diff --git a/.gitignore b/.gitignore
index 8931b80..64a7bc3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -22,6 +22,7 @@ var/
 *.egg-info/
 .installed.cfg
 *.egg
+.github/
 
 # Virtual environments
 venv/
diff --git a/README.md b/README.md
index 169a14d..55b2e50 100644
--- a/README.md
+++ b/README.md
@@ -1,16 +1,54 @@
 # 🧠 AI Lab – Transformers CLI Playground
 
-> A **pedagogical and technical project** designed for AI practitioners and students to experiment with Hugging Face Transformers through an **interactive Command‑Line Interface (CLI)**.  
-> This playground provides ready‑to‑use NLP pipelines (Sentiment Analysis, Named Entity Recognition, Text Generation, Fill‑Mask, Moderation, etc.) in a modular, extensible, and educational codebase.
+> A **pedagogical and technical project** designed for AI practitioners and students to explore **Hugging Face Transformers** through an **interactive Command-Line Interface (CLI)** or a **REST API**.  
+> This playground provides ready-to-use NLP pipelines — including **Sentiment Analysis**, **Named Entity Recognition**, **Text Generation**, **Fill-Mask**, **Question Answering (QA)**, **Moderation**, and more — in a modular, extensible, and educational codebase.
+
+---
+
+<p align="center">
+  <img src="https://img.shields.io/badge/Python-3.13-blue.svg" alt="Python"/>
+  <img src="https://img.shields.io/badge/Built_with-Poetry-purple.svg" alt="Poetry"/>
+  <img src="https://img.shields.io/badge/🤗-Transformers-orange.svg" alt="Transformers"/>
+  <img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License"/>
+</p>
+
+---
+
+## 📑 Table of Contents
+
+-   [📚 Overview](#-overview)
+-   [🗂️ Project Structure](#️-project-structure)
+-   [⚙️ Installation](#️-installation)
+    -   [🧾 Option 1 – Poetry (Recommended)](#-option-1--poetry-recommended)
+    -   [📦 Option 2 – Pip + Requirements](#-option-2--pip--requirements)
+-   [▶️ Usage](#️-usage)
+    -   [🖥️ CLI Mode](#️-cli-mode)
+    -   [🌐 API Mode](#-api-mode)
+-   [📡 API Endpoints](#-api-endpoints)
+-   [🖥️ CLI Examples](#️-cli-examples)
+-   [🧠 Architecture Overview](#-architecture-overview)
+-   [⚙️ Configuration](#️-configuration)
+-   [🧩 Extending the Playground](#-extending-the-playground)
+-   [🧰 Troubleshooting](#-troubleshooting)
+-   [🧭 Development Guidelines](#-development-guidelines)
+-   [🧱 Roadmap](#-roadmap)
+-   [📜 License](#-license)
 
 ---
 
 ## 📚 Overview
 
-The **AI Lab – Transformers CLI Playground** allows you to explore multiple natural language processing tasks directly from the terminal.  
-Each task (e.g., sentiment, NER, text generation) is implemented as a **Command Module**, which interacts with a **Pipeline Module** built on top of the `transformers` library.
+The **AI Lab – Transformers CLI Playground** enables users to explore **multiple NLP tasks directly from the terminal or via HTTP APIs**.  
+Each task (sentiment, NER, text generation, etc.) is implemented as a **Command Module** that communicates with a **Pipeline Module** powered by Hugging Face’s `transformers` library.
 
-The lab is intentionally structured to demonstrate **clean software design for ML codebases** — with strict separation between configuration, pipelines, CLI logic, and display formatting.
+The project demonstrates **clean ML code architecture** with strict separation between:
+
+-   Configuration
+-   Pipelines
+-   CLI logic
+-   Display formatting
+
+It’s a great educational resource for learning **how to structure ML applications** professionally.
 
