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app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load model
+MODEL_ID = "Bernadetta14/qwen2.5-coder-0.5b-vulnerability-detector"
+
+print("Loading model...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float32,
+    device_map="cpu"
+)
+model.eval()
+print("Model loaded!")
+
+SYSTEM_PROMPT = "You are a code security expert. Analyze the given code for security vulnerabilities and provide a structured security report."
+
+def analyze_code(code, lang):
+    if not code.strip():
+        return "⚠️ Please enter some code to analyze."
+
+    prompt = f"""<|im_start|>system
+{SYSTEM_PROMPT}<|im_end|>
+<|im_start|>user
+Analyze this {lang} code for security vulnerabilities:
+
+```{lang}
+{code[:800]}
+```
+
+Provide a structured security report:<|im_end|>
+<|im_start|>assistant
+Vulnerability :"""
+
+    inputs = tokenizer(prompt, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=300,
+            do_sample=False,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    new_tokens = outputs[0][inputs["input_ids"].shape[1]:]
+    result = tokenizer.decode(new_tokens, skip_special_tokens=True)
+    return "Vulnerability :" + result
+
+# Contoh code vulnerable
+EXAMPLES = {
+    "Python - SQL Injection": """\
+def login(username, password):
+    query = "SELECT * FROM users WHERE username='" + username + "' AND password='" + password + "'"
+    result = db.execute(query)
+    return result
+""",
+    "JavaScript - XSS": """\
+function displayMessage(userInput) {
+    document.getElementById('output').innerHTML = userInput;
+}
+""",
+    "PHP - Command Injection": """\
+<?php
+$filename = $_GET['file'];
+$output = shell_exec('cat ' . $filename);
+echo $output;
+?>
+"""
+}
+
+with gr.Blocks(title="Code Vulnerability Detector", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🔐 Code Vulnerability Detector
+    
+    Deteksi celah keamanan dalam kode secara otomatis menggunakan AI.
+    Model akan menganalisis kode dan memberikan laporan terstruktur berisi
+    jenis vulnerability, tingkat bahaya, penjelasan, dan saran perbaikan.
+    
+    **Model:** Qwen2.5-Coder-0.5B (fine-tuned on CyberNative Security Dataset)
+    """)
+
+    with gr.Row():
+        with gr.Column():
+            lang_input = gr.Dropdown(
+                choices=["python", "javascript", "java", "c++", "c#", "php", "ruby", "swift", "go", "kotlin"],
+                value="python",
+                label="🌐 Programming Language"
+            )
+            code_input = gr.Code(
+                label="📝 Code to Analyze",
+                language="python",
+                lines=15,
+                value=EXAMPLES["Python - SQL Injection"]
+            )
+            with gr.Row():
+                example_btn = gr.Dropdown(
+                    choices=list(EXAMPLES.keys()),
+                    label="📚 Load Example",
+                    value="Python - SQL Injection"
+                )
+                analyze_btn = gr.Button("🔍 Analyze Code", variant="primary")
+
+        with gr.Column():
+            output = gr.Textbox(
+                label="📋 Security Report",
+                lines=15,
+                interactive=False
+            )
+
+    gr.Markdown("""
+    ---
+    ### 📊 Model Performance
+    | Metric | Value |
+    |--------|-------|
+    | Accuracy | 62% |
+    | Training Samples | 4,187 |
+    | Supported Languages | 11 |
+    | Model Size | 0.5B params |
+    
+    **Dataset:** CyberNative/Code_Vulnerability_Security_DPO  
+    **Method:** LoRA fine-tuning via MLX-LM (Apple Silicon)
+    """)
+
+    # Load example
+    def load_example(example_name):
+        return EXAMPLES.get(example_name, "")
+
+    example_btn.change(fn=load_example, inputs=example_btn, outputs=code_input)
+
+    # Update code language highlight when lang changes
+    lang_input.change(fn=lambda l: gr.Code(language=l if l in ["python", "javascript", "java"] else "python"), inputs=lang_input, outputs=code_input)
+
+    analyze_btn.click(
+        fn=analyze_code,
+        inputs=[code_input, lang_input],
+        outputs=output
+    )
+
+demo.launch()

requirements.txt

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+gradio>=4.0.0
+transformers>=4.37.0
+torch>=2.0.0
+accelerate>=0.27.0