Upload folder using huggingface_hub

.gitignore

@@ -0,0 +1,31 @@
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+.env
+.venv
+venv/
+ENV/
+.DS_Store
+.idea/
+.vscode/
+*.swp
+*.swo
+*~
+flagged/

README.md

@@ -1,12 +1,115 @@
 ---
 title: Model Playground
-emoji: 🐢
-colorFrom: indigo
-colorTo: pink
+emoji: 🎮
+colorFrom: red
+colorTo: purple
 sdk: gradio
-sdk_version: 6.5.1
+sdk_version: 4.44.0
 app_file: app.py
 pinned: false
+license: apache-2.0
+tags:
+  - cybersecurity
+  - ISO27001
+  - GDPR
+  - RGPD
+  - model-comparison
+  - fine-tuning
+  - qwen
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Model Playground
+
+Interactive playground for experimenting with 3 fine-tuned cybersecurity AI models.
+
+## Features
+
+### Single-Turn Q&A Mode
+- Select from 3 specialized models (ISO27001, RGPD, CyberSec-3B)
+- Editable system prompt with model-specific defaults
+- Adjustable generation parameters:
+  - **Temperature** (0-2, step 0.1): Controls randomness
+  - **Top-p** (0-1, step 0.05): Nucleus sampling threshold
+  - **Top-k** (0-100, step 5): Top-k sampling (0 = disabled)
+  - **Max tokens** (128-2048, step 128): Maximum response length
+  - **Repetition penalty** (1.0-2.0, step 0.1): Reduces repetition
+- Real-time generation metrics:
+  - Token count
+  - Generation time (seconds)
+  - Tokens per second
+- Export prompt + response as JSON
+
+### Side-by-Side Comparison Mode
+- Compare responses from the same model with 2 different parameter configurations
+- Separate metrics for each configuration
+- Perfect for A/B testing prompt engineering strategies
+
+### Dark Theme
+- Optimized dark theme for comfortable extended use
+- Clean, minimal interface focused on content
+
+## Models
+
+| Model | Specialty | Base Model | Size |
+|-------|-----------|------------|------|
+| **ISO27001-Expert-1.5B** | ISO/IEC 27001 ISMS | Qwen 2.5 1.5B Instruct | 1.5B |
+| **RGPD-Expert-1.5B** | GDPR/RGPD compliance | Qwen 2.5 1.5B Instruct | 1.5B |
+| **CyberSec-Assistant-3B** | General cybersecurity | Qwen 2.5 3B Instruct | 3B |
+
+All models are fine-tuned using QLoRA on domain-specific cybersecurity datasets.
+
+## Usage Tips
+
+### Temperature Settings
+- **0.0-0.3**: Highly deterministic, factual responses (best for compliance questions)
+- **0.4-0.7**: Balanced creativity and accuracy (recommended default)
+- **0.8-1.5**: More creative and diverse responses
+- **1.6-2.0**: Very creative, potentially less coherent
+
+### Top-p and Top-k
+- Use **Top-p** for dynamic vocabulary filtering (recommended: 0.9-0.95)
+- Use **Top-k** for fixed vocabulary filtering (recommended: 40-50, or 0 to disable)
+- Generally, use one or the other, not both aggressively
+
+### Repetition Penalty
+- **1.0**: No penalty
+- **1.1-1.3**: Gentle reduction of repetition (recommended)
+- **1.4+**: Strong penalty, may affect response quality
+
+## Example Prompts
+
+**ISO27001 Expert:**
+- "What are the mandatory clauses of ISO 27001:2022?"
+- "How do I conduct a risk assessment according to ISO 27001?"
+- "What changed between ISO 27001:2013 and ISO 27001:2022?"
+
+**RGPD Expert:**
+- "What are the 6 lawful bases for processing under GDPR?"
+- "When is a Data Protection Impact Assessment (DPIA) required?"
+- "What are the penalties for GDPR non-compliance?"
