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)