import streamlit as st
from pathlib import Path
import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw, ImageFont
import time
from transformers import AutoModelForCausalLM, AutoTokenizer
import seaborn as sns
from io import BytesIO
import base64
from streamlit_drawable_canvas import st_canvas
import io
import torch
import cv2
import mediapipe as mp
import base64


# Set page config
st.set_page_config(page_title="NeuraSense AI", page_icon="🧠", layout="wide")

# Enhanced Custom CSS for a hyper-cyberpunk realistic look
custom_css = """
<style>
    @import url('https://fonts.googleapis.com/css2?family=Orbitron:wght@400;500;700&family=Roboto+Mono:wght@400;700&display=swap');

    :root {
        --neon-blue: #00FFFF;
        --neon-pink: #FF00FF;
        --neon-green: #39FF14;
        --dark-bg: #0a0a0a;
        --darker-bg: #050505;
        --light-text: #E0E0E0;
    }

    body {
        color: var(--light-text);
        background-color: var(--dark-bg);
        font-family: 'Roboto Mono', monospace;
        overflow-x: hidden;
    }

    .stApp {
        background: 
            linear-gradient(45deg, var(--darker-bg) 0%, var(--dark-bg) 100%),
            repeating-linear-gradient(45deg, #000 0%, #000 2%, transparent 2%, transparent 4%),
            repeating-linear-gradient(-45deg, #111 0%, #111 1%, transparent 1%, transparent 3%);
        background-blend-mode: overlay;
        animation: backgroundPulse 20s infinite alternate;
    }

    @keyframes backgroundPulse {
        0% { background-position: 0% 50%; }
        100% { background-position: 100% 50%; }
    }

    h1, h2, h3 {
        font-family: 'Orbitron', sans-serif;
        position: relative;
        text-shadow: 
            0 0 5px var(--neon-blue),
            0 0 10px var(--neon-blue),
            0 0 20px var(--neon-blue),
            0 0 40px var(--neon-blue);
        animation: textGlitch 5s infinite alternate;
    }

    @keyframes textGlitch {
        0% { transform: skew(0deg); }
        20% { transform: skew(5deg); text-shadow: 3px 3px 0 var(--neon-pink); }
        40% { transform: skew(-5deg); text-shadow: -3px -3px 0 var(--neon-green); }
        60% { transform: skew(3deg); text-shadow: 2px -2px 0 var(--neon-blue); }
        80% { transform: skew(-3deg); text-shadow: -2px 2px 0 var(--neon-pink); }
        100% { transform: skew(0deg); }
    }

    .stButton>button {
        color: var(--neon-blue);
        border: 2px solid var(--neon-blue);
        border-radius: 5px;
        background: linear-gradient(45deg, rgba(0,255,255,0.1), rgba(0,255,255,0.3));
        box-shadow: 0 0 15px var(--neon-blue);
        transition: all 0.3s ease;
        text-transform: uppercase;
        letter-spacing: 2px;
        backdrop-filter: blur(5px);
    }

    .stButton>button:hover {
        transform: scale(1.05) translateY(-3px);
        box-shadow: 0 0 30px var(--neon-blue);
        text-shadow: 0 0 5px var(--neon-blue);
    }

    .stTextInput>div>div>input, .stTextArea>div>div>textarea, .stSelectbox>div>div>div {
        background-color: rgba(0, 255, 255, 0.1);
        border: 1px solid var(--neon-blue);
        border-radius: 5px;
        color: var(--neon-blue);
        backdrop-filter: blur(5px);
    }

    .stTextInput>div>div>input:focus, .stTextArea>div>div>textarea:focus, .stSelectbox>div>div>div:focus {
        box-shadow: 0 0 20px var(--neon-blue);
    }

    .stSlider>div>div>div>div {
        background-color: var(--neon-blue);
    }

    .stSlider>div>div>div>div>div {
        background-color: var(--neon-pink);
        box-shadow: 0 0 10px var(--neon-pink);
    }

    ::-webkit-scrollbar {
        width: 10px;
        height: 10px;
    }

    ::-webkit-scrollbar-track {
        background: var(--darker-bg);
        border-radius: 5px;
    }

    ::-webkit-scrollbar-thumb {
        background: var(--neon-blue);
        border-radius: 5px;
        box-shadow: 0 0 5px var(--neon-blue);
    }

