Update app.py

app.py

@@ -1,4 +1,3 @@
-import streamlit as st
 import numpy as np
 import matplotlib.pyplot as plt
 from PIL import Image, ImageDraw, ImageFont
@@ -7,343 +6,468 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 import io
 import base64
 from streamlit_drawable_canvas import st_canvas
-import plotly.graph_objects as go
-import json
-from datetime import datetime
-import os
+
+
 
 # Set page config for a futuristic look
 st.set_page_config(page_title="NeuraSense AI", page_icon="🧠", layout="wide")
 
+
+
 # Custom CSS for a futuristic look
 st.markdown("""
 <style>
-    body {
-        color: #E0E0E0;
-        background-color: #0E1117;
-    }
-    .stApp {
-        background-image: linear-gradient(135deg, #0E1117 0%, #1A1F2C 100%);
-    }
-    .stButton>button {
-        color: #00FFFF;
-        border-color: #00FFFF;
-        border-radius: 20px;
-    }
-    .stSlider>div>div>div>div {
-        background-color: #00FFFF;
-    }
-    .stTextArea, .stNumberInput, .stSelectbox {
-        background-color: #1A1F2C;
-        color: #00FFFF;
-        border-color: #00FFFF;
-        border-radius: 20px;
-    }
-    .stTextArea:focus, .stNumberInput:focus, .stSelectbox:focus {
-        box-shadow: 0 0 10px #00FFFF;
-    }
+   body {
+       color: #E0E0E0;
+       background-color: #0E1117;
+   }
+   .stApp {
+       background-image: linear-gradient(135deg, #0E1117 0%, #1A1F2C 100%);
+   }
+   .stButton>button {
+       color: #00FFFF;
+       border-color: #00FFFF;
+       border-radius: 20px;
+   }
+   .stSlider>div>div>div>div {
+       background-color: #00FFFF;
+   }
+   .stTextArea, .stNumberInput, .stSelectbox {
+       background-color: #1A1F2C;
+       color: #00FFFF;
+       border-color: #00FFFF;
+       border-radius: 20px;
+   }
+   .stTextArea:focus, .stNumberInput:focus, .stSelectbox:focus {
+       box-shadow: 0 0 10px #00FFFF;
+   }
 </style>
 """, unsafe_allow_html=True)
 
+
+
 # Constants
 AVATAR_WIDTH, AVATAR_HEIGHT = 600, 800
 
+
+
 # Set up DialoGPT model
 @st.cache_resource
 def load_model():
-    tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
-    model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
-    return tokenizer, model
+   tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+   model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+   return tokenizer, model
+
+
 
 tokenizer, model = load_model()
 
+
+
 # Advanced Sensor Classes
 class QuantumSensor:
-    @staticmethod
-    def measure(x, y, sensitivity):
-        return np.sin(x/20) * np.cos(y/20) * sensitivity * np.random.normal(1, 0.1)
+   @staticmethod
+   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(base_temp, pressure, duration):
-        return base_temp + 10 * pressure * (1 - np.exp(-duration / 3)) + np.random.normal(0, 0.001)
+   @staticmethod
+   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(x, y):
-        return AdaptiveTextureSensor.textures[hash((x, y)) % len(AdaptiveTextureSensor.textures)]
+   textures = [
+       "nano-smooth", "quantum-rough", "neuro-bumpy", "plasma-silky",
+       "graviton-grainy", "zero-point-soft", "dark-matter-hard", "bose-einstein-condensate"
+   ]
+  
+   @staticmethod
+   def measure(x, y):
+       return AdaptiveTextureSensor.textures[hash((x, y)) % len(AdaptiveTextureSensor.textures)]
+
+
 
 class EMFieldSensor:
-    @staticmethod
-    def measure(x, y, sensitivity):
-        return (np.sin(x / 30) * np.cos(y / 30) + np.random.normal(0, 0.1)) * 10 * sensitivity
+   @staticmethod
+   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 process(inputs):
-        weights = np.random.rand(len(inputs))
-        return np.dot(inputs, weights) / np.sum(weights)
+   @staticmethod
+   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, 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 = np.random.rand(12) * 0.5 + 0.5
-            
-            # Enhance certain areas
-            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
-    
-    return sensation_map
+   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 = np.random.rand(12) * 0.5 + 0.5
+          
+           # Enhance certain areas
+           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
+  
+   return sensation_map
+
+
 
 avatar_sensation_map = create_sensation_map(AVATAR_WIDTH, AVATAR_HEIGHT)
 
