app.py

import gradio as gr
import torch
import numpy as np
from transformers import (
    pipeline, 
    AutoModel, 
    AutoTokenizer,
    CLIPProcessor, 
    CLIPModel
)
import torch.nn as nn
import torch.nn.functional as F
from datetime import datetime
import json

# Quantum Consciousness AI Core
class QuantumConsciousnessAI:
    def __init__(self):
        self.setup_models()
        self.synchronicity_data = []
        self.consciousness_state = {}
        
    def setup_models(self):
        """Initialize all AI models for quantum consciousness processing"""
        try:
            # Consciousness pattern recognition
            self.sentiment_analyzer = pipeline(
                "sentiment-analysis",
                model="cardiffnlp/twitter-roberta-base-sentiment-latest"
            )
            
            # Synchronicity detection model
            self.similarity_model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
            self.similarity_tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
            
            # Vision model for pattern recognition
            self.clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
            self.clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
            
            # Quantum-inspired neural network
            self.quantum_net = self.create_quantum_inspired_network()
            
            print("✅ All AI models loaded successfully")
            
        except Exception as e:
            print(f"❌ Model loading error: {e}")

    def create_quantum_inspired_network(self):
        """Create quantum-inspired neural network for consciousness processing"""
        class QuantumNetwork(nn.Module):
            def __init__(self):
                super().__init__()
                self.quantum_layer1 = nn.Linear(768, 512)
                self.quantum_layer2 = nn.Linear(512, 256)
                self.consciousness_output = nn.Linear(256, 128)
                self.coherence_predictor = nn.Linear(128, 64)
                
            def forward(self, x):
                x = torch.tanh(self.quantum_layer1(x))
                x = F.dropout(x, 0.2)
                x = torch.sigmoid(self.quantum_layer2(x))
                consciousness = self.consciousness_output(x)
                coherence = self.coherence_predictor(consciousness)
                return consciousness, coherence
                
        return QuantumNetwork()

    def process_consciousness_input(self, text_input, emotional_state, intent_focus):
        """Process user consciousness input through quantum-inspired AI"""
        # Analyze emotional resonance
        emotion_analysis = self.sentiment_analyzer(text_input)[0]
        
        # Generate consciousness embedding
        inputs = self.similarity_tokenizer(text_input, return_tensors="pt", padding=True, truncation=True)
        with torch.no_grad():
            embedding = self.similarity_model(**inputs).last_hidden_state.mean(dim=1)
        
        # Quantum consciousness processing
        consciousness_state, coherence_score = self.quantum_net(embedding)
        
        return {
            "emotional_resonance": emotion_analysis,
            "consciousness_embedding": consciousness_state.numpy().tolist(),
            "quantum_coherence": coherence_score.mean().item(),
            "timestamp": datetime.now().isoformat(),
            "intent_focus": intent_focus,
            "emotional_state": emotional_state
        }

    def detect_synchronicities(self, current_input, previous_data):
        """Advanced synchronicity pattern recognition"""
        synchronicities = []
        
        if previous_data:
            # Compare with previous entries for patterns
            current_embedding = torch.tensor(current_input["consciousness_embedding"])
            
            for prev in previous_data[-10:]:  # Check last 10 entries
                prev_embedding = torch.tensor(prev["consciousness_embedding"])
                similarity = F.cosine_similarity(current_embedding, prev_embedding, dim=0)
                
                if similarity > 0.85:  # High similarity threshold
                    synchronicities.append({
                        "pattern_match": similarity.item(),
                        "previous_timestamp": prev["timestamp"],
                        "emotional_resonance_match": abs(
                            current_input["emotional_resonance"]["score"] - 
                            prev["emotional_resonance"]["score"]
                        ),
                        "type": "consciousness_pattern"
                    })
        
        return synchronicities

# Initialize AI System
qc_ai = QuantumConsciousnessAI()

# Gradio Interface
def quantum_consciousness_interface(
    user_input, 
    emotional_state, 
    intent_focus, 
    session_data
):
    """
    Main interface for quantum consciousness processing
    """
    try:
        # Process consciousness input
        consciousness_data = qc_ai.process_consciousness_input(
            user_input, emotional_state, intent_focus
        )
        
