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| 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) |