+
+**CyberSec Assistant:**
+- "Explain the MITRE ATT&CK framework"
+- "What are the main requirements of the NIS2 directive?"
+- "How do I set up a SOC from scratch?"
+
+## Technical Details
+
+- **Framework**: Gradio 4.44.0
+- **Model Loading**: Dynamic loading with thread-safe caching
+- **Inference**: CPU-based (float32) for broader compatibility
+- **LoRA**: PEFT adapters loaded on top of base Qwen 2.5 models
+- **Generation**: Greedy or sampling based on temperature setting
+
+## Links
+
+- **Creator**: [Ayi NEDJIMI](https://huggingface.co/AYI-NEDJIMI)
+- **Models**:
+  - [ISO27001-Expert-1.5B](https://huggingface.co/AYI-NEDJIMI/ISO27001-Expert-1.5B)
+  - [RGPD-Expert-1.5B](https://huggingface.co/AYI-NEDJIMI/RGPD-Expert-1.5B)
+  - [CyberSec-Assistant-3B](https://huggingface.co/AYI-NEDJIMI/CyberSec-Assistant-3B)
+- **Full Portfolio**: [CyberSec AI Collection](https://huggingface.co/collections/AYI-NEDJIMI/cybersec-ai-portfolio-datasets-models-and-spaces-699224074a478ec0feeac493)
+
+## License
+
+Apache 2.0

app.py

@@ -0,0 +1,504 @@
+import gc
+import json
+import os
+import threading
+import time
+from typing import Optional
+
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+# ---------------------------------------------------------------------------
+# Model registry
+# ---------------------------------------------------------------------------
+MODELS = {
+    "ISO27001-Expert-1.5B": {
+        "base": "Qwen/Qwen2.5-1.5B-Instruct",
+        "adapter": "AYI-NEDJIMI/ISO27001-Expert-1.5B",
+        "default_prompt": (
+            "You are ISO 27001 Expert, a specialized AI assistant for "
+            "ISO/IEC 27001 information security management systems. "
+            "You help organizations understand, implement, and maintain "
+            "ISO 27001 certification, including risk assessment, controls "
+            "from Annex A, Statement of Applicability, and audit preparation."
+        ),
+    },
+    "RGPD-Expert-1.5B": {
+        "base": "Qwen/Qwen2.5-1.5B-Instruct",
+        "adapter": "AYI-NEDJIMI/RGPD-Expert-1.5B",
+        "default_prompt": (
+            "You are RGPD Expert, a specialized AI assistant for GDPR/RGPD "
+            "data protection regulations. You help organizations understand "
+            "their obligations under the General Data Protection Regulation, "
+            "including data subject rights, Data Protection Impact Assessments, "
+            "lawful bases for processing, and breach notification procedures."
+        ),
+    },
+    "CyberSec-Assistant-3B": {
+        "base": "Qwen/Qwen2.5-3B-Instruct",
+        "adapter": "AYI-NEDJIMI/CyberSec-Assistant-3B",
+        "default_prompt": (
+            "You are CyberSec Assistant, an expert AI specialized in "
+            "cybersecurity, compliance (GDPR, NIS2, DORA, AI Act, ISO 27001), "
+            "penetration testing, SOC operations, and AI security."
+        ),
+    },
+}
+
+# ---------------------------------------------------------------------------
+# Global model state
+# ---------------------------------------------------------------------------
+_lock = threading.Lock()
+_loaded_model_name = None
+_tokenizer = None
+_model = None
+
+
+def load_model(model_name: str):
+    """Load or switch to a different model."""
