Enhance README with Mermaid diagrams for architecture, DFD, and GitOps pipeline

README.md

@@ -8,22 +8,134 @@ This project showcases professional skills in **MLOps, Cloud-Native Architecture
 
 The platform is built on a robust, self-hosted Kubernetes (K3s) cluster with a focus on data sovereignty and scalability.
 
-- **Source Control & CI/CD**: [Gitea](https://git.techarvest.co.zw) (Self-hosted GitHub alternative)
+```mermaid
-- **Infrastructure as Code**: Terraform (Managing K3s Namespaces & Quotas)
+graph TD
-- **GitOps**: ArgoCD (Automated deployment from Git to Cluster)
+    subgraph "Frontend & Entry"
-- **Experiment Tracking**: [MLflow](https://ml.techarvest.co.zw) (Model versioning & metrics)
+        WEB[React 19 Frontend]
-- **Interactive Workspace**: [JupyterLab](https://lab.techarvest.co.zw) (Data science & training)
+        ING[Nginx Ingress]
-- **Spatial Database**: Standalone PostgreSQL + PostGIS (Port 5433)
+    end
-- **Object Storage**: MinIO (S3-compatible storage for datasets, baselines, and models)
+
-- **Frontend**: React 19 + OpenLayers (Parallel loading of baselines and ML predictions)
+    subgraph "Core Services (geocrop namespace)"
-- **Backend**: FastAPI + Redis Queue (Job orchestration)
+        API[FastAPI Backend]
-- **Visualization**: TiTiler (Dynamic tile server for Cloud Optimized GeoTIFFs)
+        RQ[Redis Queue]
+        WORKER[ML Inference Worker]
+        TILER[TiTiler Dynamic Server]
+    end
+
+    subgraph "MLOps & Infra"
+        GITEA[Gitea Source Control]
+        ARGO[ArgoCD GitOps]
+        MLF[MLflow Tracking]
+        JUPYTER[JupyterLab Workspace]
+    end
+
+    subgraph "Storage & Data"
+        MINIO[(MinIO S3 Storage)]
+        POSTGIS[(Postgres + PostGIS)]
+    end
+
+    %% Flow
+    WEB --> ING
+    ING --> API
+    API --> RQ
+    RQ --> WORKER
+    WORKER --> MINIO
+    WORKER --> POSTGIS
+    TILER --> MINIO
+    WEB --> TILER
+    ARGO --> GITEA
+    ARGO --> ING
+    JUPYTER --> MINIO
+    MLF --> POSTGIS
+```
+
+## 📊 System Data Flow (DFD)
+
+How data moves from raw satellite imagery to final crop-type predictions:
+
+```mermaid
+graph LR
+    subgraph "External Sources"
+        DEA[Digital Earth Africa STAC]
+    end
+
+    subgraph "Storage (MinIO)"
+        DS[(/geocrop-datasets)]
+        BS[(/geocrop-baselines)]
+        MD[(/geocrop-models)]
+        RS[(/geocrop-results)]
+    end
+
+    subgraph "Processing"
+        TRAIN[Jupyter Training]
+        INFER[Inference Worker]
+    end
+
+    %% Data movement
+    DEA -- "Sentinel-2 Imagery" --> INFER
+    DS -- "CSV Batches" --> TRAIN
+    TRAIN -- "Trained Models" --> MD
+    MD -- "Model Load" --> INFER
+    BS -- "DW TIFFs" --> INFER
+    INFER -- "Classification COG" --> RS
+    RS -- "Map Tiles" --> WEB[Frontend Visualization]
+```
 
 ## 🗺️ UX Data Flow: Parallel Loading Strategy
 
 To ensure a seamless user experience, the system implements a dual-loading strategy:
-1. **Instant Context**: While waiting for ML inference, Dynamic World (DW) TIFF baselines (2015-2025) are immediately served from MinIO via TiTiler.
+
-2. **Asynchronous Inference**: The ML worker processes heavy classification tasks in the background and overlays high-resolution predictions once complete.
+```mermaid
+sequenceDiagram
+    participant U as User (Frontend)
+    participant T as TiTiler (S3 Proxy)
+    participant A as FastAPI
+    participant W as ML Worker
+    participant M as MinIO
+
+    U->>A: Submit Job (AOI + Year)
+    A->>U: Job ID (Accepted)
+    
+    par Instant Visual Context
+        U->>T: Fetch Baseline Tiles (DW)
+        T->>M: Stream Baseline COG
+        M->>T: 
+        T->>U: Render Baseline Map
+    and Asynchronous Prediction
+        A->>W: Enqueue Task
+        W->>M: Fetch Model & Data
+        W->>W: Run Inference & Post-processing
+        W->>M: Upload Prediction COG
+        loop Polling
+            U->>A: Get Status?
+            A-->>U: Processing...
+        end
+        W->>A: Job Complete
+        U->>A: Get Status?
+        A->>U: Prediction URL
+        U->>T: Fetch Prediction Tiles
+        T->>M: Stream Prediction COG
+        T->>U: Overlay High-Res Result
+    end
+```
+
+## 🚀 Deployment & GitOps Pipeline
+
+```mermaid
+graph LR
+    DEV[Developer] -->|Push| GITEA[Gitea]
+    
+    subgraph "CI/CD Pipeline"
+        GITEA -->|Trigger| GA[Gitea Actions]
+        GA -->|Build & Push| DH[Docker Hub: frankchine]
+    end
+
+    subgraph "GitOps Sync"
+        ARGO[ArgoCD] -->|Monitor| GITEA
+        DH -->|Image Pull| K3S[K3s Cluster]
+        ARGO -->|Apply Manifests| K3S
+    end
+```
 
 ## 🛠️ Training Workflow
 
@@ -48,13 +160,6 @@ df = storage.load_dataset('geocrop-datasets', 'batch_1.csv')
 storage.upload_file('model.pkl', 'geocrop-models', 'Zimbabwe_Ensemble_Model.pkl')
 ```
 
-## 🚀 Deployment & GitOps
-
-The platform follows a strict **GitOps** workflow:
-1. All changes are committed to the `geocrop-platform` repository on Gitea.
-2. Gitea Actions build and push containers to Docker Hub (`frankchine`).
-3. ArgoCD monitors the `k8s/base` directory and automatically synchronizes the cluster state.
-
 ## 🖥️ Service Registry
 
 - **Portfolio Frontend**: [portfolio.techarvest.co.zw](https://portfolio.techarvest.co.zw)