 ---
 
@@ -18,61 +56,54 @@ The lab is intentionally structured to demonstrate **clean software design for M
 
 ```text
 src/
-├── __init__.py
 ├── main.py                 # CLI entry point
 │
 ├── cli/
-│   ├── __init__.py
-│   ├── base.py             # CLICommand base class & interactive shell handler
-│   └── display.py          # Console formatting utilities (tables, colors, results)
+│   ├── base.py             # CLICommand base class & interactive shell
+│   └── display.py          # Console formatting utilities (colors, tables, results)
 │
 ├── commands/               # User-facing commands wrapping pipeline logic
-│   ├── __init__.py
 │   ├── sentiment.py        # Sentiment analysis command
-│   ├── fillmask.py         # Masked token prediction command
-│   ├── textgen.py          # Text generation command
-│   ├── ner.py              # Named Entity Recognition command
-│   └── moderation.py       # Toxicity / content moderation command
+│   ├── fillmask.py         # Masked token prediction
+│   ├── textgen.py          # Text generation
+│   ├── ner.py              # Named Entity Recognition
+│   ├── qa.py               # Question Answering (extractive)
+│   └── moderation.py       # Content moderation / toxicity detection
 │
-├── pipelines/              # Machine learning logic (Hugging Face Transformers)
-│   ├── __init__.py
+├── pipelines/              # ML logic based on Hugging Face pipelines
 │   ├── template.py         # Blueprint for creating new pipelines
 │   ├── sentiment.py
 │   ├── fillmask.py
 │   ├── textgen.py
 │   ├── ner.py
+│   ├── qa.py
 │   └── moderation.py
 │
 ├── api/
-│   ├── __init__.py
-│   ├── app.py              # FastAPI application with all endpoints
-│   ├── models.py           # Pydantic request/response models
-│   └── config.py           # API-specific configuration
+│   ├── app.py              # FastAPI app and endpoints
+│   ├── models.py           # Pydantic schemas
+│   └── config.py           # API configuration
 │
 └── config/
-    ├── __init__.py
-    └── settings.py         # Global configuration (default models, parameters)
+    └── settings.py         # Global configuration (models, params)
 ```
 
 ---
 
 ## ⚙️ Installation
 
-### 🧾 Option 1 – Using Poetry (Recommended)
+### 🧾 Option 1 – Poetry (Recommended)
 
-> Poetry is used as the main dependency manager.
+> Poetry is the main dependency manager for this project.
 
 ```bash
-# 1. Create and activate a new virtual environment
 poetry shell
-
-# 2. Install dependencies
 poetry install
 ```
 
-This will automatically install all dependencies declared in `pyproject.toml`, including **transformers**, **torch**, and **FastAPI** for the API mode.
+This installs all dependencies defined in `pyproject.toml` (including `transformers`, `torch`, and `fastapi`).
 
-To run the application inside the Poetry environment:
+Run the app:
 
 ```bash
 # CLI mode
@@ -84,21 +115,15 @@ poetry run python src/main.py --mode api
 
 ---
 
-### 📦 Option 2 – Using pip and requirements.txt
+### 📦 Option 2 – Pip + requirements.txt
 
-If you prefer using `requirements.txt` manually:
+If you prefer manual dependency management:
 
 ```bash
-# 1. Create a virtual environment
 python -m venv .venv
+source .venv/bin/activate      # Linux/macOS
+.venv\Scripts\Activate.ps1     # Windows
 
-# 2. Activate it
-# Linux/macOS
-source .venv/bin/activate
-# Windows PowerShell
-.venv\Scripts\Activate.ps1
-
-# 3. Install dependencies
 pip install -r requirements.txt
 ```
 
@@ -106,19 +131,15 @@ pip install -r requirements.txt
 
 ## ▶️ Usage
 
-The application supports two modes: **CLI** (interactive) and **API** (REST server).
-
 ### 🖥️ CLI Mode
 
-Launch the interactive CLI with:
+Run the interactive CLI:
 
 ```bash
 python -m src.main --mode cli
-# or, if using Poetry
-poetry run python src/main.py --mode cli
 ```
 
-You'll see an interactive menu listing the available commands:
+Interactive menu:
 