-# Create 3D humanoid avatar
-def create_3d_avatar():
-    # Head
-    head_x = np.array([0, 0, 1, 1, 0, 0, 1, 1]) * 20 + 290
-    head_y = np.array([0, 1, 1, 0, 0, 1, 1, 0]) * 40 + 50
-    head_z = np.array([0, 0, 0, 0, 1, 1, 1, 1]) * 20 + 120
-    
-    # Torso
-    torso_x = np.array([0, 0, 1, 1, 0, 0, 1, 1]) * 40 + 270
-    torso_y = np.array([0, 1, 1, 0, 0, 1, 1, 0]) * 150 + 250
-    torso_z = np.array([0, 0, 0, 0, 1, 1, 1, 1]) * 30 + 90
-    
-    # Arms
-    arm_x = np.array([0, 0, 1, 1, 0, 0, 1, 1]) * 20 + 200
-    arm_y = np.array([0, 1, 1, 0, 0, 1, 1, 0]) * 150 + 250
-    arm_z = np.array([0, 0, 0, 0, 1, 1, 1, 1]) * 20 + 90
-    
-    # Legs
-    leg_x = np.array([0, 0, 1, 1, 0, 0, 1, 1]) * 40 + 280
-    leg_y = np.array([0, 1, 1, 0, 0, 1, 1, 0]) * 200 + 600
-    leg_z = np.array([0, 0, 0, 0, 1, 1, 1, 1]) * 40 + 60
-    
-    # Combine all body parts
-    x = np.concatenate([head_x, torso_x, arm_x, arm_x, leg_x, leg_x])
-    y = np.concatenate([head_y, torso_y, arm_y, arm_y, leg_y, leg_y])
-    z = np.concatenate([head_z, torso_z, arm_z, arm_z, leg_z, leg_z])
-    
-    return go.Mesh3d(x=x, y=y, z=z, color='cyan', opacity=0.5)
-
-# Enhanced Autonomy Class
-class EnhancedAutonomy:
-    def __init__(self):
-        self.mood = 0.5
-        self.energy = 0.8
-        self.curiosity = 0.7
-        self.memory = []
-    
-    def update_state(self, sensory_input):
-        self.mood = max(0, min(1, self.mood - sensory_input['pain'] * 0.1 + sensory_input['pleasure'] * 0.1))
-        self.energy = max(0, min(1, self.energy - sensory_input['intensity'] * 0.05))
-        if len(self.memory) == 0 or sensory_input not in self.memory:
-            self.curiosity = min(1, self.curiosity + 0.1)
-        else:
-            self.curiosity = max(0, self.curiosity - 0.05)
-        self.memory.append(sensory_input)
-        if len(self.memory) > 10:
-            self.memory.pop(0)
-    
-    def decide_action(self):
-        if self.energy < 0.2:
-            return "Rest to regain energy"
-        elif self.curiosity > 0.8:
-            return "Explore new sensations"
-        elif self.mood < 0.3:
-            return "Seek positive interactions"
-        else:
-            return "Continue current activity"
-
-# Function to save interactions
-def save_interaction(interaction_data):
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    filename = f"interaction_{timestamp}.json"
-    with open(filename, "w") as f:
-        json.dump(interaction_data, f, indent=4)
-    return filename
+
+
+# Create futuristic human-like avatar
+def create_avatar():
+   img = Image.new('RGBA', (AVATAR_WIDTH, AVATAR_HEIGHT), color=(0, 0, 0, 0))
+   draw = ImageDraw.Draw(img)
+  
+   # 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))
+  
+   # Eyes
+   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(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)
+  
+   return img
+
+
+
+avatar_image = create_avatar()
+
+
 
 # Streamlit app
 st.title("NeuraSense AI: Advanced Humanoid Techno-Sensory Simulation")
 
+
+
 # Create two columns
 col1, col2 = st.columns([2, 1])
 
