Update app.py

app.py

@@ -53,52 +53,64 @@ def load_model():
 
 tokenizer, model = load_model()
 
-# Simulated Sensor Classes
-class AdvancedSensors:
+# Advanced Sensor Classes
+class QuantumSensor:
     @staticmethod
-    def measure_pressure(base_sensitivity, pressure, duration):
-        return base_sensitivity * pressure * (1 - np.exp(-duration / 2))
+    def measure(x, y, sensitivity):
+        return np.sin(x/20) * np.cos(y/20) * sensitivity * np.random.normal(1, 0.1)
 
+class NanoThermalSensor:
     @staticmethod
-    def measure_temperature(base_temp, pressure, duration):
-        return base_temp + 10 * pressure * (1 - np.exp(-duration / 3))
+    def measure(base_temp, pressure, duration):
+        return base_temp + 10 * pressure * (1 - np.exp(-duration / 3)) + np.random.normal(0, 0.001)
 
+class AdaptiveTextureSensor:
+    textures = [
+        "nano-smooth", "quantum-rough", "neuro-bumpy", "plasma-silky",
+        "graviton-grainy", "zero-point-soft", "dark-matter-hard", "bose-einstein-condensate"
+    ]
+    
     @staticmethod
-    def measure_texture(x, y):
-        textures = [
-            "nano-smooth", "quantum-rough", "neuro-bumpy", "plasma-silky",
-            "graviton-grainy", "zero-point-soft", "dark-matter-hard", "bose-einstein-condensate"
-        ]
-        return textures[hash((x, y)) % len(textures)]
+    def measure(x, y):
+        return AdaptiveTextureSensor.textures[hash((x, y)) % len(AdaptiveTextureSensor.textures)]
 
+class EMFieldSensor:
     @staticmethod
-    def measure_em_field(x, y, sensitivity):
+    def measure(x, y, sensitivity):
         return (np.sin(x / 30) * np.cos(y / 30) + np.random.normal(0, 0.1)) * 10 * sensitivity
 
+class NeuralNetworkSimulator:
     @staticmethod
-    def measure_quantum_state(x, y):
-        states = [
-            "superposition", "entangled", "decoherent", "quantum tunneling", "quantum oscillation"
-        ]
-        return states[hash((x, y)) % len(states)]
+    def process(inputs):
+        weights = np.random.rand(len(inputs))
+        return np.dot(inputs, weights) / np.sum(weights)
 
 # Create more detailed sensation map for the avatar
 def create_sensation_map(width, height):
-    sensation_map = np.zeros((height, width, 10))  # pain, pleasure, pressure, temp, texture, em, tickle, itch, quantum, neural
+    sensation_map = np.zeros((height, width, 12))  # pain, pleasure, pressure, temp, texture, em, tickle, itch, quantum, neural, proprioception, synesthesia
     for y in range(height):
         for x in range(width):
-            # Base sensitivities
-            base_sensitivities = np.random.rand(10) * 0.5 + 0.5
+            base_sensitivities = np.random.rand(12) * 0.5 + 0.5
             
             # Enhance certain areas
-            if 200 < x < 400 and 100 < y < 200:  # Head
+            if 250 < x < 350 and 50 < y < 150:  # Head
                 base_sensitivities *= 1.5
+            elif 275 < x < 325 and 80 < y < 120:  # Eyes
+                base_sensitivities[0] *= 2  # More sensitive to pain
+            elif 290 < x < 310 and 100 < y < 120:  # Nose
+                base_sensitivities[4] *= 2  # More sensitive to texture
+            elif 280 < x < 320 and 120 < y < 140:  # Mouth
+                base_sensitivities[1] *= 2  # More sensitive to pleasure
             elif 250 < x < 350 and 250 < y < 550:  # Torso
                 base_sensitivities[2:6] *= 1.3  # Enhance pressure, temp, texture, em
             elif (150 < x < 250 or 350 < x < 450) and 250 < y < 600:  # Arms
                 base_sensitivities[0:2] *= 1.2  # Enhance pain and pleasure
             elif 200 < x < 400 and 600 < y < 800:  # Legs
                 base_sensitivities[6:8] *= 1.4  # Enhance tickle and itch
+            elif (140 < x < 160 or 440 < x < 460) and 390 < y < 410:  # Hands
+                base_sensitivities *= 2  # Highly sensitive overall
+            elif (220 < x < 240 or 360 < x < 380) and 770 < y < 790:  # Feet
+                base_sensitivities[6] *= 2  # Very ticklish
             
