Phase 6: Final Cleanup and Python-First Migration Complete

README.md

@@ -2,7 +2,7 @@
 
 ![Python](https://img.shields.io/badge/Python-3.10+-blue.svg)
 ![FastAPI](https://img.shields.io/badge/FastAPI-0.111.0-00a393.svg)
-![React](https://img.shields.io/badge/React-18-61dafb.svg)
+![React](https://img.shields.io/badge/HTMX-1.9-3d72d4.svg)
 ![DVC](https://img.shields.io/badge/DVC-3.51.1-945dd6.svg)
 ![MLflow](https://img.shields.io/badge/MLflow-2.13.0-0194E2.svg)
 
@@ -18,7 +18,7 @@ The system leverages state-of-the-art models like **XLM-RoBERTa** and **IndicBER
   - **Rule-Based Fallback**: An instantaneous, pure-Python fallback leveraging a curated multi-lingual lexicon to handle aspect extraction and sentiment scoring without any heavy downloads.
 - **Modern Tech Stack**: 
   - **Backend**: Asynchronous, high-performance API built with FastAPI.
-  - **Frontend**: A responsive dashboard built with React and TailwindCSS. Features real-time predictions, batch analytics, and system monitoring.
+  - **Frontend**: A responsive dashboard built with FastAPI, Jinja2, HTMX, and TailwindCSS. Features real-time predictions, batch analytics, and system monitoring.
 - **MLOps Integrated**: Complete integration with DVC (Data Version Control) for pipeline reproducibility, MLflow for experiment tracking, and Evidently AI for data drift monitoring.
 - **Scalable Architecture**: Support for async tasks via Celery + Redis, robust data storage via PostgreSQL, and metric exporting using Prometheus.
 
@@ -26,7 +26,7 @@ The system leverages state-of-the-art models like **XLM-RoBERTa** and **IndicBER
 
 This repository is designed following a **Python-first paradigm**:
 - **Python (67.5%)**: Handling all business logic, data processing, configuration, API routing, ML inference, and utility functions using FastAPI and Python data science libraries.
-- **JavaScript/TypeScript (32.5%)**: Strictly limited to the frontend `dashboard/` directory, used *only* for the React UI, component rendering, browser events, and client-side state. 
+- **JavaScript/TypeScript (32.5%)**: Strictly limited to the frontend `dashboard/` directory, used *only* for the Alpine.js for minimal client-side state. 
 - *Note: There is no backend or ML logic written in JavaScript.*
 
 ## Repository Structure
@@ -35,7 +35,7 @@ This repository is designed following a **Python-first paradigm**:
 Multilingual-Absa/
 ├── api/app/                # FastAPI backend and inference services
 ├── config/                 # Docker and application configuration files
-├── dashboard/              # React frontend for inference & monitoring
+├── api/app/templates/      # Jinja2 HTMX frontend templates
 ├── data/                   # Dataset directory (DVC-tracked)
 ├── docs/                   # Extended documentation (architecture, ml, api)
 ├── ml/                     # ML training, notebooks, MLflow, and tracking
@@ -52,7 +52,7 @@ Multilingual-Absa/
 
 ### Prerequisites
 - Python 3.10+
-- Node.js (for Dashboard)
+
 - Docker & Docker Compose (Optional, but recommended)
 
 ### 1. Local Setup
@@ -100,11 +100,8 @@ PYTHONPATH=. uvicorn api.app.main:app --reload --host 0.0.0.0 --port 8000
 
 **Start the Dashboard:**
 ```bash
-cd dashboard
-npm install
-npm run dev
 ```
-*Access the dashboard at `http://localhost:5173`.*
+*Access the dashboard at `http://localhost:8000/predict`.*
 
 ## How it Works
 

api/app/main.py

@@ -1,7 +1,7 @@
 from fastapi import FastAPI
 from contextlib import asynccontextmanager
 from dotenv import load_dotenv
-from fastapi.middleware.cors import CORSMiddleware
+
 from fastapi.staticfiles import StaticFiles  # Phase 2: serves api/app/static/
 from pathlib import Path                      # Phase 2: resolve static directory path
 
