_DIR, training_pipeline.TRAIN_FILE_NAME)
+ self.test_file_path = os.path.join(self.data_ingestion_dir, training_pipeline.DATA_INGESTION_INGESTED_DIR, training_pipeline.TEST_FILE_NAME)
+ self.database_name = training_pipeline.DATA_INGESTION_DATBASE_NANE
+ self.collection_name = training_pipeline.DATA_INGESTION_COLLECTION_NAME
+ self.train_test_split_ratio = training_pipeline.DATA_INGESTION_TRAIN_TEST_SPLIT_RATION
+
+class Data_validation_config:
+ def __init__(self,training_pipeline_config: TrainingPipelineConfig):
+ self.data_validation_dir:str = os.path.join(training_pipeline_config.artifact_dir, training_pipeline.DATA_VALIDATION_DIR_NAMR)
+ self.valid_data_dir:str = os.path.join(self.data_validation_dir, training_pipeline.DATA_VALIDATION_VALID_DIR)
+ self.invalid_data_dir:str = os.path.join(self.data_validation_dir, training_pipeline.DATA_VALIDATION_INVALID_DIR)
+ self.valid_train_file_path:str = os.path.join(self.valid_data_dir, training_pipeline.TRAIN_FILE_NAME)
+ self.valid_test_file_path:str = os.path.join(self.valid_data_dir, training_pipeline.TEST_FILE_NAME)
+ self.invalid_train_file_path:str = os.path.join(self.invalid_data_dir, training_pipeline.TRAIN_FILE_NAME)
+ self.invalid_test_file_path:str = os.path.join(self.invalid_data_dir, training_pipeline.TEST_FILE_NAME)
+ self.drift_report_file_path:str = os.path.join(self.data_validation_dir, training_pipeline.DATA_VALIDATION_DRIFT_REPORT_DIR, training_pipeline.DATA_VALIDATION_DRIFT_REPORT_FILE_NAME)
+
+
+class Data_transformation_config:
+ def __init__(self, training_pipeline_config: TrainingPipelineConfig):
+ self.data_transformation_dir:str = os.path.join(training_pipeline_config.artifact_dir, training_pipeline.DATA_TRANSFORMATION_DIR_NAME)
+
+ self.transformed_train_file_path: str = os.path.join( self.data_transformation_dir,training_pipeline.DATA_TRANSFORMATION_TRANSFORMED_DIR_NAME,
+ training_pipeline.TRAIN_FILE_NAME.replace("csv", "npy"),)
+
+ self.transformed_test_file_path: str = os.path.join(self.data_transformation_dir, training_pipeline.DATA_TRANSFORMATION_TRANSFORMED_DIR_NAME,
+ training_pipeline.TEST_FILE_NAME.replace("csv", "npy"),
+ )
+ self.transformed_object_file_path: str = os.path.join( self.data_transformation_dir, training_pipeline.DATA_TRANSFORMATION_TRANSFORMED_OBJECT_DIR,
+ training_pipeline.PREPROCESSING_OBJECT_FILE_NAME)
+
+class Model_trainer_config:
+ def __init__(self, training_pipeline_config: TrainingPipelineConfig):
+ self.model_trainer_dir:str = os.path.join(
+ training_pipeline_config.artifact_dir, training_pipeline.MODEL_TRAINER_DIR_NAME
+ )
+ self.trained_model_file_path:str = os.path.join(
+ self.model_trainer_dir, training_pipeline.MODEL_TRAINER_MODEL_DIR, training_pipeline.MODEL_TRAINER_MODEL_NAME
+ )
+ self.expected_accuracy:float = training_pipeline.MODEL_TRAINER_EXPECTED_SCORE
+ self.overfitting_underfitting_threshold = training_pipeline.MODEL_TRAINER_OVERFITTING_UNDERFITTING_THRESHOLD
+
\ No newline at end of file
diff --git a/src/exception/__init__.py b/src/exception/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/exception/__pycache__/__init__.cpython-310.pyc b/src/exception/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a706b82bc40d8c3b325c46856e35769d7c079e54
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diff --git a/src/exception/__pycache__/exception.cpython-310.pyc b/src/exception/__pycache__/exception.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..22d7adea12777bf61b323164e44e4d7ef75403ab
Binary files /dev/null and b/src/exception/__pycache__/exception.cpython-310.pyc differ
diff --git a/src/exception/exception.py b/src/exception/exception.py
new file mode 100644
index 0000000000000000000000000000000000000000..a88ce711933515687f8ab61112ed71910e903a4a
--- /dev/null
+++ b/src/exception/exception.py
@@ -0,0 +1,12 @@