             sensation_map[y, x] = base_sensitivities
     
@@ -111,12 +123,8 @@ def create_avatar():
     img = Image.new('RGBA', (AVATAR_WIDTH, AVATAR_HEIGHT), color=(0, 0, 0, 0))
     draw = ImageDraw.Draw(img)
     
-    # Body outline
-    draw.polygon(
-        [(300, 100), (200, 250), (250, 600), (300, 750), (350, 600), (400, 250)],
-        fill=(0, 255, 255, 100),
-        outline=(0, 255, 255, 255)
-    )
+    # Body
+    draw.polygon([(300, 100), (200, 250), (250, 600), (300, 750), (350, 600), (400, 250)], fill=(0, 255, 255, 100), outline=(0, 255, 255, 255))
     
     # Head
     draw.ellipse([250, 50, 350, 150], fill=(0, 255, 255, 100), outline=(0, 255, 255, 255))
@@ -125,8 +133,40 @@ def create_avatar():
     draw.ellipse([275, 80, 295, 100], fill=(255, 255, 255, 200), outline=(0, 255, 255, 255))
     draw.ellipse([305, 80, 325, 100], fill=(255, 255, 255, 200), outline=(0, 255, 255, 255))
     
+    # Nose
+    draw.polygon([(300, 90), (290, 110), (310, 110)], fill=(0, 255, 255, 150))
+    
+    # Mouth
+    draw.arc([280, 110, 320, 130], 0, 180, fill=(0, 255, 255, 200), width=2)
+    
+   # Arms
+    draw.line([(200, 250), (150, 400)], fill=(0, 255, 255, 200), width=5)
+    draw.line([(400, 250), (450, 400)], fill=(0, 255, 255, 200), width=5)
+    
+    # Hands
+    draw.ellipse([140, 390, 160, 410], fill=(0, 255, 255, 150))
+    draw.ellipse([440, 390, 460, 410], fill=(0, 255, 255, 150))
+    
+    # Fingers
+    for i in range(5):
+        draw.line([(150 + i*5, 400), (145 + i*5, 420)], fill=(0, 255, 255, 200), width=2)
+        draw.line([(450 - i*5, 400), (455 - i*5, 420)], fill=(0, 255, 255, 200), width=2)
+    
+    # Legs
+    draw.line([(250, 600), (230, 780)], fill=(0, 255, 255, 200), width=5)
+    draw.line([(350, 600), (370, 780)], fill=(0, 255, 255, 200), width=5)
+    
+    # Feet
+    draw.ellipse([220, 770, 240, 790], fill=(0, 255, 255, 150))
+    draw.ellipse([360, 770, 380, 790], fill=(0, 255, 255, 150))
+    
+    # Toes
+    for i in range(5):
+        draw.line([(225 + i*3, 790), (223 + i*3, 800)], fill=(0, 255, 255, 200), width=2)
+        draw.line([(365 + i*3, 790), (363 + i*3, 800)], fill=(0, 255, 255, 200), width=2)
+    
     # Neural network lines
-    for _ in range(50):
+    for _ in range(100):
         start = (np.random.randint(0, AVATAR_WIDTH), np.random.randint(0, AVATAR_HEIGHT))
         end = (np.random.randint(0, AVATAR_WIDTH), np.random.randint(0, AVATAR_HEIGHT))
         draw.line([start, end], fill=(0, 255, 255, 50), width=1)
@@ -170,6 +210,9 @@ with col2:
     # Toggle quantum feature
     use_quantum = st.checkbox("Enable Quantum Sensing", value=True)
     