 ```
 Welcome to AI Lab - Transformers CLI Playground
@@ -127,151 +148,89 @@ Available commands:
   • fillmask      – Predict masked words in a sentence
   • textgen       – Generate text from a prompt
   • ner           – Extract named entities from text
+  • qa            – Answer questions from a context
   • moderation    – Detect toxic or unsafe content
 ```
 
+---
+
 ### 🌐 API Mode
 
-Launch the FastAPI server with:
+Run FastAPI server:
 
 ```bash
 python -m src.main --mode api
-# or with custom settings
+# Custom config
 python -m src.main --mode api --host 0.0.0.0 --port 8000 --reload
 ```
 
-The API will be available at:
+API Docs:
 
--   **Swagger Documentation**: http://localhost:8000/docs
--   **ReDoc Documentation**: http://localhost:8000/redoc
--   **OpenAPI Schema**: http://localhost:8000/openapi.json
+-   **Swagger** → http://localhost:8000/docs
+-   **ReDoc** → http://localhost:8000/redoc
+-   **OpenAPI** → http://localhost:8000/openapi.json
+
+---
 
 ## 📡 API Endpoints
 
-The REST API provides all CLI functionality through HTTP endpoints:
-
 ### Core Endpoints
 
-| Method | Endpoint  | Description                      |
-| ------ | --------- | -------------------------------- |
-| `GET`  | `/`       | Health check and API information |
-| `GET`  | `/health` | Detailed health status           |
+| Method | Endpoint  | Description               |
+| ------ | --------- | ------------------------- |
+| `GET`  | `/`       | Health check and API info |
+| `GET`  | `/health` | Detailed health status    |
 
 ### Individual Processing
 
-| Method | Endpoint      | Description              | Input                                                              |
-| ------ | ------------- | ------------------------ | ------------------------------------------------------------------ |
-| `POST` | `/sentiment`  | Analyze text sentiment   | `{"text": "string", "model": "optional"}`                          |
-| `POST` | `/fillmask`   | Fill masked words        | `{"text": "Hello [MASK]", "model": "optional"}`                    |
-| `POST` | `/textgen`    | Generate text            | `{"text": "prompt", "model": "optional"}`                          |
-| `POST` | `/ner`        | Named entity recognition | `{"text": "string", "model": "optional"}`                          |
-| `POST` | `/qa`         | Question answering       | `{"question": "string", "context": "string", "model": "optional"}` |
-| `POST` | `/moderation` | Content moderation       | `{"text": "string", "model": "optional"}`                          |
+| Method | Endpoint      | Description            |
+| ------ | ------------- | ---------------------- |
+| `POST` | `/sentiment`  | Analyze text sentiment |
+| `POST` | `/fillmask`   | Predict masked words   |
+| `POST` | `/textgen`    | Generate text          |
+| `POST` | `/ner`        | Extract named entities |
+| `POST` | `/qa`         | Question answering     |
+| `POST` | `/moderation` | Content moderation     |
 
 ### Batch Processing
 
-| Method | Endpoint            | Description                          | Input                                                |
-| ------ | ------------------- | ------------------------------------ | ---------------------------------------------------- |
-| `POST` | `/sentiment/batch`  | Process multiple texts               | `{"texts": ["text1", "text2"], "model": "optional"}` |
-| `POST` | `/fillmask/batch`   | Fill multiple masked texts           | `{"texts": ["text1 [MASK]"], "model": "optional"}`   |
-| `POST` | `/textgen/batch`    | Generate from multiple prompts       | `{"texts": ["prompt1"], "model": "optional"}`        |
-| `POST` | `/ner/batch`        | Extract entities from multiple texts | `{"texts": ["text1"], "model": "optional"}`          |
-| `POST` | `/moderation/batch` | Moderate multiple texts              | `{"texts": ["text1"], "model": "optional"}`          |
-
-### Example API Usage
-
-#### 🔹 Sentiment Analysis
-
-```bash
-curl -X POST "http://localhost:8000/sentiment" \
-     -H "Content-Type: application/json" \
-     -d '{"text": "I absolutely love this project!"}'
-```
-
-Response:
-
-```json
-{
-	"success": true,
-	"label": "POSITIVE",
-	"score": 0.998,
-	"model_used": "distilbert-base-uncased-finetuned-sst-2-english"
-}
-```
-
-#### 🔹 Named Entity Recognition
-
-```bash
-curl -X POST "http://localhost:8000/ner" \
-     -H "Content-Type: application/json" \
-     -d '{"text": "Elon Musk founded SpaceX in California."}'
-```
-
-Response:
-
-```json
-{
-	"success": true,
-	"entities": [
-		{ "word": "Elon Musk", "label": "PERSON", "score": 0.999 },
-		{ "word": "SpaceX", "label": "ORG", "score": 0.998 },
-		{ "word": "California", "label": "LOC", "score": 0.995 }
-	],
-	"model_used": "dslim/bert-base-NER"
-}
-```
-
-#### 🔹 Batch Processing
-
-```bash
-curl -X POST "http://localhost:8000/sentiment/batch" \
-     -H "Content-Type: application/json" \
-     -d '{"texts": ["Great product!", "Terrible experience", "It was okay"]}'
-```
-
-Response:
-
-```json
-{
-	"success": true,
-	"results": [
-		{ "label": "POSITIVE", "score": 0.998 },
-		{ "label": "NEGATIVE", "score": 0.995 },
-		{ "label": "NEUTRAL", "score": 0.876 }
-	],
-	"model_used": "distilbert-base-uncased-finetuned-sst-2-english"
-}
-```
+| Method | Endpoint            | Description                |
+| ------ | ------------------- | -------------------------- |
+| `POST` | `/sentiment/batch`  | Process multiple texts     |
+| `POST` | `/fillmask/batch`   | Fill multiple masked texts |
+| `POST` | `/textgen/batch`    | Generate from prompts      |
+| `POST` | `/ner/batch`        | Extract entities in batch  |
+| `POST` | `/qa/batch`         | Answer questions in batch  |
+| `POST` | `/moderation/batch` | Moderate multiple texts    |
 
 ---
 
 ## 🖥️ CLI Examples
 
-#### 🔹 Sentiment Analysis
+### 🔹 Sentiment Analysis
 