+    global _loaded_model_name, _tokenizer, _model
+
+    with _lock:
+        if _loaded_model_name == model_name and _model is not None:
+            return  # Already loaded
+
+        # Unload previous model
+        if _model is not None:
+            del _model
+            del _tokenizer
+            gc.collect()
+            torch.cuda.empty_cache()
+
+        # Load new model
+        cfg = MODELS[model_name]
+        hf_token = os.getenv("HF_TOKEN")
+
+        _tokenizer = AutoTokenizer.from_pretrained(
+            cfg["base"],
+            trust_remote_code=True,
+            token=hf_token,
+        )
+
+        base = AutoModelForCausalLM.from_pretrained(
+            cfg["base"],
+            torch_dtype=torch.float32,
+            device_map="cpu",
+            trust_remote_code=True,
+            token=hf_token,
+        )
+
+        _model = PeftModel.from_pretrained(
+            base,
+            cfg["adapter"],
+            torch_dtype=torch.float32,
+            token=hf_token,
+        )
+        _model.eval()
+
+        _loaded_model_name = model_name
+
+
+def generate_single(
+    model_name: str,
+    system_prompt: str,
+    user_prompt: str,
+    temperature: float,
+    top_p: float,
+    top_k: int,
+    max_tokens: int,
+    repetition_penalty: float,
+) -> tuple[str, dict]:
+    """
+    Generate a single response with metrics.
+    Returns: (response_text, metrics_dict)
+    """
+    if not user_prompt.strip():
+        return "", {}
+
+    # Load model
+    try:
+        load_model(model_name)
+    except Exception as e:
+        return f"Error loading model: {e}", {}
+
+    # Build messages
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_prompt},
+    ]
+
+    input_text = _tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    inputs = _tokenizer(input_text, return_tensors="pt").to("cpu")
+    input_length = inputs.input_ids.shape[1]
+
+    # Generation
+    start_time = time.time()
+
+    with torch.no_grad():
+        outputs = _model.generate(
+            **inputs,
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k if top_k > 0 else None,
+            do_sample=temperature > 0,
+            repetition_penalty=repetition_penalty,
+            pad_token_id=_tokenizer.eos_token_id,
+        )
+
+    end_time = time.time()
+    elapsed = end_time - start_time
+
+    # Decode
+    generated_ids = outputs[0][input_length:]
+    response = _tokenizer.decode(generated_ids, skip_special_tokens=True)
+
+    # Metrics
+    num_tokens = len(generated_ids)
+    tokens_per_sec = num_tokens / elapsed if elapsed > 0 else 0
+
+    metrics = {
+        "tokens": num_tokens,
+        "time_sec": round(elapsed, 2),
+        "tokens_per_sec": round(tokens_per_sec, 2),
+    }
+
+    return response, metrics
+
+
+# ---------------------------------------------------------------------------
+# UI handlers
+# ---------------------------------------------------------------------------
+def generate_response(
+    model_name: str,
+    system_prompt: str,
+    user_prompt: str,
+    temperature: float,
+    top_p: float,
+    top_k: int,
+    max_tokens: int,
+    repetition_penalty: float,
+):
+    """Handler for single-turn Q&A."""
+    response, metrics = generate_single(
+        model_name, system_prompt, user_prompt,
+        temperature, top_p, top_k, max_tokens, repetition_penalty
+    )
+
+    metrics_text = ""
+    if metrics:
+        metrics_text = (
+            f"**Generation Metrics:**\n"
+            f"- Tokens: {metrics['tokens']}\n"
+            f"- Time: {metrics['time_sec']}s\n"
+            f"- Speed: {metrics['tokens_per_sec']} tokens/sec"
+        )
+
+    return response, metrics_text
+
+
+def export_json(model_name: str, system_prompt: str, user_prompt: str, response: str, metrics_text: str):
+    """Export conversation as JSON."""
+    data = {
+        "model": model_name,
+        "system_prompt": system_prompt,
+        "user_prompt": user_prompt,
+        "response": response,
+        "metrics": metrics_text,
+    }
+    return json.dumps(data, indent=2, ensure_ascii=False)
+
+
+def generate_comparison(
+    model_name: str,
+    system_prompt: str,
+    user_prompt: str,
+    # Config A
+    temp_a: float, top_p_a: float, top_k_a: int, max_tok_a: int, rep_pen_a: float,
+    # Config B
+    temp_b: float, top_p_b: float, top_k_b: int, max_tok_b: int, rep_pen_b: float,
+):
+    """Generate side-by-side comparison with different parameter sets."""