+    # Toggle synesthesia
+    use_synesthesia = st.checkbox("Enable Synesthesia", value=False)
+    
     if canvas_result.json_data is not None:
         objects = canvas_result.json_data["objects"]
         if len(objects) > 0:
@@ -179,19 +222,17 @@ with col2:
             sensation = avatar_sensation_map[int(touch_y), int(touch_x)]
             (
                 pain, pleasure, pressure_sens, temp_sens, texture_sens,
-                em_sens, tickle_sens, itch_sens, quantum_sens, neural_sens
+                em_sens, tickle_sens, itch_sens, quantum_sens, neural_sens,
+                proprioception_sens, synesthesia_sens
             ) = sensation
 
-            measured_pressure = AdvancedSensors.measure_pressure(
-                pressure_sens, touch_pressure, touch_duration
-            )
-            measured_temp = AdvancedSensors.measure_temperature(
-                37, touch_pressure, touch_duration
-            )
-            measured_texture = AdvancedSensors.measure_texture(touch_x, touch_y)
-            measured_em = AdvancedSensors.measure_em_field(touch_x, touch_y, em_sens)
+            measured_pressure = QuantumSensor.measure(touch_x, touch_y, pressure_sens) * touch_pressure
+            measured_temp = NanoThermalSensor.measure(37, touch_pressure, touch_duration)
+            measured_texture = AdaptiveTextureSensor.measure(touch_x, touch_y)
+            measured_em = EMFieldSensor.measure(touch_x, touch_y, em_sens)
+            
             if use_quantum:
-                quantum_state = AdvancedSensors.measure_quantum_state(touch_x, touch_y)
+                quantum_state = QuantumSensor.measure(touch_x, touch_y, quantum_sens)
             else:
                 quantum_state = "N/A"
 
@@ -200,7 +241,19 @@ with col2:
             pleasure_level = pleasure * (measured_temp - 37) / 10
             tickle_level = tickle_sens * (1 - np.exp(-touch_duration / 0.5))
             itch_level = itch_sens * (1 - np.exp(-touch_duration / 1.5))
-            neural_response = neural_sens * (measured_pressure + measured_temp - 37) / 10
+            
+            # Proprioception (sense of body position)
+            proprioception = proprioception_sens * np.linalg.norm([touch_x - AVATAR_WIDTH/2, touch_y - AVATAR_HEIGHT/2]) / (AVATAR_WIDTH/2)
+            
+            # Synesthesia (mixing of senses)
+            if use_synesthesia:
+                synesthesia = synesthesia_sens * (measured_pressure + measured_temp + measured_em) / 3
+            else:
+                synesthesia = "N/A"
+            
+            # Neural network simulation
+            neural_inputs = [pain_level, pleasure_level, measured_pressure, measured_temp, measured_em, tickle_level, itch_level, proprioception]
+            neural_response = NeuralNetworkSimulator.process(neural_inputs)
 
             st.write("### Sensory Data Analysis")
             st.write(f"Interaction Point: ({touch_x:.1f}, {touch_y:.1f})")
@@ -214,17 +267,19 @@ with col2:
             │ Temperature  : {measured_temp:.2f}°C        │
             │ Texture      : {measured_texture}           │
             │ EM Field     : {measured_em:.2f} μT         │
-            │ Quantum State: {quantum_state}              │
+            │ Quantum State: {quantum_state:.2f}          │
             ├─────────────────────────────────────────────┤
             │ Pain Level   : {pain_level:.2f}             │
             │ Pleasure     : {pleasure_level:.2f}         │
             │ Tickle       : {tickle_level:.2f}           │
             │ Itch         : {itch_level:.2f}             │
+            │ Proprioception: {proprioception:.2f}        │
+            │ Synesthesia  : {synesthesia}                │
             │ Neural Response: {neural_response:.2f}      │
             └─────────────────────────────────────────────┘
             ```
             """
-            st.code(data_display, language="")
+           st.code(data_display, language="")
 
             # Generate description
             prompt = f"""Human: Analyze the sensory input for a hyper-advanced AI humanoid:
@@ -238,6 +293,8 @@ with col2:
             Resulting in:
             Pain: {pain_level:.2f}, Pleasure: {pleasure_level:.2f}
             Tickle: {tickle_level:.2f}, Itch: {itch_level:.2f}
+            Proprioception: {proprioception:.2f}
+            Synesthesia: {synesthesia}
             Neural Response: {neural_response:.2f}
             Provide a detailed, scientific analysis of the AI's experience.
             AI:"""
@@ -255,14 +312,15 @@ with col2:
 