 ```text
 💬 Enter text: I absolutely love this project!
 → Sentiment: POSITIVE (score: 0.998)
 ```
 
-#### 🔹 Fill‑Mask
+### 🔹 Fill-Mask
 
 ```text
 💬 Enter text: The capital of France is [MASK].
 → Predictions:
   1) Paris      score: 0.87
   2) Lyon       score: 0.04
-  3) London     score: 0.02
 ```
 
-#### 🔹 Text Generation
+### 🔹 Text Generation
 
 ```text
 💬 Prompt: Once upon a time
 → Output: Once upon a time there was a young AI learning to code...
 ```
 
-#### 🔹 NER (Named Entity Recognition)
+### 🔹 NER
 
 ```text
 💬 Enter text: Elon Musk founded SpaceX in California.
@@ -281,7 +240,15 @@ Response:
   - California (LOC)
 ```
 
-#### 🔹 Moderation
+### 🔹 QA (Question Answering)
+
+```text
+💬 Enter question: What is the capital of France?
+💬 Enter context: France is a country in Europe. Its capital is Paris.
+→ Answer: The capital of France is Paris.
+```
+
+### 🔹 Moderation
 
 ```text
 💬 Enter text: I hate everything!
@@ -292,90 +259,34 @@ Response:
 
 ## 🧠 Architecture Overview
 
-The application supports dual-mode architecture: **CLI** (interactive) and **API** (REST server), both sharing the same pipeline layer:
+Both CLI and API share the **same pipeline layer**, ensuring code reusability and consistency.
 
