convert to GGUF optimized CPU bug bounty agent

app.py

@@ -215,7 +215,162 @@ def chat(message, history):
 
 # =========================
 # UI
+# =================import gradio as gr
+import sqlite3
+from ctransformers import AutoModelForCausalLM
+
+# =========================
+# MEMORY SYSTEM (LIGHT)
+# =========================
+
+conn = sqlite3.connect("memory.db", check_same_thread=False)
+cursor = conn.cursor()
+
+cursor.execute("""
+CREATE TABLE IF NOT EXISTS memory (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    role TEXT,
+    content TEXT
+)
+""")
+conn.commit()
+
+
+def save(role, content):
+    cursor.execute(
+        "INSERT INTO memory (role, content) VALUES (?, ?)",
+        (role, content)
+    )
+    conn.commit()
+
+
+def load(limit=2):  # VERY IMPORTANT FOR RAM
+    cursor.execute("""
+        SELECT role, content FROM memory
+        ORDER BY id DESC LIMIT ?
+    """, (limit,))
+    rows = cursor.fetchall()
+    rows.reverse()
+    return rows
+
+
+# =========================
+# LOAD GGUF MODEL
+# =========================
+
+model = AutoModelForCausalLM.from_pretrained(
+    "TheBloke/Qwen2.5-Coder-1.5B-Instruct-GGUF",
+    model_file="q4_k_m.gguf",   # IMPORTANT: quantized file
+    model_type="qwen",
+    gpu_layers=0
+)
+
+# =========================
+# SYSTEM PROMPT
+# =========================
+
+SYSTEM_PROMPT = """
+You are Symbolic Alpha Bug Bounty AI Agent.
+
+Rules:
+- Be accurate and technical
+- Do not invent fake exploits or tools
+- Focus on cybersecurity education
+- Keep answers concise
+"""
+
+
+# =========================
+# PROMPT BUILDER
+# =========================
+
+def build_prompt(user_input):
+
+    prompt = SYSTEM_PROMPT + "\n\n"
+
+    memory = load()
+
+    for role, content in memory:
+        prompt += f"{role}: {content}\n"
+
+    prompt += f"user: {user_input}\nassistant:"
+
+    return prompt
+
+
+# =========================
+# SIMPLE SECURITY SCANNER TOOL
+# =========================
+
+def scanner(text):
+
+    findings = []
+    t = text.lower()
+
+    if "'" in t and "select" in t:
+        findings.append("SQL Injection pattern detected")
+
+    if "script" in t or "innerhtml" in t:
+        findings.append("XSS risk detected")
+
+    if "eval(" in t:
+        findings.append("RCE risk detected")
+
+    if not findings:
+        findings.append("No obvious issues found")
+
+    return findings
+
+
+# =========================
+# CHAT ENGINE
+# =========================
+
+def chat(message, history):
+
+    try:
+        save("user", message)
+
+        # TOOL MODE
+        if message.startswith("scan:"):
+            target = message.replace("scan:", "")
+            result = scanner(target)
+
+            output = "BUG BOUNTY SCAN RESULTS:\n"
+            for r in result:
+                output += "- " + r + "\n"
+
+            save("assistant", output)
+            return output
+
+        # NORMAL MODE
+        prompt = build_prompt(message)
+
+        response = model(
+            prompt,
+            max_new_tokens=80,   # VERY IMPORTANT FOR CPU
+            temperature=0.2,
+            stop=["user:"]
+        )
+
+        save("assistant", response)
+        return response
+
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
 # =========================
+# UI
+# =========================
+
+demo = gr.ChatInterface(
+    fn=chat,
+    title="Symbolic Alpha GGUF Bug Bounty Agent",
+    description="Lightweight CPU-optimized AI security assistant"
+)
+
+demo.launch()========
 
 demo = gr.ChatInterface(
     fn=chat,