Update README to reflect Phase 5 completion

README.md

@@ -2,16 +2,22 @@
 
 Welcome to the Urdu Sentiment and Emotion Analysis Engine project! This repository contains the code for a multilingual NLP system that classifies sentiment (Positive, Negative, Neutral) and emotion (Joy, Anger, Fear, Sadness) from Urdu, Roman Urdu, and mixed-language text using a fine-tuned XLM-RoBERTa transformer.
 
-## Phase 1: Environment Setup & Training Pipeline
-This initial commit includes **Phase 1** of our project roadmap: setting up the environment, establishing the dataset loaders, and building the initial training scripts for the sentiment and emotion models.
-
-### Repository Structure (Phase 1)
-- `requirements.txt`: Environment dependencies required for training and inference.
+## Current Progress: Phase 5 (Backend Complete)
+The project has successfully completed Phases 1 through 5. The models have been fully trained on GPU clusters, critical label-mapping alignments have been applied across Urdu/Roman datasets, and the models are now integrated into a functional REST API using Flask.
+
+### Repository Structure
+- `app.py`: The Flask Web Server that exposes the `/predict` REST API endpoint.
+- `predictor.py`: Object-Oriented class handling the safe loading and inference of the XLM-RoBERTa models.
+- `requirements.txt`: Environment dependencies required for training and the web server.
+- `test_models.py`: A utility script to run interactive CLI inference without starting the server.
 - `training/`: Contains the core scripts for data processing and model fine-tuning.
-  - `dataset.py`: PyTorch `Dataset` implementation for loading and tokenizing text using XLM-RoBERTa.
-  - `train_sentiment.py`: Training script for the sentiment classification model.
-  - `train_emotion.py`: Training script for the emotion classification model.
-- `test_models.py`: A utility script to load trained models and run interactive inference.
+  - `dataset.py`: PyTorch `Dataset` implementation for loading and tokenizing text, now utilizing unified canonical label mappings.
+  - `train_sentiment.py`: Training script for the sentiment classification model (Multi-GPU enabled).
+  - `train_emotion.py`: Training script for the emotion classification model (Multi-GPU enabled).
+- `evaluation/`: Scripts for evaluating model performance and generating attention visualizations.
+- `results/`: Contains output matrices and evaluation reports.
+
+*(Note: The `models/` directory containing the 1GB `.safetensors` files is ignored via `.gitignore` due to size constraints. The models must be downloaded from the training environment and placed locally to run the API.)*
 
 ### Environment Setup
 To get started, create a virtual environment and install the required dependencies:
@@ -30,7 +36,12 @@ source urdu_env/bin/activate
 pip install -r requirements.txt
 ```
 
-### Next Steps
-The models are trained using HuggingFace's Trainer API on local datasets (which have been kept separate from the repository due to size constraints). In upcoming phases, we will introduce the Flask Backend (REST API), Frontend Dashboard, and cloud deployment via HuggingFace Spaces.
+### Running the API Server
+To start the backend server and test predictions:
+```bash
+python app.py
+```
+The server will boot up and listen on `http://127.0.0.1:5000`. You can send POST requests to `/predict` containing `{"text": "your urdu text"}` to receive JSON sentiment/emotion scores.
 
-Stay tuned for Phase 2 updates!
+### Next Steps (Phase 6)
+The upcoming Phase 6 will involve building the UI/Frontend (HTML/CSS/JS) to interact beautifully with the `app.py` backend!