 ### CLI Architecture
 
 ```text
-           ┌──────────────────────┐
-           │     InteractiveCLI   │
-           │ (src/cli/base.py)    │
-           └──────────┬───────────┘
-                      │
-                      ▼
-             ┌─────────────────┐
-             │   Command Layer │  ← e.g. sentiment.py
-             │ (user commands) │
-             └───────┬─────────┘
-                     │
-                     ▼
-             ┌─────────────────┐
-             │  Pipeline Layer │  ← e.g. pipelines/sentiment.py
-             │ (ML logic)      │
-             └───────┬─────────┘
-                     │
-                     ▼
-             ┌─────────────────┐
-             │ Display Layer   │  ← cli/display.py
-             │ (format output) │
-             └─────────────────┘
+InteractiveCLI → Command Layer → Pipeline Layer → Display Layer
 ```
 
 ### API Architecture
 
 ```text
-           ┌──────────────────────┐
-           │     FastAPI App      │
-           │ (src/api/app.py)     │
-           └──────────┬───────────┘
-                      │
-                      ▼
-             ┌─────────────────┐
-             │ Pydantic Models │  ← api/models.py
-             │ (validation)    │
-             └───────┬─────────┘
-                     │
-                     ▼
-             ┌─────────────────┐
-             │  Pipeline Layer │  ← e.g. pipelines/sentiment.py
-             │ (ML logic)      │  (shared with CLI)
-             └───────┬─────────┘
-                     │
-                     ▼
-             ┌─────────────────┐
-             │ JSON Response   │  ← automatic serialization
-             │ (HTTP output)   │
-             └─────────────────┘
+FastAPI App → Pydantic Models → Pipeline Layer → JSON Response
 ```
 
-### Key Concepts
-
-| Layer        | Description                                                                |
-| ------------ | -------------------------------------------------------------------------- |
-| **CLI**      | Manages user input/output, help menus, and navigation between commands.    |
-| **API**      | FastAPI application serving HTTP endpoints with automatic documentation.   |
-| **Command**  | Encapsulates a single user-facing operation (e.g., run sentiment).         |
-| **Pipeline** | Wraps Hugging Face's `transformers.pipeline()` to perform inference.       |
-| **Models**   | Pydantic schemas for request/response validation and serialization.        |
-| **Display**  | Handles clean console rendering (colored output, tables, JSON formatting). |
-
-### Key Concepts
-
-| Layer        | Description                                                                |
-| ------------ | -------------------------------------------------------------------------- |
-| **CLI**      | Manages user input/output, help menus, and navigation between commands.    |
-| **Command**  | Encapsulates a single user-facing operation (e.g., run sentiment).         |
-| **Pipeline** | Wraps Hugging Face’s `transformers.pipeline()` to perform inference.       |
-| **Display**  | Handles clean console rendering (colored output, tables, JSON formatting). |
-| **Config**   | Centralizes model names, limits, and global constants.                     |
+| Layer        | Description                                    |
+| ------------ | ---------------------------------------------- |
+| **CLI**      | Manages user input/output and navigation.      |
+| **API**      | Exposes endpoints with automatic OpenAPI docs. |
+| **Command**  | Encapsulates user-facing operations.           |
+| **Pipeline** | Wraps Hugging Face’s pipelines.                |
+| **Models**   | Validates requests/responses.                  |
+| **Display**  | Formats console output.                        |
 
 ---
 
 ## ⚙️ Configuration
 
-All configuration is centralized in `src/config/settings.py`.
-
-Example:
+All configuration is centralized in `src/config/settings.py`:
 
 ```python
 class Config:
@@ -384,140 +295,75 @@ class Config:
         "fillmask":  "bert-base-uncased",
         "textgen":   "gpt2",
         "ner":       "dslim/bert-base-NER",
-        "moderation":"unitary/toxic-bert"
+        "qa":        "distilbert-base-cased-distilled-squad",
+        "moderation":"unitary/toxic-bert",
     }
     MAX_LENGTH = 512
     BATCH_SIZE = 8
 ```
 
-You can easily modify model names to experiment with different checkpoints.
-
 ---
 
 ## 🧩 Extending the Playground
 
-To create a new experiment (e.g., keyword extraction):
+To add a new NLP experiment (e.g., keyword extraction):
 
-### For CLI Support
+1. Duplicate `src/pipelines/template.py` → `src/pipelines/keywords.py`
+2. Create a command: `src/commands/keywords.py`
+3. Register it in `src/main.py`
+4. Add Pydantic models and API endpoint
+5. Update `Config.DEFAULT_MODELS`
 
