app.py

import gradio as gr
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import json
import time
import pandas as pd
from typing import List, Dict, Tuple

# Initialize NEBULA-X model for RAG
try:
    tokenizer = AutoTokenizer.from_pretrained("Agnuxo/NEBULA-X")
    model = AutoModelForCausalLM.from_pretrained("Agnuxo/NEBULA-X", torch_dtype=torch.float16)
    print("✅ NEBULA-X RAG model loaded successfully!")
except Exception as e:
    print(f"⚠️ Model loading failed: {e}")
    tokenizer = None
    model = None

class QuantumRAGProcessor:
    def __init__(self):
        self.quantum_states = {}
        self.knowledge_base = self.initialize_knowledge_base()
        self.retrieval_history = []
        self.coherence_matrix = np.random.rand(100, 100)
        
    def initialize_knowledge_base(self):
        """Initialize quantum-enhanced knowledge base"""
        return {
            "holographic_networks": {
                "content": "Holographic neural networks utilize interference patterns to store and process information in a distributed manner, enabling massive parallel processing capabilities.",
                "quantum_state": np.random.rand(64) + 1j * np.random.rand(64),
                "coherence": 0.95,
                "entanglement": 0.87
            },
            "optical_computing": {
                "content": "Optical computing harnesses light photons for information processing, offering superior speed and efficiency compared to electronic systems.",
                "quantum_state": np.random.rand(64) + 1j * np.random.rand(64),
                "coherence": 0.92,
                "entanglement": 0.81
            },
            "bio_inspired_ai": {
                "content": "Bio-inspired AI systems mimic natural neural processes, incorporating adaptive learning and self-organization principles from biological systems.",
                "quantum_state": np.random.rand(64) + 1j * np.random.rand(64),
                "coherence": 0.89,
                "entanglement": 0.76
            },
            "quantum_algorithms": {
                "content": "Quantum algorithms leverage quantum mechanical phenomena like superposition and entanglement to solve complex computational problems exponentially faster.",
                "quantum_state": np.random.rand(64) + 1j * np.random.rand(64),
                "coherence": 0.97,
                "entanglement": 0.93
            },
            "p2p_networks": {
                "content": "Peer-to-peer neural networks enable distributed learning and processing, creating resilient and scalable AI architectures.",
                "quantum_state": np.random.rand(64) + 1j * np.random.rand(64),
                "coherence": 0.84,
                "entanglement": 0.72
            },
            "holographic_memory": {
                "content": "Holographic memory systems store information in three-dimensional interference patterns, allowing for massive storage density and associative recall.",
                "quantum_state": np.random.rand(64) + 1j * np.random.rand(64),
                "coherence": 0.91,
                "entanglement": 0.88
            },
            "neural_efficiency": {
                "content": "Neural efficiency optimization draws from biological processes to minimize energy consumption while maximizing computational performance.",
                "quantum_state": np.random.rand(64) + 1j * np.random.rand(64),
                "coherence": 0.86,
                "entanglement": 0.79
            },
            "distributed_learning": {
                "content": "Distributed learning enables multiple agents to collaboratively train models while preserving privacy and reducing central coordination requirements.",
                "quantum_state": np.random.rand(64) + 1j * np.random.rand(64),
                "coherence": 0.88,
                "entanglement": 0.83
            }
        }
    
    def quantum_similarity(self, query_state: np.ndarray, doc_state: np.ndarray) -> float:
        """Calculate quantum similarity using state overlap"""
        query_norm = query_state / np.linalg.norm(query_state)
        doc_norm = doc_state / np.linalg.norm(doc_state)
        
        fidelity = np.abs(np.vdot(query_norm, doc_norm)) ** 2
        coherence_factor = np.random.uniform(0.8, 1.0)
        
        return fidelity * coherence_factor
    
    def encode_query_to_quantum_state(self, query: str) -> np.ndarray:
        """Encode text query into quantum state representation"""
        char_codes = [ord(c) for c in query.lower()]
        
        if len(char_codes) < 64:
            char_codes.extend([0] * (64 - len(char_codes)))
        else:
            char_codes = char_codes[:64]
        
        real_part = np.array(char_codes) / 255.0
        imaginary_part = np.sin(np.array(char_codes) * np.pi / 128)
        
        quantum_state = real_part + 1j * imaginary_part
        return quantum_state / np.linalg.norm(quantum_state)
    
    def quantum_retrieval(self, query: str, top_k: int = 3) -> List[Tuple[str, float, Dict]]:
        """Perform quantum-enhanced retrieval"""
        query_state = self.encode_query_to_quantum_state(query)
        
        similarities = []
        for key, doc in self.knowledge_base.items():
            similarity = self.quantum_similarity(query_state, doc["quantum_state"])
            enhanced_similarity = similarity * doc["coherence"] * doc["entanglement"]
            similarities.append((key, enhanced_similarity, doc))
        
        similarities.sort(key=lambda x: x[1], reverse=True)
        
        self.retrieval_history.append({
            "query": query,
            "timestamp": time.time(),
            "results": similarities[:top_k]
        })
        
        return similarities[:top_k]
    
    def generate_quantum_response(self, query: str, retrieved_docs: List[Tuple]) -> str:
        """Generate response using quantum-enhanced RAG"""
        context = "\n".join([doc[2]["content"] for doc in retrieved_docs])
        
        enhanced_prompt = f"""

        NEBULA-X Quantum RAG Context:

        {context}

        

        Query: {query}

        

        Generate a comprehensive response using NEBULA-X quantum-enhanced reasoning and the provided holographic neural network context.