+import sys
+from src.logging import logger
+
+class NetworkSecurityException(Exception):
+ def __init__(self, error_message, error_details:sys):
+ self.error_message = error_message
+ _, _, exc_tb = error_details.exc_info()
+ self.line_number = exc_tb.tb_lineno
+ self.file_name = exc_tb.tb_frame.f_code.co_filename
+
+ def __str__(self):
+ return f"Error occurred in python script name [{self.file_name}] line number [{self.line_number}] error message [{self.error_message}]"
\ No newline at end of file
diff --git a/src/logging/__init__.py b/src/logging/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/logging/__pycache__/__init__.cpython-310.pyc b/src/logging/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..28878ab4be770d8f3e23ef33880393ef01ee81f1
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diff --git a/src/logging/__pycache__/logger.cpython-310.pyc b/src/logging/__pycache__/logger.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..dda6b096416adc1e62e15b9ebde847ee9e9d271f
Binary files /dev/null and b/src/logging/__pycache__/logger.cpython-310.pyc differ
diff --git a/src/logging/logger.py b/src/logging/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..01aea8835fdc0340bfe41d4b1e993bd371b4f6c1
--- /dev/null
+++ b/src/logging/logger.py
@@ -0,0 +1,14 @@
+import logging
+import os
+from datetime import datetime
+
+LOG_FILE = f"{datetime.now().strftime('%m_%d_%Y_%H_%M_%S')}.log"
+logs_path= os.path.join(os.getcwd(), "logs", LOG_FILE)
+os.makedirs(logs_path, exist_ok=True)
+LOG_FILE_PATH = os.path.join(logs_path, LOG_FILE)
+
+logging.basicConfig(
+ filename = LOG_FILE_PATH,
+ format = "[%(asctime)s] %(lineno)d %(name)s - %(levelname)s - %(message)s",
+ level = logging.INFO
+)
\ No newline at end of file
diff --git a/src/pipeline/__init__.py b/src/pipeline/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/pipeline/__pycache__/__init__.cpython-310.pyc b/src/pipeline/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3694ebd9d55ef3677ad7c28f39c6a3c5e6e8ccc0
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diff --git a/src/pipeline/__pycache__/training_pipeline.cpython-310.pyc b/src/pipeline/__pycache__/training_pipeline.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ae70782f4e8f8302d072464787492eaaa0f4ae74
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diff --git a/src/pipeline/batch_prediction.py b/src/pipeline/batch_prediction.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/pipeline/training_pipeline.py b/src/pipeline/training_pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..3547f489441232fbcb2d1a80c1f867b747f811c5
--- /dev/null
+++ b/src/pipeline/training_pipeline.py
@@ -0,0 +1,107 @@
+import os,sys
+from src.exception.exception import NetworkSecurityException
+from src.logging.logger import logging
+from src.components.data_ingestion import DataIngestion
+from src.components.data_validation import DataValidation
+from src.components.data_transformation import DataTransformation
+from src.components.model_trainer import ModelTrainer
+from src.entity.artifact_entity import DataIngestionArtifact, DataValidationArtifact, DataTransformationArtifact, ModelTrainerArtifact
+from src.entity.config_entity import TrainingPipelineConfig, Data_ingestion_config, Data_transformation_config, Data_validation_config, Model_trainer_config
+from src.constant.training_pipeline import TRAINING_BUCKET_NAME
+from src.cloud.s3_syncer import s3sync
+class Trainingpipeline:
+ def __init__(self):
+ try:
+ self.training_pipeline_config = TrainingPipelineConfig()
+ self.s3_sync = s3sync()
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ def start_data_ingestion(self):
+ try:
+ self.data_ingestion_config = Data_ingestion_config(training_pipeline_config=self.training_pipeline_config)
+ logging.info("Data ingestion started!")