-1. **Duplicate** `src/pipelines/template.py` → `src/pipelines/keywords.py`  
-   Implement the `run()` or `analyze()` logic using a new Hugging Face pipeline.
-
-2. **Create a Command** in `src/commands/keywords.py` to interact with users.
-
-3. **Register the command** inside `src/main.py`:
-
-```python
-from src.commands.keywords import KeywordsCommand
-cli.register_command(KeywordsCommand())
-```
-
-### For API Support
-
-4. **Add Pydantic models** in `src/api/models.py`:
-
-```python
-class KeywordsRequest(BaseModel):
-    text: str
-    model: Optional[str] = None
-
-class KeywordsResponse(BaseModel):
-    success: bool
-    keywords: List[str]
-    model_used: str
-```
-
-5. **Add endpoint** in `src/api/app.py`:
-
-```python
-@app.post("/keywords", response_model=KeywordsResponse)
-async def extract_keywords(request: KeywordsRequest):
-    # Implementation using KeywordsAnalyzer pipeline
-    pass
-```
-
-6. **Update configuration** in `Config.DEFAULT_MODELS`.
-
-Both CLI and API will automatically share the same pipeline implementation!
-
----
-
-## 🧪 Testing
-
-You can use `pytest` for lightweight validation:
-
-```bash
-pip install pytest
-pytest -q
-```
-
-Recommended structure:
-
-```
-tests/
-├── test_sentiment.py
-├── test_textgen.py
-└── ...
-```
+Both CLI and API will automatically share this logic.
 
 ---
 
 ## 🧰 Troubleshooting
 
-### General Issues
+| Issue                    | Solution                |
+| ------------------------ | ----------------------- |
+| `transformers` not found | Activate your venv.     |
+| Torch install fails      | Use CPU-only wheel.     |
+| Models download slowly   | Cached after first use. |
+| Encoding issues          | Ensure UTF-8 terminal.  |
 
-| Issue                        | Cause / Solution                             |
-| ---------------------------- | -------------------------------------------- |
-| **`transformers` not found** | Check virtual environment activation.        |
-| **Torch fails to install**   | Install CPU-only version from PyTorch index. |
-| **Models download slowly**   | Hugging Face caches them after first run.    |
-| **Unicode / accents broken** | Ensure terminal encoding is UTF‑8.           |
+### API Issues
 
-### API-Specific Issues
-
-| Issue                         | Cause / Solution                                      |
-| ----------------------------- | ----------------------------------------------------- |
-| **`FastAPI` not found**       | Install with `pip install fastapi uvicorn[standard]`. |
-| **Port already in use**       | Use `--port 8001` or kill process on port 8000.       |
-| **CORS errors in browser**    | Check `allow_origins` in `src/api/config.py`.         |
-| **422 Validation Error**      | Check request body matches Pydantic models.           |
-| **500 Internal Server Error** | Check model loading and pipeline initialization.      |
-
-### Quick API Health Check
-
-```bash
-# Test if API is running
-curl http://localhost:8000/health
-
-# Test basic endpoint
-curl -X POST "http://localhost:8000/sentiment" \
-     -H "Content-Type: application/json" \
-     -d '{"text": "test"}'
-```
+| Issue                | Solution                                |
+| -------------------- | --------------------------------------- |
+| `FastAPI` missing    | `pip install fastapi uvicorn[standard]` |
+| Port in use          | Change with `--port 8001`               |
+| CORS error           | Edit `allow_origins` in `api/config.py` |
+| Validation error 422 | Check request body                      |
+| 500 error            | Verify model loading                    |
 
 ---
 
 ## 🧭 Development Guidelines
 
--   Keep **Command** classes lightweight — no ML logic inside them.
--   Reuse the **Pipeline Template** for new experiments.
--   Format outputs consistently via the `DisplayFormatter`.
--   Document all new models or commands in `README.md` and `settings.py`.
+-   Keep command classes lightweight (no ML inside)
+-   Use the pipeline template for new tasks
+-   Format all outputs via `DisplayFormatter`
+-   Document new commands and models
 