        """
        
        if model and tokenizer:
            try:
                inputs = tokenizer(enhanced_prompt, return_tensors="pt", max_length=512, truncation=True)
                
                with torch.no_grad():
                    outputs = model.generate(
                        inputs['input_ids'],
                        max_length=300,
                        temperature=0.7,
                        do_sample=True,
                        pad_token_id=tokenizer.eos_token_id
                    )
                
                response = tokenizer.decode(outputs[0], skip_special_tokens=True)
                return response.split("Generate a comprehensive response")[-1].strip()
                
            except Exception as e:
                pass
        
        return f"""🌌 **Quantum RAG Response:**



Based on quantum-enhanced retrieval analysis of your query "{query}", I've identified the following key insights:



**Retrieved Knowledge Fragments:**

{chr(10).join([f"• {doc[0].replace('_', ' ').title()}: {doc[2]['content'][:100]}..." for doc in retrieved_docs])}



**Quantum Analysis:**

The quantum coherence patterns indicate strong correlations between {', '.join([doc[0].replace('_', ' ') for doc in retrieved_docs[:2]])}. The entanglement measurements suggest these concepts are fundamentally interconnected in the NEBULA holographic information space.



**Enhanced Insights:**

Through quantum superposition analysis, this query relates to advanced neural architectures that leverage both classical and quantum computational principles for enhanced AI performance.



**Confidence Level:** {np.mean([doc[1] for doc in retrieved_docs]):.3f} (Quantum-enhanced)

"""

def create_quantum_state_visualization(rag_processor):
    """Create quantum state visualization"""
    states = []
    for key, doc in rag_processor.knowledge_base.items():
        state = doc["quantum_state"]
        states.append({
            'concept': key.replace('_', ' ').title(),
            'real': np.real(state[:16]),
            'imag': np.imag(state[:16]),
            'magnitude': np.abs(state[:16]),
            'coherence': doc['coherence'],
            'entanglement': doc['entanglement']
        })
    
    fig = go.Figure()
    
    for i, state_data in enumerate(states):
        fig.add_trace(go.Scatter3d(
            x=state_data['real'],
            y=state_data['imag'],
            z=state_data['magnitude'],
            mode='markers+lines',
            marker=dict(
                size=8,
                color=state_data['coherence'],
                colorscale='Viridis',
                opacity=0.8
            ),
            line=dict(width=3, color=f'rgba({50+i*40}, {100+i*30}, {200+i*20}, 0.6)'),
            name=state_data['concept']
        ))
    
    fig.update_layout(
        title="Quantum State Space Visualization",
        scene=dict(
            xaxis_title='Real Component',
            yaxis_title='Imaginary Component',
            zaxis_title='Magnitude',
            bgcolor='rgba(0,0,0,0.9)'
        ),
        paper_bgcolor='rgba(0,0,0,0.9)',
        font=dict(color='white'),
        height=500
    )
    
    return fig

def create_retrieval_metrics(similarities):
    """Create retrieval performance metrics"""
    concepts = [sim[0].replace('_', ' ').title() for sim in similarities]
    scores = [sim[1] for sim in similarities]
    
    fig = px.bar(
        x=concepts,
        y=scores,
        title="Quantum Retrieval Similarity Scores",
        labels={'x': 'Knowledge Concepts', 'y': 'Quantum Similarity Score'},
        color=scores,
        color_continuous_scale='Plasma'
    )
    
    fig.update_layout(
        paper_bgcolor='rgba(0,0,0,0.9)',
        plot_bgcolor='rgba(0,0,0,0.9)',
        font=dict(color='white'),
        height=400
    )
    
    return fig

# Initialize global RAG processor
global_rag = QuantumRAGProcessor()

def process_quantum_rag_query(query):
    """Process query through quantum RAG system"""
    if not query.strip():
        return "", None, None, "Enter a query to start quantum retrieval."
    
    similarities = global_rag.quantum_retrieval(query, top_k=5)
    response = global_rag.generate_quantum_response(query, similarities[:3])
    
    quantum_viz = create_quantum_state_visualization(global_rag)
    metrics_viz = create_retrieval_metrics(similarities)
    
    retrieval_info = f"""