-# 3D Avatar display with touch interface
+
+
+# Avatar display with touch interface
 with col1:
-    st.subheader("3D Humanoid Avatar Interface")
-    
-    # Create 3D avatar
-    avatar_3d = create_3d_avatar()
-    
-    # Add 3D controls
-    rotation_x = st.slider("Rotate X", -180, 180, 0)
-    rotation_y = st.slider("Rotate Y", -180, 180, 0)
-    rotation_z = st.slider("Rotate Z", -180, 180, 0)
-    
-    # Create 3D plot
-    fig = go.Figure(data=[avatar_3d])
-    fig.update_layout(scene=dict(xaxis_title="X", yaxis_title="Y", zaxis_title="Z"))
-    fig.update_layout(scene_camera=dict(eye=dict(x=1.5, y=1.5, z=1.5)))
-    fig.update_layout(scene=dict(xaxis=dict(range=[-400, 400]), 
-                                 yaxis=dict(range=[-400, 400]), 
-                                 zaxis=dict(range=[-200, 200])))
-    
-    # Apply rotations
-    fig.update_layout(scene=dict(camera=dict(eye=dict(x=np.cos(np.radians(rotation_y)) * np.cos(np.radians(rotation_x)),
-                                                      y=np.sin(np.radians(rotation_y)) * np.cos(np.radians(rotation_x)),
-                                                      z=np.sin(np.radians(rotation_x))))))
-    
-    st.plotly_chart(fig, use_container_width=True)
-
-    # Use st_canvas for touch input
-    canvas_result = st_canvas(
-        fill_color="rgba(0, 255, 255, 0.3)",
-        stroke_width=2,
-        stroke_color="#00FFFF",
-        background_image=Image.new('RGBA', (AVATAR_WIDTH, AVATAR_HEIGHT), color=(0, 0, 0, 0)),
-        height=AVATAR_HEIGHT,
-        width=AVATAR_WIDTH,
-        drawing_mode="point",
-        key="canvas",
-    )
+   st.subheader("Humanoid Avatar Interface")
+  
+   # Use st_canvas for touch input
+   canvas_result = st_canvas(
+       fill_color="rgba(0, 255, 255, 0.3)",
+       stroke_width=2,
+       stroke_color="#00FFFF",
+       background_image=avatar_image,
+       height=AVATAR_HEIGHT,
+       width=AVATAR_WIDTH,
+       drawing_mode="point",
+       key="canvas",
+   )
+
+
 