    ::-webkit-scrollbar-thumb:hover {
        background: var(--neon-pink);
        box-shadow: 0 0 5px var(--neon-pink);
    }

    .stPlot, .stDataFrame {
        border: 1px solid var(--neon-blue);
        border-radius: 5px;
        overflow: hidden;
        box-shadow: 0 0 15px rgba(0, 255, 255, 0.3);
    }

    .stImage, .stIcon {
        filter: drop-shadow(0 0 5px var(--neon-blue));
    }

    .stSidebar, .stContainer {
        background: 
            linear-gradient(45deg, var(--darker-bg) 0%, var(--dark-bg) 100%),
            repeating-linear-gradient(45deg, #000 0%, #000 2%, transparent 2%, transparent 4%);
        animation: sidebarPulse 10s infinite alternate;
    }

    @keyframes sidebarPulse {
        0% { background-position: 0% 50%; }
        100% { background-position: 100% 50%; }
    }

    .element-container {
        position: relative;
    }

    .element-container::before {
        content: '';
        position: absolute;
        top: -5px;
        left: -5px;
        right: -5px;
        bottom: -5px;
        border: 1px solid var(--neon-blue);
        border-radius: 10px;
        opacity: 0.5;
        pointer-events: none;
    }

    .stMarkdown a {
        color: var(--neon-pink);
        text-decoration: none;
        position: relative;
        transition: all 0.3s ease;
    }

    .stMarkdown a::after {
        content: '';
        position: absolute;
        width: 100%;
        height: 1px;
        bottom: -2px;
        left: 0;
        background-color: var(--neon-pink);
        transform: scaleX(0);
        transform-origin: bottom right;
        transition: transform 0.3s ease;
    }

    .stMarkdown a:hover::after {
        transform: scaleX(1);
        transform-origin: bottom left;
    }

    /* Cyberpunk-style progress bar */
    .stProgress > div > div {
        background-color: var(--neon-blue);
        background-image: linear-gradient(
            45deg, 
            var(--neon-pink) 25%, 
            transparent 25%, 
            transparent 50%, 
            var(--neon-pink) 50%, 
            var(--neon-pink) 75%, 
            transparent 75%, 
            transparent
        );
        background-size: 40px 40px;
        animation: progress-bar-stripes 1s linear infinite;
    }

    @keyframes progress-bar-stripes {
        0% { background-position: 40px 0; }
        100% { background-position: 0 0; }
    }

    /* Glowing checkbox */
    .stCheckbox > label > div {
        border-color: var(--neon-blue);
        transition: all 0.3s ease;
    }

    .stCheckbox > label > div[data-checked="true"] {
        background-color: var(--neon-blue);
        box-shadow: 0 0 10px var(--neon-blue);
    }

    /* Futuristic radio button */
    .stRadio > div {
        background-color: rgba(0, 255, 255, 0.1);
        border-radius: 10px;
        padding: 10px;
    }

    .stRadio > div > label > div {
        border-color: var(--neon-blue);
        transition: all 0.3s ease;
    }

    .stRadio > div > label > div[data-checked="true"] {
        background-color: var(--neon-blue);
        box-shadow: 0 0 10px var(--neon-blue);
    }

    /* Cyberpunk-style tables */
    .stDataFrame table {
        border-collapse: separate;
        border-spacing: 0;
        border: 1px solid var(--neon-blue);
        border-radius: 10px;
        overflow: hidden;
    }

    .stDataFrame th {
        background-color: rgba(0, 255, 255, 0.2);
        color: var(--neon-blue);
        text-transform: uppercase;
        letter-spacing: 1px;
    }

    .stDataFrame td {
        border-bottom: 1px solid rgba(0, 255, 255, 0.2);
    }

    .stDataFrame tr:last-child td {
        border-bottom: none;
    }

    /* Futuristic file uploader */
    .stFileUploader > div {
        border: 2px dashed var(--neon-blue);
        border-radius: 10px;
        background-color: rgba(0, 255, 255, 0.05);
        transition: all 0.3s ease;
    }

    .stFileUploader > div:hover {
        background-color: rgba(0, 255, 255, 0.1);
        box-shadow: 0 0 15px rgba(0, 255, 255, 0.3);
    }

    /* Cyberpunk-style tooltips */
    .stTooltipIcon {
        color: var(--neon-pink);
        transition: all 0.3s ease;
    }