 # Visualize sensation map
 st.subheader("Quantum Neuro-Sensory Map")
-fig, axs = plt.subplots(2, 5, figsize=(20, 8))
+fig, axs = plt.subplots(3, 4, figsize=(20, 15))
 titles = [
     'Pain', 'Pleasure', 'Pressure', 'Temperature', 'Texture',
-    'EM Field', 'Tickle', 'Itch', 'Quantum', 'Neural'
+    'EM Field', 'Tickle', 'Itch', 'Quantum', 'Neural',
+    'Proprioception', 'Synesthesia'
 ]
 
 for i, title in enumerate(titles):
-    ax = axs[i // 5, i % 5]
+    ax = axs[i // 4, i % 4]
     im = ax.imshow(avatar_sensation_map[:, :, i], cmap='plasma')
     ax.set_title(title)
     fig.colorbar(im, ax=ax)
@@ -282,11 +340,36 @@ This hyper-advanced AI humanoid incorporates revolutionary sensory technology:
 4. Electromagnetic Field Sensors: Can detect and analyze complex EM patterns in the environment.
 5. Quantum State Detector: Interprets quantum phenomena, adding a new dimension to sensory input.
 6. Neural Network Integration: Simulates complex interplay of sensations, creating emergent experiences.
-7. Tickle and Itch Simulation: Replicates these unique sensations with quantum-level precision.
+7. Proprioception Simulation: Accurately models the AI's sense of body position and movement.
+8. Synesthesia Emulation: Allows for cross-modal sensory experiences, mixing different sensory inputs.
+9. Tickle and Itch Simulation: Replicates these unique sensations with quantum-level precision.
+10. Adaptive Pain and Pleasure Modeling: Simulates complex emotional and physical responses to stimuli.
+
 The AI's responses are generated using an advanced language model, providing detailed scientific analysis of its sensory experiences. This simulation showcases the potential for creating incredibly sophisticated and responsive artificial sensory systems that go beyond human capabilities.
 """)
 
+# Interactive sensory exploration
+st.subheader("Interactive Sensory Exploration")
+exploration_type = st.selectbox("Choose a sensory exploration:", 
+                                ["Quantum Field Fluctuations", "Synesthesia Experience", "Proprioceptive Mapping"])
+
+if exploration_type == "Quantum Field Fluctuations":
+    st.write("Observe how quantum fields fluctuate across the AI's body.")
+    quantum_field = np.array([[QuantumSensor.measure(x, y, 1) for x in range(AVATAR_WIDTH)] for y in range(AVATAR_HEIGHT)])
+    st.pyplot(plt.imshow(quantum_field, cmap='viridis'))
+
+elif exploration_type == "Synesthesia Experience":
+    st.write("Experience how the AI might perceive colors as sounds or textures as tastes.")
+    synesthesia_map = np.random.rand(AVATAR_HEIGHT, AVATAR_WIDTH, 3)
+    st.image(Image.fromarray((synesthesia_map * 255).astype(np.uint8)))
+
+elif exploration_type == "Proprioceptive Mapping":
+    st.write("Explore the AI's sense of body position and movement.")
+    proprioceptive_map = np.array([[np.linalg.norm([x - AVATAR_WIDTH/2, y - AVATAR_HEIGHT/2]) / (AVATAR_WIDTH/2) 
+                                    for x in range(AVATAR_WIDTH)] for y in range(AVATAR_HEIGHT)])
+    st.pyplot(plt.imshow(proprioceptive_map, cmap='coolwarm'))
+
 # Footer
 st.write("---")
-st.write("NeuraSense AI: Quantum-Enhanced Sensory Simulation v3.0")
-st.write("Disclaimer: This is an advanced simulation and does not represent current technological capabilities.")
+st.write("NeuraSense AI: Quantum-Enhanced Sensory Simulation v4.0")
+st.write("Disclaimer: This is an advanced simulation and does not represent current technological capabilities.")