+
+    response_a, metrics_a = generate_single(
+        model_name, system_prompt, user_prompt,
+        temp_a, top_p_a, top_k_a, max_tok_a, rep_pen_a
+    )
+
+    response_b, metrics_b = generate_single(
+        model_name, system_prompt, user_prompt,
+        temp_b, top_p_b, top_k_b, max_tok_b, rep_pen_b
+    )
+
+    metrics_text_a = ""
+    if metrics_a:
+        metrics_text_a = (
+            f"**Config A Metrics:**\n"
+            f"- Tokens: {metrics_a['tokens']}\n"
+            f"- Time: {metrics_a['time_sec']}s\n"
+            f"- Speed: {metrics_a['tokens_per_sec']} tok/s"
+        )
+
+    metrics_text_b = ""
+    if metrics_b:
+        metrics_text_b = (
+            f"**Config B Metrics:**\n"
+            f"- Tokens: {metrics_b['tokens']}\n"
+            f"- Time: {metrics_b['time_sec']}s\n"
+            f"- Speed: {metrics_b['tokens_per_sec']} tok/s"
+        )
+
+    return response_a, metrics_text_a, response_b, metrics_text_b
+
+
+def update_system_prompt(model_name: str):
+    """Update system prompt textbox when model changes."""
+    return MODELS[model_name]["default_prompt"]
+
+
+# ---------------------------------------------------------------------------
+# Gradio UI
+# ---------------------------------------------------------------------------
+DESCRIPTION = """\
+## Model Playground
+
+Experiment with **3 fine-tuned cybersecurity models** using customizable parameters.
+
+**Features:**
+- Single-turn Q&A (no chat history)
+- Adjustable generation parameters (temperature, top-p, top-k, max tokens, repetition penalty)
+- Real-time generation metrics (tokens/sec, total time, token count)
+- Export conversations as JSON
+- Side-by-side comparison mode with 2 different parameter configurations
+- Dark theme optimized for readability
+
+**Models:**
+- **ISO27001-Expert-1.5B**: ISO/IEC 27001 ISMS specialist
+- **RGPD-Expert-1.5B**: GDPR/RGPD compliance expert
+- **CyberSec-Assistant-3B**: General cybersecurity assistant
+"""
+
+theme = gr.themes.Monochrome(
+    primary_hue="red",
+    secondary_hue="purple",
+    neutral_hue="slate",
+    font=gr.themes.GoogleFont("Inter"),
+).set(
+    body_background_fill="#0a0a0a",
+    body_background_fill_dark="#0a0a0a",
+    block_background_fill="#1a1a1a",
+    block_background_fill_dark="#1a1a1a",
+    input_background_fill="#262626",
+    input_background_fill_dark="#262626",
+    button_primary_background_fill="#dc2626",
+    button_primary_background_fill_dark="#dc2626",
+)
+
+with gr.Blocks(theme=theme, title="Model Playground") as demo:
+
+    gr.Markdown("# Model Playground")
+    gr.Markdown(DESCRIPTION)
+
+    with gr.Tabs():
+
+        # ===================================================================
+        # Tab 1: Single-Turn Q&A
+        # ===================================================================
+        with gr.Tab("Single-Turn Q&A"):
+            with gr.Row():
+                with gr.Column(scale=2):
+                    model_select = gr.Dropdown(
+                        choices=list(MODELS.keys()),
+                        value="ISO27001-Expert-1.5B",
+                        label="Select Model",
+                    )
+                with gr.Column(scale=3):
+                    system_prompt_box = gr.Textbox(
+                        value=MODELS["ISO27001-Expert-1.5B"]["default_prompt"],
+                        label="System Prompt (Editable)",
+                        lines=4,
+                    )
+
+            user_prompt_box = gr.Textbox(
+                label="Your Question",
+                placeholder="Enter your question here...",
+                lines=3,
+            )
+
+            with gr.Accordion("Generation Parameters", open=True):
+                with gr.Row():
+                    temperature_slider = gr.Slider(
+                        minimum=0, maximum=2, value=0.7, step=0.1,
