Update README.md

README.md

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   https://cdn-uploads.huggingface.co/production/uploads/67c714e90b99a2332e310979/L02-prFfHa7eBZGVf4uvR.jpeg
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-# Purple Team Code Workbench - Streamlit Starter
+# Purple Team Code Workbench
 
-This package contains a working Streamlit implementation based on the provided Purple Team Code Workbench project spec.
+<p align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/67c714e90b99a2332e310979/L02-prFfHa7eBZGVf4uvR.jpeg" width="720" alt="Purple Team Code Workbench Banner"/>
+</p>
 
-## Files
+<p align="center">
+  <strong>
+    Streamlit-powered code generation and workflow orchestration
+    surface for authorized purple-team operations.
+  </strong>
+</p>
 
-- `app.py` - main Streamlit application
-- `requirements.txt` - Python dependencies
-- `.streamlit/config.toml` - theme and server defaults
+<p align="center">
+  <img alt="Python" src="https://img.shields.io/badge/python-3.11%2B-blue">
+  <img alt="Streamlit" src="https://img.shields.io/badge/streamlit-1.57.0-red">
+  <img alt="License" src="https://img.shields.io/badge/license-Apache--2.0-green">
+  <img alt="Security" src="https://img.shields.io/badge/focus-purple--team-purple">
+</p>
 