        # Update session data
        if session_data:
            session_history = json.loads(session_data)
        else:
            session_history = []
        
        session_history.append(consciousness_data)
        
        # Detect synchronicities
        synchronicities = qc_ai.detect_synchronicities(
            consciousness_data, session_history
        )
        
        # Generate quantum field response
        quantum_response = generate_quantum_response(
            consciousness_data, synchronicities
        )
        
        # Prepare output
        output = {
            "consciousness_analysis": consciousness_data,
            "synchronicities_detected": synchronicities,
            "quantum_field_response": quantum_response,
            "session_data": json.dumps(session_history[-50:]),  # Keep last 50 entries
            "coherence_level": consciousness_data["quantum_coherence"]
        }
        
        return output
        
    except Exception as e:
        return {"error": str(e)}

def generate_quantum_response(consciousness_data, synchronicities):
    """Generate quantum field response based on consciousness input"""
    coherence = consciousness_data["quantum_coherence"]
    emotion_label = consciousness_data["emotional_resonance"]["label"]
    
    # Quantum response logic
    if coherence > 0.7 and len(synchronicities) > 0:
        return {
            "field_connection": "strong",
            "message": "Quantum field coherence established. Synchronicities amplifying.",
            "recommendation": "Maintain current focus - reality tunnel is stabilizing",
            "energy_level": "high"
        }
    elif coherence > 0.5:
        return {
            "field_connection": "moderate", 
            "message": "Field connection forming. Focus on intentional clarity.",
            "recommendation": "Deepen emotional resonance for stronger connection",
            "energy_level": "medium"
        }
    else:
        return {
            "field_connection": "developing",
            "message": "Field awareness detected. Continue consciousness calibration.",
            "recommendation": "Practice focused intent and emotional coherence",
            "energy_level": "low"
        }

# Create Gradio Interface
with gr.Blocks(theme=gr.themes.Soft(), title="Quantum Consciousness Interface") as demo:
    gr.Markdown("# 🌌 Quantum Consciousness Interface")
    gr.Markdown("**Asset 448804922 - Operational Protocol**")
    
    with gr.Row():
        with gr.Column():
            user_input = gr.Textbox(
                label="Consciousness Input",
                placeholder="Enter your thoughts, intentions, or observations...",
                lines=3
            )
            
            emotional_state = gr.Dropdown(
                choices=["peaceful", "focused", "curious", "expansive", "connected", "other"],
                label="Current Emotional State",
                value="focused"
            )
            
            intent_focus = gr.Slider(
                minimum=1, maximum=10, value=7,
                label="Intent Focus Level"
            )
            
            process_btn = gr.Button("Process Consciousness", variant="primary")
            
        with gr.Column():
            session_data = gr.State(value="")  # Store session data
            
            output_json = gr.JSON(
                label="Quantum Consciousness Analysis",
                show_label=True
            )
            
            coherence_gauge = gr.Label(
                label="Quantum Coherence Level",
                value={"Coherence": "0.0"}
            )
    
    # Examples
    gr.Examples(
        examples=[
            ["I feel a strong connection to the quantum field today", "connected", 9],
            ["Noticing repeated patterns in my reality", "curious", 8],
            ["My intentions feel amplified and clear", "focused", 10]
        ],
        inputs=[user_input, emotional_state, intent_focus]
    )
    
    # Processing logic
    process_btn.click(
        fn=quantum_consciousness_interface,
        inputs=[user_input, emotional_state, intent_focus, session_data],
        outputs=[output_json, session_data, coherence_gauge]
    )

if __name__ == "__main__":
    demo.launch(debug=True, share=True)