+                        label="Temperature",
+                        info="Higher = more creative, lower = more deterministic"
+                    )
+                    top_p_slider = gr.Slider(
+                        minimum=0, maximum=1, value=0.9, step=0.05,
+                        label="Top-p (nucleus sampling)",
+                    )
+                    top_k_slider = gr.Slider(
+                        minimum=0, maximum=100, value=50, step=5,
+                        label="Top-k (0 = disabled)",
+                    )
+
+                with gr.Row():
+                    max_tokens_slider = gr.Slider(
+                        minimum=128, maximum=2048, value=512, step=128,
+                        label="Max Tokens",
+                    )
+                    repetition_penalty_slider = gr.Slider(
+                        minimum=1.0, maximum=2.0, value=1.1, step=0.1,
+                        label="Repetition Penalty",
+                    )
+
+            generate_btn = gr.Button("Generate Response", variant="primary", size="lg")
+
+            with gr.Row():
+                with gr.Column(scale=3):
+                    response_box = gr.Textbox(
+                        label="Response",
+                        lines=15,
+                        interactive=False,
+                    )
+                with gr.Column(scale=1):
+                    metrics_box = gr.Markdown(label="Metrics")
+
+            with gr.Row():
+                export_btn = gr.Button("Export as JSON")
+                json_output = gr.Textbox(label="JSON Export", lines=10, visible=False)
+
+            # Wire up events
+            model_select.change(
+                fn=update_system_prompt,
+                inputs=[model_select],
+                outputs=[system_prompt_box],
+            )
+
+            generate_btn.click(
+                fn=generate_response,
+                inputs=[
+                    model_select, system_prompt_box, user_prompt_box,
+                    temperature_slider, top_p_slider, top_k_slider,
+                    max_tokens_slider, repetition_penalty_slider,
+                ],
+                outputs=[response_box, metrics_box],
+            )
+
+            export_btn.click(
+                fn=export_json,
+                inputs=[model_select, system_prompt_box, user_prompt_box, response_box, metrics_box],
+                outputs=[json_output],
+            ).then(
+                fn=lambda: gr.update(visible=True),
+                outputs=[json_output],
+            )
+
+            gr.Examples(
+                examples=[
+                    ["What are the mandatory clauses of ISO 27001:2022?"],
+                    ["What are the 6 lawful bases for processing under GDPR?"],
+                    ["Explain the MITRE ATT&CK framework."],
+                    ["What are the main requirements of the NIS2 directive?"],
+                ],
+                inputs=user_prompt_box,
+            )
+
+        # ===================================================================
+        # Tab 2: Side-by-Side Comparison
+        # ===================================================================
+        with gr.Tab("Side-by-Side Comparison"):
+            gr.Markdown("### Compare responses from the same model with 2 different parameter configurations")
+
+            with gr.Row():
+                with gr.Column(scale=2):
+                    model_select_comp = gr.Dropdown(
+                        choices=list(MODELS.keys()),
+                        value="ISO27001-Expert-1.5B",
+                        label="Select Model",
+                    )
+                with gr.Column(scale=3):
+                    system_prompt_comp = gr.Textbox(
+                        value=MODELS["ISO27001-Expert-1.5B"]["default_prompt"],
+                        label="System Prompt (Editable)",
+                        lines=4,