+ data_ingestion = DataIngestion(data_ingestion_config=self.data_ingestion_config)
+ data_ingestion_artifact= data_ingestion.initiate_data_ingestion()
+ logging.info("Data ingestion completed!")
+ return data_ingestion_artifact
+
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ def start_data_validation(self, data_ingestion_artifact: DataIngestionArtifact):
+ try:
+ data_validation_config = Data_validation_config(training_pipeline_config = self.training_pipeline_config)
+ Data_validation = DataValidation(data_ingestion_artifact, data_validation_config)
+ logging.info("Data validation started")
+ data_validation_artifacts = Data_validation.intiate_data_validation()
+ logging.info("Data validation completed")
+ print(data_validation_artifacts)
+ return data_validation_artifacts
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ def start_data_transformation(self,data_validation_artifact:DataValidationArtifact):
+ try:
+ data_transformation_config = Data_transformation_config(training_pipeline_config=self.training_pipeline_config)
+ data_transformation = DataTransformation(data_validation_artifact=data_validation_artifact, data_transformation_config=data_transformation_config)
+ logging.info("Data Transformation started !")
+ data_transformation_artifact = data_transformation.initiate_data_transformation()
+ logging.info("Data Transformation completed !")
+ return data_transformation_artifact
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ def start_model_training(self, data_transformation_artifact: DataTransformationArtifact)-> ModelTrainerArtifact:
+ try:
+ self.model_trainer_config:Model_trainer_config = Model_trainer_config(training_pipeline_config=self.training_pipeline_config)
+ model_trainer = ModelTrainer(
+ data_transformation_artifact= data_transformation_artifact,
+ model_trainer_config=self.model_trainer_config,
+ )
+ logging.info("Model training started!")
+ model_trainer_artifact = model_trainer.initiate_model_trainer()
+ logging.info("Model training completed!")
+ return model_trainer_artifact
+
+ # logging.info("Model training started")
+ # model_trainer_config = Model_trainer_config(traingning_pipeline_config)
+ # model_trainer = ModelTrainer(model_trainer_config=model_trainer_config, data_transformation_artifact=data_transformation_artifact)
+ # model_trainer_artifact = model_trainer.initiate_model_trainer()
+ # logging.info("Model training completed")
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+ # artifacts to s3 bucket
+ def sync_artifact_dir_to_s3(self):
+ try:
+ aws_bucket_url = f"s3://{TRAINING_BUCKET_NAME}/artifact/{self.training_pipeline_config.timestamp}"
+ self.s3_sync.sync_folder_to_s3(
+ folder=self.training_pipeline_config.artifact_dir,
+ aws_bucket_url=aws_bucket_url
+ )
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+ # model to s3 bucket
+ def sync_saved_model_to_s3(self):
+ try:
+ aws_bucket_url = f"s3://{TRAINING_BUCKET_NAME}/final_model/{self.training_pipeline_config.timestamp}"
+ self.s3_sync.sync_folder_to_s3(
+ folder=self.training_pipeline_config.model_dir, aws_bucket_url=aws_bucket_url
+ )
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+
+ def run_pipeline(self):
+ try:
+ data_ingestion_artifact = self.start_data_ingestion()
+ data_validation_artifact = self.start_data_validation(data_ingestion_artifact=data_ingestion_artifact)
+ data_transformation_artifact = self.start_data_transformation(data_validation_artifact=data_validation_artifact)
+ model_trainer_artifact = self.start_model_training(data_transformation_artifact=data_transformation_artifact)
+
+ self.sync_artifact_dir_to_s3()
+ self.sync_saved_model_to_s3()
+ return model_trainer_artifact
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