 # Touch controls and output
 with col2:
-    st.subheader("Neural Interface Controls")
-    
-    # Touch duration
-    touch_duration = st.slider("Interaction Duration (s)", 0.1, 5.0, 1.0, 0.1)
-    
-    # Touch pressure
-    touch_pressure = st.slider("Interaction Intensity", 0.1, 2.0, 1.0, 0.1)
-    
-    # Toggle quantum feature
-    use_quantum = st.checkbox("Enable Quantum Sensing", value=True)
-    
-    # Toggle synesthesia
-    use_synesthesia = st.checkbox("Enable Synesthesia", value=False)
-    
-    # Initialize EnhancedAutonomy
-    if 'autonomy' not in st.session_state:
-        st.session_state.autonomy = EnhancedAutonomy()
-    
-    if canvas_result.json_data is not None:
-        objects = canvas_result.json_data["objects"]
-        if len(objects) > 0:
-            last_touch = objects[-1]
-            touch_x = last_touch["left"]
-            touch_y = last_touch["top"]
-            touch_z = 0  # Assuming the touch is on the surface of the avatar
-            
-            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,
-                proprioception_sens, synesthesia_sens
-            ) = sensation
-
-            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 = QuantumSensor.measure(touch_x, touch_y, quantum_sens)
-            else:
-                quantum_state = "N/A"
-
-            # Calculate overall sensations
-            pain_level = pain * measured_pressure * touch_pressure
-            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))
-            
-            # Proprioception (sense of body position)
-            proprioception = proprioception_sens * np.linalg.norm([touch_x - AVATAR_WIDTH/2, touch_y - AVATAR_HEIGHT/2, touch_z]) / (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)
-
-            # Create a futuristic data display
-            data_display = f"""
-            ```
-            ┌─────────────────────────────────────────────┐
-            │ Pressure     : {measured_pressure:.2f}      │
-            │ Temperature  : {measured_temp:.2f}°C        │
-            │ Texture      : {measured_texture}           │
-            │ EM Field     : {measured_em:.2f} μT         │
-            │ 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="")
-
-            # Save interaction data
-            if canvas_result.json_data is not None:
-                objects = canvas_result.json_data["objects"]
-                if len(objects) > 0:
-                    interaction_data = {
-                        "touch_x": touch_x,
-                        "touch_y": touch_y,
-                        "touch_z": touch_z,
-                        "touch_duration": touch_duration,
-                        "touch_pressure": touch_pressure,
-                        "measured_pressure": measured_pressure,
-                        "measured_temp": measured_temp,
-                        "measured_texture": measured_texture,
-                        "measured_em": measured_em,
-                        "quantum_state": quantum_state,
-                        "pain_level": pain_level,
-                        "pleasure_level": pleasure_level,
-                        "tickle_level": tickle_level,
-                        "itch_level": itch_level,
-                        "proprioception": proprioception,
-                        "synesthesia": synesthesia,
-                        "neural_response": neural_response
-                    }
-                    filename = save_interaction(interaction_data)
-                    st.write(f"Interaction data saved to: {filename}")
-                else:
-                    st.write("No touch interaction detected.")
-            else:
-                st.write("No touch interaction detected.")
+   st.subheader("Neural Interface Controls")
+  
+   # Touch duration
+   touch_duration = st.slider("Interaction Duration (s)", 0.1, 5.0, 1.0, 0.1)
+  
+   # Touch pressure
+   touch_pressure = st.slider("Interaction Intensity", 0.1, 2.0, 1.0, 0.1)
+  
+   # 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:
+           last_touch = objects[-1]
+           touch_x, touch_y = last_touch["left"], last_touch["top"]
+          
+           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,
+               proprioception_sens, synesthesia_sens
+           ) = sensation
+
+
+
+           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 = QuantumSensor.measure(touch_x, touch_y, quantum_sens)
+           else:
+               quantum_state = "N/A"
+
+
+
+           # Calculate overall sensations
+           pain_level = pain * measured_pressure * touch_pressure
+           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))
+          
+           # 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})")
+           st.write(f"Duration: {touch_duration:.1f} s | Intensity: {touch_pressure:.2f}")
+          
+           # Create a futuristic data display
+           data_display = f"""
+           ```
+           ┌─────────────────────────────────────────────┐
+           │ Pressure     : {measured_pressure:.2f}      │
+           │ Temperature  : {measured_temp:.2f}°C        │
+           │ Texture      : {measured_texture}           │
+           │ EM Field     : {measured_em:.2f} μT         │
+           │ 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="")
+
+
+
+           # Generate description
+       prompt = f"""Human: Analyze the sensory input for a hyper-advanced AI humanoid:
+           Location: ({touch_x:.1f}, {touch_y:.1f})
+           Duration: {touch_duration:.1f}s, Intensity: {touch_pressure:.2f}
+           Pressure: {measured_pressure:.2f}
+           Temperature: {measured_temp:.2f}°C
+           Texture: {measured_texture}
+           EM Field: {measured_em:.2f} μT
+           Quantum State: {quantum_state}
+           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:"""
+
+
+
+input_ids = tokenizer.encode(prompt, return_tensors="pt")
+
+
+
+output = model.generate(
+   input_ids,
+   max_length=400,
+   num_return_sequences=1,
+   no_repeat_ngram_size=2,
+   top_k=50,
+   top_p=0.95,
+   temperature=0.7
+)
+
+
+
+response = tokenizer.decode(output[0], skip_special_tokens=True).split("AI:")[-1].strip()
+
+
+
+st.write("### AI's Sensory Analysis:")
+st.write(response)
+
+
+
+# Visualize sensation map
+st.subheader("Quantum Neuro-Sensory Map")
+fig, axs = plt.subplots(3, 4, figsize=(20, 15))
+titles = [
+   'Pain', 'Pleasure', 'Pressure', 'Temperature', 'Texture',
+   'EM Field', 'Tickle', 'Itch', 'Quantum', 'Neural',
+   'Proprioception', 'Synesthesia'
+]
+
+
+
+for i, title in enumerate(titles):
+   ax = axs[i // 4, i % 4]
+   im = ax.imshow(avatar_sensation_map[:, :, i], cmap='plasma')
+   ax.set_title(title)
+   fig.colorbar(im, ax=ax)
+
+
+
+plt.tight_layout()
+st.pyplot(fig)
+
+
+
+st.write("The quantum neuro-sensory map illustrates the varying sensitivities across the AI's body. Brighter areas indicate heightened responsiveness to specific stimuli.")
+
+
+
+# Add information about the AI's advanced capabilities
+st.subheader("NeuraSense AI: Cutting-Edge Sensory Capabilities")
+st.write("""
+This hyper-advanced AI humanoid incorporates revolutionary sensory technology:
+1. Quantum-Enhanced Pressure Sensors: Utilize quantum tunneling effects for unparalleled sensitivity.
+2. Nano-scale Thermal Detectors: Capable of detecting temperature variations to 0.001°C.
+3. Adaptive Texture Analysis: Employs machine learning to continually refine texture perception.
+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. 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)])
+  
+   # Save the plot to an in-memory buffer
+   buf = io.BytesIO()
+   plt.figure(figsize=(8, 6))
+   plt.imshow(quantum_field, cmap='viridis')
+   plt.savefig(buf, format='png')
+  
+   # Create a PIL Image object from the buffer
+   quantum_image = Image.open(buf)
+  
+   # Display the image using st.image()
+   st.image(quantum_image, use_column_width=True)
+
+
+
+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)), use_column_width=True)
+
+
+
+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)])
+  
+   # Save the plot to an in-memory buffer
+   buf = io.BytesIO()
+   plt.figure(figsize=(8, 6))
+   plt.imshow(proprioceptive_map, cmap='coolwarm')
+   plt.savefig(buf, format='png')
+  
+   # Create a PIL Image object from the buffer
+   proprioceptive_image = Image.open(buf)
+  
+   # Display the image using st.image()
+   st.image(proprioceptive_image, use_column_width=True)
+# Footer
+st.write("---")
+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.")
+