+                    )
+
+            user_prompt_comp = gr.Textbox(
+                label="Your Question",
+                placeholder="Enter your question here...",
+                lines=3,
+            )
+
+            with gr.Row():
+                # Config A
+                with gr.Column():
+                    gr.Markdown("#### Configuration A")
+                    temp_a = gr.Slider(0, 2, value=0.3, step=0.1, label="Temperature")
+                    top_p_a = gr.Slider(0, 1, value=0.9, step=0.05, label="Top-p")
+                    top_k_a = gr.Slider(0, 100, value=50, step=5, label="Top-k")
+                    max_tok_a = gr.Slider(128, 2048, value=512, step=128, label="Max Tokens")
+                    rep_pen_a = gr.Slider(1.0, 2.0, value=1.1, step=0.1, label="Rep. Penalty")
+
+                # Config B
+                with gr.Column():
+                    gr.Markdown("#### Configuration B")
+                    temp_b = gr.Slider(0, 2, value=1.2, step=0.1, label="Temperature")
+                    top_p_b = gr.Slider(0, 1, value=0.95, step=0.05, label="Top-p")
+                    top_k_b = gr.Slider(0, 100, value=40, step=5, label="Top-k")
+                    max_tok_b = gr.Slider(128, 2048, value=512, step=128, label="Max Tokens")
+                    rep_pen_b = gr.Slider(1.0, 2.0, value=1.2, step=0.1, label="Rep. Penalty")
+
+            compare_btn = gr.Button("Generate Comparison", variant="primary", size="lg")
+
+            with gr.Row():
+                with gr.Column():
+                    response_a = gr.Textbox(label="Response A", lines=12, interactive=False)
+                    metrics_a = gr.Markdown()
+                with gr.Column():
+                    response_b = gr.Textbox(label="Response B", lines=12, interactive=False)
+                    metrics_b = gr.Markdown()
+
+            # Wire up events
+            model_select_comp.change(
+                fn=update_system_prompt,
+                inputs=[model_select_comp],
+                outputs=[system_prompt_comp],
+            )
+
+            compare_btn.click(
+                fn=generate_comparison,
+                inputs=[
+                    model_select_comp, system_prompt_comp, user_prompt_comp,
+                    temp_a, top_p_a, top_k_a, max_tok_a, rep_pen_a,
+                    temp_b, top_p_b, top_k_b, max_tok_b, rep_pen_b,
+                ],
+                outputs=[response_a, metrics_a, response_b, metrics_b],
+            )
+
+            gr.Examples(
+                examples=[
+                    ["What is a Data Protection Impact Assessment?"],
+                    ["Explain the concept of Zero Trust security."],
+                    ["What are the penalties for GDPR non-compliance?"],
+                ],
+                inputs=user_prompt_comp,
+            )
+
+    # Footer
+    gr.HTML("""
+    <div style="text-align:center; margin-top:2rem; padding-top:1rem; border-top:1px solid #333; color:#888; font-size:0.85rem;">
+      <p>Built by <a href="https://huggingface.co/AYI-NEDJIMI" style="color:#dc2626;">Ayi NEDJIMI</a>
+      | Models: <a href="https://huggingface.co/AYI-NEDJIMI/ISO27001-Expert-1.5B" style="color:#dc2626;">ISO27001</a>,
+      <a href="https://huggingface.co/AYI-NEDJIMI/RGPD-Expert-1.5B" style="color:#dc2626;">RGPD</a>,
+      <a href="https://huggingface.co/AYI-NEDJIMI/CyberSec-Assistant-3B" style="color:#dc2626;">CyberSec-3B</a>
+      | <a href="https://huggingface.co/collections/AYI-NEDJIMI/cybersec-ai-portfolio-datasets-models-and-spaces-699224074a478ec0feeac493" style="color:#dc2626;">Portfolio</a></p>
+      <p style="font-size:0.75rem; color:#666;">Fine-tuned with QLoRA on Qwen 2.5 | Model Playground</p>
+    </div>
+    """)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+gradio==4.44.0
+transformers==4.46.2
+torch==2.5.1
+peft==0.13.2
+accelerate==1.1.1