\ No newline at end of file
diff --git a/src/utils/__init__.py b/src/utils/__init__.py
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new file mode 100644
index 0000000000000000000000000000000000000000..f196725b4047b30649cd67ff1fc0c334d8c92538
Binary files /dev/null and b/src/utils/main_utils/__pycache__/utils.cpython-310.pyc differ
diff --git a/src/utils/main_utils/utils.py b/src/utils/main_utils/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..38f0b1c55da2f867c2c4bc8930a9ee1f86433692
--- /dev/null
+++ b/src/utils/main_utils/utils.py
@@ -0,0 +1,82 @@
+import yaml
+import pandas as pd
+from src.exception.exception import NetworkSecurityException
+from src.logging.logger import logging
+import os, sys
+import numpy as np
+import pickle
+from sklearn.model_selection import GridSearchCV
+from sklearn.metrics import r2_score
+
+def read_yaml_file(file_path:str) -> dict:
+ try:
+ with open(file_path, "rb") as yaml_file:
+ return yaml.safe_load(yaml_file)
+ except Exception as e:
+ raise NetworkSecurityException(e,sys) from e
+
+def write_yaml_file(file_path: str, content:object, replace:bool = False)-> None:
+ try:
+ if replace:
+ if os.path.exists(file_path):
+ os.remove(file_path)
+ os.makedirs(os.path.dirname(file_path), exist_ok=True)
+ with open(file_path, "w") as file:
+ yaml.dump(content, file)
+ except Exception as e:
+ raise NetworkSecurityException(e, sys)
+
+def save_np_array(file_path:str, array:np.array):
+ try:
+ dir_path = os.path.dirname(file_path)
+ os.makedirs(dir_path, exist_ok=True)
+ with open(file_path, "wb") as file_obj:
+ np.save(file_obj, array)
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+def save_object(file_path:str, obj:object) -> None:
+ try:
+ os.makedirs(os.path.dirname(file_path), exist_ok=True)
+ with open(file_path, "wb") as file_obj:
+ pickle.dump(obj, file_obj)
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+def load_object(file_path:str)-> object:
+ try:
+ if not os.path.exists(file_path):
+ raise Exception(f"The file: {file_path} is not exists")
+ with open(file_path, "rb") as file_obj:
+ print(file_obj)
+ return pickle.load(file_obj)
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+def load_numpy_array_data(file_path:str) -> np.array:
+ try:
+ with open(file_path, "rb") as file_obj:
+ return np.load(file_obj)
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+
+def evaluate_models(x_train, y_train, x_test, y_test, models, params):
+ try:
+ report = {}
+ for i in range(len(list(models))):
+ model = list(models.values())[i]
+ param = params[list(models.keys())[i]]
+ grid = GridSearchCV(model, param, cv=3)
+ grid.fit(x_train, y_train)
+ model.set_params(**grid.best_params_)
+ model.fit(x_train, y_train)
+ y_train_pred = model.predict(x_train)
+ y_test_pred = model.predict(x_test)
+ train_model_score = r2_score(y_train, y_train_pred)
+ test_model_score = r2_score(y_test, y_test_pred)
+ report[list(models.keys())[i]] = test_model_score
+ return report
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
\ No newline at end of file
diff --git a/src/utils/ml_utils/__init__.py b/src/utils/ml_utils/__init__.py
new file mode 100644
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diff --git a/src/utils/ml_utils/__pycache__/__init__.cpython-310.pyc b/src/utils/ml_utils/__pycache__/__init__.cpython-310.pyc
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diff --git a/src/utils/ml_utils/metric/__init__.py b/src/utils/ml_utils/metric/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/utils/ml_utils/metric/__pycache__/__init__.cpython-310.pyc b/src/utils/ml_utils/metric/__pycache__/__init__.cpython-310.pyc
