Create app.py

app.py

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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)