-## Run locally
+---
+
+## Overview
+
+Purple Team Code Workbench is an AI-assisted cybersecurity experimentation environment designed for defensive researchers, purple-team operators, and security engineers.
+
+The platform combines:
+
+- LLM-driven code generation
+- Workflow prototyping
+- Adversarial simulation
+- Structured findings management
+- Report generation
+- Human-in-the-loop operational control
+
+inside a lightweight Streamlit interface.
+
+This repository currently includes a working starter implementation with a scope gate, workflow prompt builder, structured findings manager, hash-linked evidence ledger, model profile panel, and Markdown report export. It is designed to run locally or as a Hugging Face Streamlit Space without requiring a GPU.
+
+The architecture emphasizes modular orchestration, reproducible workflows, and human-supervised operational control.
+
+The platform focuses on:
+
+- Authorized assessment workflows
+- Defensive and adversarial simulation support
+- Code generation for security operations
+- Evidence handling and finding management
+- Prompt-assisted workflow acceleration
+- Report artifact generation
+- Research and analysis augmentation
+
+The system is intentionally structured around controlled workflows rather than unrestricted autonomous execution.
+
+---
+
+## Why Purple Team?
+
+Purple-team methodology combines offensive security simulation with defensive validation and detection engineering.
+
+This workbench is designed to support collaborative workflows between:
+
+- security researchers
+- defenders
+- detection engineers
+- SOC analysts
+- incident responders
+- application security teams
+
+The focus is operational learning, validation, and resilience improvement rather than isolated offensive capability.
+
+---
+
+## Safety & Intended Use
+
+Purple Team Code Workbench is intended for:
+
+- Authorized security testing
+- Defensive security research
+- Secure software experimentation
+- Educational cybersecurity workflows
+- Purple-team simulation and analysis
+
+This project is not intended for:
+
+- Unauthorized access
+- Malware deployment
+- Credential theft
+- Persistence mechanisms
+- Destructive operations
+- Autonomous offensive activity
+
+Users are responsible for complying with applicable laws, organizational policies, and authorization requirements.
+
+---
+
+## Non-Goals
+
+This project is not intended to provide:
+
+- autonomous offensive operations
+- malware automation
+- persistence tooling
+- uncontrolled exploitation workflows
+- credential harvesting systems
+
+---
+
+## Model Roles
+
+| Model | Purpose |
+|---|---|
+| HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive | Experimental reasoning and adversarial simulation support |
+| DeepHat/DeepHat-V1-7B | Security-oriented coding and workflow assistance |
+| meta-llama/Meta-Llama-3-8B-Instruct | General reasoning and structured instruction following |
+
+---
+
+## Runtime Environment
+
+- Python 3.11
+- Streamlit 1.57.0
+- pandas
+- CPU-compatible deployment
+- Optional GPU acceleration if model inference is added later
+- Hugging Face Streamlit Space compatibility
+
+The included starter app is intentionally lightweight. It can run locally or inside a Hugging Face Space without requiring a GPU.
+
+---
+
+## Core Design Principles
+
+### Scope-First Architecture
+
+Every workflow begins with explicit authorization and target definition.
+
+The system is designed to reduce:
+
+- accidental scope drift
+- unsafe automation
+- uncontrolled execution paths
+- ambiguous operational state
+
+---
+
+### Human-in-the-Loop Control
+
+The workbench assists analysts and engineers rather than replacing operational judgment.
+
+Generation ≠ execution.
+
+All generated output should be reviewed before use.
+
+---
+
+### Evidence-Centric Workflow
+
+Outputs are treated as operational artifacts:
+
+- findings
+- prompts
+- code snippets
+- reports
+- remediation notes
+- validation records
+
+The system emphasizes traceability and reproducibility over opaque AI behavior.
+
+A tragically rare design choice in modern software tooling.
+
+---
+
+## Features
+
+### Current Capabilities
+
+- Streamlit-based UI
+- Scope-gated workflow controls
+- Security code generation surface
+- Passive recon helpers
+- Structured findings management
+- Markdown report export
+- Multi-model workflow support
+- Hugging Face Space deployment compatibility
+- CPU-compatible starter runtime
+- Session-state based local workflow records
+- JSON, CSV, and Markdown exports
+
+---
+
+### Included Starter Files
+
+The current starter package contains:
+
+| File | Purpose |
+|---|---|
+| `app.py` | Main Streamlit application |
+| `requirements.txt` | Python dependencies |
+| `README.md` | Hugging Face Space metadata and project documentation |
+| `.streamlit/config.toml` | Theme and server defaults |
+
+The app does **not** call external model APIs by default. The configured model list is used as a profile/routing layer so inference can be added later without hiding provider behavior inside the UI. Because invisible API calls are how dashboards become haunted.
+
+---
+
+### Planned Capabilities
+
+- Workflow templates
+- Prompt chaining
+- Agent orchestration
+- Typed finding schemas
+- Multi-provider inference routing
+- Local LLM runtime support
+- Evidence graphing
+- Drift-aware execution state
+- Report diff/version tracking
+- LangGraph integration
+- MCP-compatible tool surfaces
+
+---
+
+## Supported Models
+
+Current configured models:
+
+| Model | Purpose |
+|---|---|
+| HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive | Experimental coding and reasoning |
+| DeepHat/DeepHat-V1-7B | Security-oriented generation workflows |
+| Meta-Llama-3-8B-Instruct | General-purpose assistant workflows |
+
+Model availability depends on provider access and deployment configuration.
+
+---
+
+## Repository Structure
+
+Current starter package:
+
+```text
+.
+├── app.py
+├── requirements.txt
+├── README.md
+└── .streamlit/
+    └── config.toml
+```
+
+Recommended expanded structure:
+
+```text
+.
+├── app.py
+├── requirements.txt
+├── README.md
+├── assets/
+├── workflows/
+├── prompts/
+├── reports/
+├── utils/
+├── components/
+└── tests/
+```
+
+Recommended modularization:
+
+| Directory | Purpose |
+|---|---|
+| workflows/ | Workflow orchestration logic |
+| prompts/ | Prompt templates and chains |
+| reports/ | Generated report artifacts |
+| utils/ | Shared utilities |
+| components/ | Streamlit UI components |
+| assets/ | Static images and branding |
+| tests/ | Unit tests and workflow validation checks |
+
+---
+
+## Installation
+
+### Local Development
+
+Clone the repository:
 
 ```bash
-python -m venv .venv
-.venv\Scripts\activate
-pip install -r requirements.txt
-streamlit run app.py
+git clone https://github.com/your-org/purple-team-code-workbench.git
+cd purple-team-code-workbench
 ```
 