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diff --git a/src/utils/ml_utils/metric/__pycache__/classification_metric.cpython-310.pyc b/src/utils/ml_utils/metric/__pycache__/classification_metric.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..97125a63925efbdfc0507d7ec5cca12f5da58dbe
Binary files /dev/null and b/src/utils/ml_utils/metric/__pycache__/classification_metric.cpython-310.pyc differ
diff --git a/src/utils/ml_utils/metric/classification_metric.py b/src/utils/ml_utils/metric/classification_metric.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ff2933137e4ea0d62fabb839d529c0cfcfe9528
--- /dev/null
+++ b/src/utils/ml_utils/metric/classification_metric.py
@@ -0,0 +1,14 @@
+from src.entity.artifact_entity import ClassificationMetricArtifact
+from src.exception.exception import NetworkSecurityException
+from sklearn.metrics import f1_score, precision_score, recall_score
+import sys
+
+def classification_score(y_true, y_pred):
+ try:
+ model_f1_score = f1_score(y_true, y_pred)
+ model_precision_score = precision_score(y_true, y_pred)
+ model_recall_score = recall_score(y_true, y_pred)
+ classification_metric = ClassificationMetricArtifact(f1_score=model_f1_score, precision_score=model_precision_score, recall_score=model_recall_score)
+ return classification_metric
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
\ No newline at end of file
diff --git a/src/utils/ml_utils/model/__init__.py b/src/utils/ml_utils/model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/utils/ml_utils/model/__pycache__/__init__.cpython-310.pyc b/src/utils/ml_utils/model/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..20254487ad7ae896308e18b7b49b11db86ddfe44
Binary files /dev/null and b/src/utils/ml_utils/model/__pycache__/__init__.cpython-310.pyc differ
diff --git a/src/utils/ml_utils/model/__pycache__/estimator.cpython-310.pyc b/src/utils/ml_utils/model/__pycache__/estimator.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d37256daf57d5ea4779743c61a1685e3ca9cc8f4
Binary files /dev/null and b/src/utils/ml_utils/model/__pycache__/estimator.cpython-310.pyc differ
diff --git a/src/utils/ml_utils/model/estimator.py b/src/utils/ml_utils/model/estimator.py
new file mode 100644
index 0000000000000000000000000000000000000000..27fa1ff9d8be74e9c8795ecaf0439c7fb8d1337d
--- /dev/null
+++ b/src/utils/ml_utils/model/estimator.py
@@ -0,0 +1,19 @@
+from src.constant.training_pipeline import SAVED_MODEL_DIR,MODEL_FILE_NAME
+from src.exception.exception import NetworkSecurityException
+from src.logging.logger import logging
+import os, sys
+
+class NetworkSecurityModel:
+ def __init__(self, preprocessing_object, trained_model_object):
+ self.preprocessing_object = preprocessing_object
+ self.trained_model_object = trained_model_object
+
+ def predict(self, X):
+ try:
+ transformed_feature = self.preprocessing_object.transform(X)
+ y_hat = self.trained_model_object.predict(transformed_feature)
+ return y_hat
+ except Exception as e:
+ raise NetworkSecurityException(e, sys) from e
+
+
\ No newline at end of file
diff --git a/templates/table.html b/templates/table.html
new file mode 100644
index 0000000000000000000000000000000000000000..1673561b8e9c744f5c6b7c8ec0a9d2206362823d
--- /dev/null
+++ b/templates/table.html
@@ -0,0 +1,21 @@
+
+
+
+ Data Table
+
+
+
+ Predicted Data
+ {{ table | safe }}
+
+
\ No newline at end of file
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new file mode 100644
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--- /dev/null
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@@ -0,0 +1,2256 @@
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+requires-python = ">=3.13"
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index 0000000000000000000000000000000000000000..cddfb2e9b129bd960a55d3bc8a655370b875d795
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