Restore images with strict LFS tracking

Temporarily remove images to fix LFS sync

Convert images to Git LFS

feat: Add Hugging Face Hub sync workflow, MongoDB connection verification script, and update Dockerfile for Python 3.11 and non-root user.

Fix data ingestion path, update README images, and enable reload

Restore history and apply final clean deployment

docs: Update README title, emoji, colors, and pinned status, and remove `app_port`.

Optimize deployment config

chore: delete file

chore: add project-specific build, model, and data directories to .gitignore

feat: Initialize project structure with ML components, utilities, exception handling, and S3 integration.

chore: remove unused file

feat: Implement web application for phishing URL detection with new UI, integrated model training, and prediction endpoints, removing old artifacts.

Removed unused docker-compose.yml (single container deploy)

Fix rm tmp

Fixed the docker-compose.yml (using small mongodb image) and main.yaml (in Continuous-Deployment freeing up the disk space)

Fixing Cleanup & Deploy with Docker Compose

docker-compose file spelling 😂

full CI/CD pipeline with Docker Compose + MongoDB fallback

removed the unset-current-credentials and mask-aws-account-id from Continuous-Deployment and also did the changed in the permission of GitHubRunnerECRS3Policy

Changes in the Continuous deployment

Excelling my carrier in the field of DevOps i think the Continuous Deployment has only given me continuous erros it seems now now i understand why we need the DevOps guys

I am tried now solving this , the project has now officially entered from MLOps project to DevOps project

❌ Removed:

Changed the port to 8080 and dagshub init enavles uding Variable "ENABLE_DAGSHUB"

Fixing version error in configure-aws-credentials @v1 to @v4

Add AWS credentials configuration step to Continuous Delivery job in main.yaml

Add AWS region setup step to Continuous Deployment job in main.yaml

Refactor main.yaml to remove AWS credentials configuration step and streamline continuous deployment steps

Remove redundant AWS credentials configuration step from Continuous Deployment job

Update main.yaml and app.py for improved CI/CD workflow and application accessibility

Add Continuous Deployment job to main.yaml for ECR image management

Refactor Dockerfile to separate awscli installation and improve dependency management; remove pywin32 from requirements

Refactor Dockerfile to streamline AWS CLI installation and improve layer caching

Add build, tag, and push image step to ECR in CI/CD workflow

Add build and push ECR image job to CI/CD workflow

Fix workflow syntax for linting and testing steps in main.yaml

Deployment changes

Cloud pushed and s3 data storage implemented

Pipeline completed and fastapi created

mlflow and dagshub and you can see the models and compare them side by side and pick the best model according to you , I am using test_f1 as decision metric

mlflow implemented in model_trainer.py

Model Trainer And Evaluation With Hyperparameter Tuning

Data Transformation done

Data Validation done

Data Ingestion done

ETL pipeline done

Exception Handling and logging done

setup.py ready

Created file structure