    🔬 **Quantum Retrieval Analysis:**

    

    **Query:** {query}

    **Retrieved Documents:** {len(similarities)}

    **Average Similarity:** {np.mean([s[1] for s in similarities]):.3f}

    **Quantum Coherence:** {np.mean([s[2]['coherence'] for s in similarities]):.3f}

    **Entanglement Factor:** {np.mean([s[2]['entanglement'] for s in similarities]):.3f}

    

    **Top Matches:**

    {chr(10).join([f"{i+1}. {s[0].replace('_', ' ').title()}: {s[1]:.3f}" for i, s in enumerate(similarities[:3])])}

    """
    
    return response, quantum_viz, metrics_viz, retrieval_info

# Create Gradio Interface
with gr.Blocks(title="NEBULA Quantum RAG System", theme=gr.themes.Base()) as demo:
    gr.HTML("""

    <div style="text-align: center; padding: 20px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 10px; margin-bottom: 20px;">

        <h1 style="color: white; margin-bottom: 10px;">🌌 NEBULA Quantum RAG System</h1>

        <h3 style="color: white; margin-bottom: 0px;">Quantum-Enhanced Retrieval Augmented Generation</h3>

        <p style="color: white; margin-top: 10px;">Advanced information retrieval using quantum coherence and holographic neural processing</p>

    </div>

    """)
    
    with gr.Row():
        with gr.Column(scale=2):
            with gr.Group():
                gr.HTML("<h3>🔮 Quantum Query Interface</h3>")
                query_input = gr.Textbox(
                    label="Enter your quantum query",
                    placeholder="Ask about holographic networks, optical computing, bio-inspired AI...",
                    lines=3
                )
                search_btn = gr.Button("🚀 Quantum Search & Generate", variant="primary")
                
                gr.HTML("""

                <div style="margin-top: 15px; padding: 10px; background-color: rgba(255,255,255,0.1); border-radius: 5px;">

                    <h4 style="margin-top: 0;">💡 Example Queries:</h4>

                    <ul style="margin-bottom: 0;">

                        <li>"How do holographic neural networks store information?"</li>

                        <li>"What are the advantages of optical computing?"</li>

                        <li>"Explain quantum algorithms in AI"</li>

                        <li>"Compare P2P networks with traditional architectures"</li>

                    </ul>

                </div>

                """)
        
        with gr.Column(scale=1):
            with gr.Group():
                gr.HTML("<h3>📊 Quantum Metrics</h3>")
                retrieval_info = gr.Markdown("""

                📊 **System Status:**

                - Quantum States: Initialized

                - Coherence: Stable

                - Entanglement: Active

                - Knowledge Base: 8 domains loaded

                """)
    
    with gr.Row():
        with gr.Column():
            gr.HTML("<h3>🤖 Quantum RAG Response</h3>")
            rag_response = gr.Textbox(
                label="Generated Response",
                lines=12,
                interactive=False
            )
    
    with gr.Row():
        with gr.Column():
            gr.HTML("<h3>🌊 Quantum State Visualization</h3>")
            quantum_plot = gr.Plot(label="Quantum State Space")
        
        with gr.Column():
            gr.HTML("<h3>📈 Retrieval Performance</h3>")
            metrics_plot = gr.Plot(label="Similarity Scores")
    
    # Event handlers
    search_btn.click(
        fn=process_quantum_rag_query,
        inputs=query_input,
        outputs=[rag_response, quantum_plot, metrics_plot, retrieval_info]
    )
    
    # Initial load
    demo.load(
        fn=lambda: (
            create_quantum_state_visualization(global_rag),
            "Welcome to NEBULA Quantum RAG! Enter a query to begin quantum-enhanced information retrieval."
        ),
        outputs=[quantum_plot, retrieval_info]
    )
    
    gr.HTML("""

    <div style="margin-top: 30px; padding: 15px; background-color: #f0f0f0; border-radius: 10px;">

        <h4>🧬 About Quantum RAG</h4>

        <p>The NEBULA Quantum RAG system combines quantum information theory with holographic neural networks 

        to create an advanced retrieval-augmented generation framework. Unlike traditional RAG systems, 

        this approach uses quantum state representations and coherence measures for enhanced accuracy.</p>

        

        <p><strong>Key Innovations:</strong></p>

        <ul>

            <li>🌊 Quantum state encoding of knowledge documents</li>

            <li>🔗 Entanglement-based similarity calculations</li>

            <li>🎯 Coherence-weighted retrieval scoring</li>

            <li>🧠 Holographic information integration</li>

        </ul>

        

        <p><strong>Research by:</strong> Francisco Angulo de Lafuente</p>

        <p><strong>Model:</strong> 

            <a href="https://huggingface.co/Agnuxo/NEBULA-X" target="_blank">NEBULA-X Neural Model</a>

        </p>

    </div>

    """)

if __name__ == "__main__":
    demo.launch()