-On Linux/macOS:
+Create a virtual environment:
 
 ```bash
 python -m venv .venv
+```
+
+Activate the environment:
+
+#### Linux/macOS
+
+```bash
 source .venv/bin/activate
+```
+
+#### Windows PowerShell
+
+```powershell
+.venv\Scripts\Activate.ps1
+```
+
+#### Windows Command Prompt
+
+```cmd
+.venv\Scripts\activate.bat
+```
+
+Install dependencies:
+
+```bash
 pip install -r requirements.txt
+```
+
+Run the application:
+
+```bash
+streamlit run app.py
+```
+
+---
+
+## Hugging Face Spaces Deployment
+
+This repository is compatible with:
+
+- Hugging Face Streamlit Spaces
+- CPU deployments
+- OAuth-enabled Spaces
+- External inference providers
+
+The README front matter already includes Space metadata:
+
+```yaml
+sdk: streamlit
+sdk_version: 1.57.0
+python_version: "3.11"
+app_file: app.py
+license: apache-2.0
+suggested_hardware: cpu-upgrade
+suggested_storage: small
+```
+
+Basic deployment path:
+
+1. Create a new Hugging Face Space.
+2. Select **Streamlit** as the SDK.
+3. Upload `app.py`, `requirements.txt`, `README.md`, and `.streamlit/config.toml`.
+4. Confirm the Space builds against Python 3.11 and Streamlit 1.57.0.
+5. Add secrets only if external inference providers are integrated later.
+
+---
+
+## Inference Providers
+
+The starter app does not include live inference calls by default.
+
+Future provider integrations may use:
+
+- Hugging Face Inference Providers
+- External API routing
+- Local runtime configuration
+- OAuth authentication state
+- Deployment hardware constraints
+
+Recommended provider design:
+
+- keep API keys in environment variables or Space secrets
+- separate provider logic from UI components
+- log model profile, prompt template, and output metadata
+- avoid storing secrets in reports, findings, or exported prompt artifacts
+- treat model output as untrusted until reviewed
+
+---
+
+## Recommended Operational Controls
+
+If deploying in production environments:
+
+- Require authentication
+- Log workflow activity
+- Separate trusted/untrusted prompts
+- Sandbox execution environments
+- Restrict outbound networking
+- Validate generated artifacts
+- Maintain immutable audit trails
+- Enforce scoped execution policies
+- Require approval before provider calls
+- Prevent secrets from entering exported reports
+- Separate draft generation from operational action
+
+---
+
+## Example Workflow
+
+```text
+Scope Definition
+        ↓
+Passive Recon
+        ↓
+Evidence Collection
+        ↓
+Finding Classification
+        ↓
+Code / Prompt Generation
+        ↓
+Human Validation
+        ↓
+Report Export
+```
+
+---
+
+## Data Handling
+
+By default, the starter app stores records in Streamlit session state.
+
+That means:
+
+- records persist only for the active session
+- exports should be downloaded before closing or refreshing the session
+- no database is configured by default
+- no external telemetry is implemented by default
+
+For production use, add explicit persistence through a controlled backend such as SQLite, PostgreSQL, Supabase, or another approved datastore.
+
+---
+
+## Exported Artifacts
+
+The current app can export:
+
+- workflow prompts as Markdown
+- findings as JSON
+- findings as CSV
+- findings as Markdown
+- evidence ledger as JSON
+- full report as Markdown
+
+All exported artifacts should be reviewed before use in client reports, internal tickets, detection engineering tasks, or remediation workflows.
+
+---
+
+## Testing & Quality Checks
+
+Suggested local checks:
+
+```bash
+python -m py_compile app.py
 streamlit run app.py
 ```
 
-## Safety posture
+Recommended future checks:
+
+```bash
+python -m pip install ruff pytest bandit pip-audit
+ruff check .
+bandit -r .
+pip-audit
+pytest
+```
+
+For now, the starter package is intentionally small, so the primary validation path is syntax checking plus manual UI testing.
+
+---
+
+## Development Roadmap
+
+### Phase 1
+
+- Scope-gated workflows
+- Findings management
+- Report export
+- Prompt surface
+- Evidence ledger
+
+### Phase 2
+
+- Agent coordination
+- Structured memory
+- Typed contracts
+- Multi-model routing
+
+### Phase 3
+
+- Drift-aware orchestration
+- Evidence graphs
+- Policy enforcement engine
+- Autonomous validation loops
+
+---
+
+## Contributing
+
+Contributions should prioritize:
+
+- clarity
+- safety
+- reproducibility
+- deterministic behavior
+- typed interfaces
+- operational traceability
+
+Before submitting:
+
+- run linting
+- validate workflows
+- document assumptions
+- avoid opaque automation behavior
+- confirm no unsafe workflow bypasses were introduced
+- keep generated content reviewable by humans
+
+---
+
+## License
+
+Licensed under the Apache 2.0 License.
+
+See the LICENSE file for details.
+
+---
+
+## Disclaimer
+
+This project is provided for authorized security research, defensive engineering, and educational purposes only.
+
+The maintainers assume no liability for misuse, unauthorized deployment, or operational damage caused by derivative implementations.
+
+Generated outputs may contain inaccuracies, insecure assumptions, or incomplete implementations.
+
+Human review is required before production or operational use.
+
+---
+
+## Acknowledgements
+
+Built with:
+
+- Streamlit
+- Hugging Face
+- Python Software Foundation
 
-The app intentionally avoids autonomous offensive execution. It is a scope-gated workflow, findings, evidence, and report workspace for authorized defensive/purple-team work.
+Inspired by structured operational engineering, purple-team methodology, and the stubborn belief that security tooling should behave like systems engineering rather than ritual magic.