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README.md

@@ -1,189 +1,39 @@
 ---
 title: CyberForge AI
 emoji: 🔐
-colorFrom: purple
-colorTo: blue
+colorFrom: blue
+colorTo: purple
 sdk: gradio
-sdk_version: 4.44.0
+sdk_version: 5.9.1
 app_file: app.py
-pinned: true
+pinned: false
 license: mit
 ---
 
-# 🔐 CyberForge AI - ML Training Platform
+# CyberForge AI - ML Training Platform
 
-**Train, Deploy, and Serve Cybersecurity Machine Learning Models**
+Train cybersecurity ML models and run Jupyter notebooks on Hugging Face.
 
-A comprehensive platform for training cybersecurity ML models in the cloud with Hugging Face Spaces integration.
+## Features
 
-## 🚀 Features
+- **Notebooks Tab**: Run ML pipeline notebooks directly in the cloud
+- **Train Model**: Upload CSV data and train security models
+- **Inference**: Load trained models and make predictions
+- **API**: REST API for integration
 
-- **📊 Model Training**: Upload datasets and train multiple ML models (Random Forest, Gradient Boosting, Neural Networks, Ensembles)
-- **🤖 Multiple Security Tasks**: Malware detection, phishing detection, network intrusion, anomaly detection, and more
-- **☁️ Cloud Training**: Leverage Hugging Face's infrastructure for training without local compute resources
-- **🔗 API Integration**: RESTful API endpoints for backend integration
-- **💾 Model Hub**: Upload trained models to Hugging Face Hub for sharing and deployment
+## Notebooks
 
-## 📦 Supported Security Tasks
+| # | Notebook | Purpose |
+|---|----------|---------|
+| 00 | environment_setup | System validation |
+| 01 | data_acquisition | Data collection |
+| 02 | feature_engineering | Feature extraction |
+| 03 | model_training | Train models |
+| 04 | agent_intelligence | AI reasoning |
+| 05 | model_validation | Testing |
+| 06 | backend_integration | API packaging |
+| 07 | deployment_artifacts | Deployment |
 
-| Task | Description |
-|------|-------------|
-| Malware Detection | Identify malicious software patterns |
-| Phishing Detection | Detect phishing URLs and emails |
-| Network Intrusion Detection | Identify network attack patterns |
-| Anomaly Detection | Detect unusual system behavior |
-| Botnet Detection | Identify botnet command & control traffic |
-| Web Attack Detection | Detect SQL injection, XSS, etc. |
-| Spam Detection | Filter spam messages |
-| Vulnerability Assessment | Assess system vulnerabilities |
-| DNS Tunneling Detection | Detect DNS-based data exfiltration |
-| Cryptomining Detection | Identify unauthorized mining activity |
+## Links
 
-## 🛠️ Model Types
-
-- **Random Forest**: Robust ensemble classifier
-- **Gradient Boosting**: High-performance gradient boosting
-- **Logistic Regression**: Fast baseline classifier
-- **Isolation Forest**: Unsupervised anomaly detection
-- **Neural Networks**: Deep learning models (when available)
-- **Ensemble Models**: Voting and stacking classifiers
-
-## 📖 How to Use
-
-### 1. Training a Model
-
-1. Go to the **🎯 Train Model** tab
-2. Upload your dataset (CSV, JSON, or Parquet)
-3. Select the security task type
-4. Choose a model type
-5. Enter the target column name
-6. Click **Train Model**
-
-### 2. Running Inference
-
-1. Go to the **🔮 Run Inference** tab
-2. Enter the model ID from training
-3. Provide input features as JSON
-4. Click **Run Inference**
-
-### 3. Backend Integration
-
-```python
-from gradio_client import Client
-
-# Connect to the Space
-client = Client("Che237/cyberforge")
-
-# Train a model
-result = client.predict(
-    file="path/to/dataset.csv",
-    task_type="Malware Detection",
-    model_type="Random Forest",
-    target_column="label",
-    test_size=0.2,
-    model_name="my_model",
-    api_name="/train_model"
-)
-
-# Run inference
-predictions = client.predict(
-    model_id="my_model_malware_detection_20240101_120000",
-    input_data='[{"feature1": 0.5, "feature2": 1.2}]',
-    api_name="/run_inference"
-)
-```
-
-### 4. Node.js Backend Integration
-
-```javascript
-const { Client } = require("@gradio/client");
-
-async function runPrediction(modelId, features) {
-    const client = await Client.connect("Che237/cyberforge");
-    const result = await client.predict("/run_inference", {
-        model_id: modelId,
-        input_data: JSON.stringify([features])
-    });
-    return JSON.parse(result.data);
-}
-
-// Usage
-const prediction = await runPrediction(
-    "cyberforge_model_malware_detection_20240101",
-    { src_bytes: 1000, dst_bytes: 500, protocol_type: 0 }
-);
-console.log(prediction);
-```
-
-## 📊 Dataset Format
-
-Your dataset should be in CSV, JSON, or Parquet format with:
-
-- **Features**: Numerical or categorical columns
-- **Target**: A column indicating the class/label (e.g., `label`, `is_malicious`, `attack_type`)
-
-### Example CSV Structure:
-
-```csv
-src_bytes,dst_bytes,protocol_type,service,flag,label
-1000,500,tcp,http,SF,normal
-5000,2000,udp,dns,REJ,attack
-...
-```
-
-## 🔗 API Endpoints
-
-| Endpoint | Method | Description |
-|----------|--------|-------------|
-| `/train_model` | POST | Train a new model |
-| `/run_inference` | POST | Run predictions |
-| `/list_trained_models` | GET | List available models |
-| `/upload_model_to_hub` | POST | Upload model to Hub |
-| `/download_model_from_hub` | POST | Download model from Hub |
-
-## 🏗️ Architecture
-
-```
-┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
-│   Your Backend  │ ──▶ │  HF Space (API)  │ ──▶ │  Trained Models │
-│   (Node.js)     │ ◀── │  (Gradio)        │ ◀── │  (pkl files)    │
-└─────────────────┘     └──────────────────┘     └─────────────────┘
-                               │
-                               ▼
-                        ┌──────────────────┐
-                        │  Hugging Face    │
-                        │  Model Hub       │
-                        └──────────────────┘
-```
-
-## 📁 Files
-
-- `app.py` - Main Gradio application
-- `trainer.py` - Advanced model training module
-- `hf_client.py` - Client library for backend integration
-- `requirements.txt` - Python dependencies
-
-## 🔧 Local Development
-
-```bash
-# Clone the space
-git clone https://huggingface.co/spaces/Che237/cyberforge
-
-# Install dependencies
-pip install -r requirements.txt
-
-# Run locally
-python app.py
-```
-
-## 📄 License
-
-MIT License - See LICENSE file for details.
-
-## 🤝 Contributing
-
-Contributions are welcome! Please feel free to submit a Pull Request.
-
----
-
-Built with ❤️ for the cybersecurity community
+- [Datasets](https://huggingface.co/datasets/Che237/cyberforge-datasets)