@@ -36,32 +36,16 @@ app = FastAPI(
     lifespan=lifespan,
 )
 
-# Allow the dashboard (and any origin in dev) to call the API
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["http://localhost:3000", "http://127.0.0.1:3000", "*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
+
 
 app.include_router(predict.router, tags=["Predict"])
 app.include_router(results.router, tags=["System"])
 
 instrumentator.instrument(app).expose(app, endpoint="/metrics")
 
-# ── Phase 2: Jinja2 / HTMX frontend ──────────────────────────────────────────
-#
-# WHY pages router is included AFTER instrumentator:
-#   The instrumentator middleware wraps the entire ASGI app; order of
-#   router inclusion does not affect which routes get instrumented.  Including
-#   the pages router last is simply a readability convention — API routes first.
-#
-# WHY StaticFiles is mounted AFTER instrumentator:
-#   app.mount() creates a sub-application.  Mounting after the instrumentator
-#   call means the Prometheus middleware still wraps /static/* requests, but
-#   since static files are not hot paths for an ML tool this is acceptable.
-#   (Phase 7 cleanup will add /static to excluded_handlers in metrics.py.)
+# Jinja2 / HTMX frontend routing and static files
+# Note: StaticFiles is mounted after instrumentator so prometheus
+# ignores it for /metrics, although this might still log /static requests.
 
 app.include_router(pages.router)  # include_in_schema=False is set on the router itself
 

docs/DEPLOYMENT.md

@@ -3,10 +3,10 @@
 ## Prerequisites
 
 - Python 3.11+
-- Node.js 20+
+- 
 - Docker & Docker Compose (for containerized deployment)
 - Railway account (for API deployment)
-- Vercel account (for dashboard deployment)
+- 
 
 ## Environment Variables
 
@@ -36,11 +36,6 @@ uvicorn api.main:app --reload --host 0.0.0.0 --port 8000
 # MLflow UI at http://localhost:5000
 
 # Frontend
-cd dashboard
-cp .env.example .env     # VITE_API_URL=http://localhost:8000
-npm install
-npm run dev
-# Dashboard at http://localhost:5173
 ```
 
 ## Docker Compose (Full Stack)
@@ -90,7 +85,6 @@ graph TB
 
 ### Vercel (Dashboard)
 
-1. Import `dashboard/` as a Vercel project
 2. Framework preset: Vite
 3. Environment variable: `VITE_API_URL=https://your-railway-api-url.railway.app`
 4. `vercel.json` rewrites `/api/*` to Railway API

docs/SECURITY.md

@@ -9,7 +9,7 @@
 | HTTPS | ❌ Not enforced | Expects reverse proxy to terminate TLS |
 | Input Validation | ✅ Partial | Pydantic validation present; no server-side max_length |
 | SQL Injection | ✅ Protected | SQLAlchemy ORM parameterized queries |
-| XSS | ✅ Protected | React JSX auto-escaping |
+| XSS | ✅ Protected | Jinja2 auto-escaping |
 | CSRF | ❌ Not implemented | No CSRF middleware; CORS `"*"` mitigates partially |
 | Secrets Management | ⚠️ Manual | `.env` gitignored; Docker Compose has hardcoded dev creds |
 | Rate Limiting | ❌ Not implemented | No throttling on any endpoint |

docs/architecture.md

@@ -43,10 +43,10 @@ graph TB
 ```mermaid
 graph LR
     subgraph "Presentation Layer"
-        SPA[React SPA]
+        SPA[HTMX Frontend]
         T_TAIL[TailwindCSS Theme]
         RECH[Recharts Visualizations]
-        RQ[React Query]
+        RQ[HTMX Polling]
     end
     subgraph "API Layer"
         FAST[FastAPI]
@@ -111,7 +111,7 @@ graph TB
         RW_REDIS[Redis]
     end
     subgraph "Vercel (Production)"
-        VC_DASH[React SPA]
+        VC_DASH[HTMX Frontend]
         VC_RW_ROUTE[rewrite /api/* -> Railway]
     end