Update app.py

app.py

@@ -1,64 +1,112 @@
+import os
 import gradio as gr
-from huggingface_hub import InferenceClient
+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
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# 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 respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+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())
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    # Use OpenAI or Hugging Face embeddings
+    if USE_OPENAI:
+        embeddings = OpenAIEmbeddings()
+    else:
+        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
 
-    messages.append({"role": "user", "content": message})
+    return Chroma.from_documents(all_docs, embeddings)
 
-    response = ""
+# Load Vector Database
+vector_db = load_data()
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+# 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})
 
-        response += token
-        yield response
+# 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;}
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+
+# 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>
 """
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
 
+# 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
+)
 
-if __name__ == "__main__":
-    demo.launch()
+# Embed 3D Avatar into the interface
+iface.launch(share=True, custom_js=three_js_html)