import os
import gradio as gr
from langchain.chains import RetrievalQA
from langchain.vectorstores import Chroma
from langchain.llms import OpenAI, HuggingFaceHub
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceEmbeddings
from langchain.document_loaders import PyPDFLoader
import time

# Define paths for cybersecurity documents (Add your PDFs here)
PDF_FILES = ["NIST_CSWP_04162018.pdf", "ISOIEC 27001_2ef522.pdf", "MITRE ATLAS Overview Combined_v1.pdf", "ISO-IEC-27005-2022.pdf"]

# Choose LLM Model (Switch between OpenAI and Hugging Face)
USE_OPENAI = False  # Change to True if you prefer OpenAI API

def load_data():
    """Loads multiple PDFs and stores embeddings in ChromaDB"""
    all_docs = []
    for pdf in PDF_FILES:
        if os.path.exists(pdf):
            loader = PyPDFLoader(pdf)
            all_docs.extend(loader.load())

    # Use OpenAI or Hugging Face embeddings
    if USE_OPENAI:
        embeddings = OpenAIEmbeddings()
    else:
        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")

    return Chroma.from_documents(all_docs, embeddings)

# Load Vector Database
vector_db = load_data()

# Select LLM model (Online: OpenAI | Offline: Hugging Face)
if USE_OPENAI:
    llm = OpenAI()
else:
    llm = HuggingFaceHub(repo_id="google/flan-t5-large", model_kwargs={"temperature": 0.5, "max_length": 512})

# Create Retrieval QA chain
qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=vector_db.as_retriever())

# Function to simulate futuristic typing effect
def chatbot_response(question):
    """Handles chatbot queries with a typing effect"""
    response = qa_chain.run(question)
    displayed_response = ""
    for char in response:
        displayed_response += char
        time.sleep(0.02)  # Simulate typing delay
        yield displayed_response

# Custom futuristic CSS style
custom_css = """
body {background-color: #0f172a; color: #0ff; font-family: 'Orbitron', sans-serif;}
#chatbot-container {border: 2px solid #00ffff; background: rgba(0, 0, 0, 0.8); padding: 20px; border-radius: 15px;}
.gradio-container {background: linear-gradient(to bottom, #020c1b, #001f3f);}
textarea {background: #011627; color: #0ff; font-size: 18px;}
button {background: #0088ff; color: white; font-size: 20px; border-radius: 5px; border: none; padding: 10px;}
button:hover {background: #00ffff; color: #000;}
"""

# 3D Avatar using Three.js
three_js_html = """
<div id="avatar-container">
  <script src="https://cdnjs.cloudflare.com/ajax/libs/three.js/r128/three.min.js"></script>
  <script>
    function create3DAvatar() {
        var scene = new THREE.Scene();
        var camera = new THREE.PerspectiveCamera(75, 1, 0.1, 1000);
        var renderer = new THREE.WebGLRenderer({ alpha: true });
        renderer.setSize(300, 300);
        document.getElementById('avatar-container').appendChild(renderer.domElement);

        var geometry = new THREE.SphereGeometry(1, 32, 32);
        var material = new THREE.MeshStandardMaterial({ color: 0x00ffff, wireframe: true });
        var avatar = new THREE.Mesh(geometry, material);
        scene.add(avatar);

        var light = new THREE.PointLight(0x00ffff, 1, 100);
        light.position.set(2, 2, 5);
        scene.add(light);

        camera.position.z = 3;

        function animate() {
            requestAnimationFrame(animate);
            avatar.rotation.y += 0.01;
            renderer.render(scene, camera);
        }
        animate();
    }
    window.onload = create3DAvatar;
  </script>
</div>
"""

# Create Gradio Interface with Custom Styling and 3D Avatar
iface = gr.Interface(
    fn=chatbot_response,
    inputs="text",
    outputs="text",
    title="🤖 Cybernetic AI: Your Cybersecurity Assistant",
    description="Ask me about NIST, ISO/IEC 27001, MITRE ATT&CK, and ISO/IEC 27005. Now with a 3D Avatar!",
    theme="default",
    css=custom_css,
    live=True,  # Enables real-time updates for typing effect
)

# Embed 3D Avatar into the interface
iface.launch(share=True, custom_js=three_js_html)