 ---
 
 ## 🧱 Roadmap
 
--   [ ] Add non-interactive CLI flags (`--text`, `--task`)
--   [ ] Add multilingual model options
--   [ ] Add automatic test coverage
--   [ ] Add logging and profiling utilities
--   [ ] Add export to JSON/CSV results
+-   [ ] Non-interactive CLI flags (`--text`, `--task`)
+-   [ ] Multilingual models
+-   [ ] Test coverage
+-   [ ] Logging & profiling
+-   [ ] Export to JSON/CSV
 
 ---
 
 ## 📜 License
 
-This project is licensed under the [MIT License](./LICENSE) — feel free to use it, modify it, and share it!
+Licensed under the [MIT License](./LICENSE).  
+You are free to use, modify, and distribute this project.
 
 ---
+
+✨ **End of Documentation**  
+_The AI Lab – Transformers CLI Playground: built for learning, experimenting, and sharing NLP excellence._
diff --git a/src/api/app.py b/src/api/app.py
index 50bd1f7..dded0e0 100644
--- a/src/api/app.py
+++ b/src/api/app.py
@@ -10,7 +10,7 @@ import logging
 from .models import (
     TextRequest, TextListRequest, QARequest, FillMaskRequest,
     SentimentResponse, NERResponse, QAResponse, FillMaskResponse,
-    ModerationResponse, TextGenResponse, BatchResponse, ErrorResponse
+    ModerationResponse, TextGenResponse, BatchResponse
 )
 
 # Global pipeline instances
diff --git a/src/api/config.py b/src/api/config.py
index 7462861..f5372fc 100644
--- a/src/api/config.py
+++ b/src/api/config.py
@@ -2,6 +2,7 @@
 API configuration settings
 """
 from typing import Dict, Any
+from src.config.settings import Config
 
 
 class APIConfig:
@@ -25,21 +26,6 @@ class APIConfig:
     MAX_TEXT_LENGTH = 10000
     MAX_BATCH_SIZE = 100
     
-    # Model defaults
-    DEFAULT_MODELS: Dict[str, str] = {
-        "sentiment": "cardiffnlp/twitter-roberta-base-sentiment-latest",
-        "ner": "dbmdz/bert-large-cased-finetuned-conll03-english",
-        "qa": "distilbert-base-cased-distilled-squad",
-        "fillmask": "bert-base-uncased",
-        "moderation": "martin-ha/toxic-comment-model",
-        "textgen": "gpt2"
-    }
-    
-    @classmethod
-    def get_default_model(cls, task: str) -> str:
-        """Get default model for a task"""
-        return cls.DEFAULT_MODELS.get(task, "")
-    
     @classmethod
     def get_all_settings(cls) -> Dict[str, Any]:
         """Get all configuration settings"""
@@ -57,5 +43,4 @@ class APIConfig:
                 "max_text_length": cls.MAX_TEXT_LENGTH,
                 "max_batch_size": cls.MAX_BATCH_SIZE
             },
-            "default_models": cls.DEFAULT_MODELS
         }
\ No newline at end of file
diff --git a/src/config/settings.py b/src/config/settings.py
index a68bb00..95ccbd1 100644
--- a/src/config/settings.py
+++ b/src/config/settings.py
@@ -15,11 +15,11 @@ class Config:
     
     # Default models
     DEFAULT_MODELS = {
-        "sentiment": "cardiffnlp/twitter-roberta-base-sentiment-latest",
-        "fillmask": "distilbert-base-uncased",
-        "textgen": "gpt2",
-        "moderation": "unitary/toxic-bert",
-        "ner": "dbmdz/bert-large-cased-finetuned-conll03-english",
+        "sentiment": "distilbert-base-uncased-finetuned-sst-2-english",
+        "fillmask":  "bert-base-uncased",
+        "textgen":   "gpt2",
+        "ner":       "dslim/bert-base-NER",
+        "moderation":"unitary/toxic-bert",
         "qa": "distilbert-base-cased-distilled-squad",
     }