    .stTooltipIcon:hover {
        color: var(--neon-blue);
        text-shadow: 0 0 5px var(--neon-blue);
    }

    /* Futuristic date input */
    .stDateInput > div > div > input {
        background-color: rgba(0, 255, 255, 0.1);
        border: 1px solid var(--neon-blue);
        border-radius: 5px;
        color: var(--neon-blue);
        backdrop-filter: blur(5px);
    }

    .stDateInput > div > div > input:focus {
        box-shadow: 0 0 20px var(--neon-blue);
    }

    /* Cyberpunk-style code blocks */
    .stCodeBlock {
        background-color: rgba(0, 0, 0, 0.6);
        border: 1px solid var(--neon-green);
        border-radius: 5px;
        color: var(--neon-green);
        font-family: 'Roboto Mono', monospace;
        padding: 10px;
        position: relative;
        overflow: hidden;
    }

    .stCodeBlock::before {
        content: '';
        position: absolute;
        top: -10px;
        left: -10px;
        right: -10px;
        bottom: -10px;
        background: linear-gradient(45deg, var(--neon-green), transparent);
        opacity: 0.1;
        z-index: -1;
    }
</style>
"""

# Apply the custom CSS
st.markdown(custom_css, unsafe_allow_html=True)
AVATAR_WIDTH = 600
AVATAR_HEIGHT = 800



# Your Streamlit app code goes here
st.title("NeuraSense AI")


# 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, 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)



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)



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)]



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



class NeuralNetworkSimulator:
   @staticmethod
   def process(inputs):
       weights = np.random.rand(len(inputs))
       return np.dot(inputs, weights) / np.sum(weights)



# Set up MediaPipe Pose
mp_pose = mp.solutions.pose
pose = mp_pose.Pose(static_image_mode=True, min_detection_confidence=0.5)

def detect_humanoid(image):
    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    results = pose.process(image_rgb)
    
    if results.pose_landmarks:
        landmarks = results.pose_landmarks.landmark
        image_height, image_width, _ = image.shape
        keypoints = []
        for landmark in landmarks:
            x = int(landmark.x * image_width)
            y = int(landmark.y * image_height)
            keypoints.append((x, y))
        return keypoints
    return []

def apply_touch_points(image, keypoints):
    draw = ImageDraw.Draw(image)
    for point in keypoints:
        draw.ellipse([point[0]-5, point[1]-5, point[0]+5, point[1]+5], fill='red')
    return image

def create_sensation_map(width, height, keypoints):
    sensation_map = np.zeros((height, width, 12))
    for y in range(height):
        for x in range(width):
            base_sensitivities = np.random.rand(12) * 0.5 + 0.5
            
            # Enhance sensitivities near keypoints
            for kp in keypoints:
                distance = np.sqrt((x - kp[0])**2 + (y - kp[1])**2)
                if distance < 30:  # Adjust this value to change the area of influence
                    base_sensitivities *= 1.5
            
            sensation_map[y, x, 0] = base_sensitivities[0] * np.random.rand()  # Pain
            sensation_map[y, x, 1] = base_sensitivities[1] * np.random.rand()  # Pleasure
            sensation_map[y, x, 2] = base_sensitivities[2] * np.random.rand()  # Pressure
            sensation_map[y, x, 3] = base_sensitivities[3] * (np.random.rand() * 10 + 30)  # Temperature
            sensation_map[y, x, 4] = base_sensitivities[4] * np.random.rand()  # Texture
            sensation_map[y, x, 5] = base_sensitivities[5] * np.random.rand()  # EM field
            sensation_map[y, x, 6] = base_sensitivities[6] * np.random.rand()  # Tickle
            sensation_map[y, x, 7] = base_sensitivities[7] * np.random.rand()  # Itch
            sensation_map[y, x, 8] = base_sensitivities[8] * np.random.rand()  # Quantum
            sensation_map[y, x, 9] = base_sensitivities[9] * np.random.rand()  # Neural
            sensation_map[y, x, 10] = base_sensitivities[10] * np.random.rand()  # Proprioception
            sensation_map[y, x, 11] = base_sensitivities[11] * np.random.rand()  # Synesthesia

    return sensation_map

def create_heatmap(sensation_map, sensation_type):
    plt.figure(figsize=(10, 15))
    sns.heatmap(sensation_map[:, :, sensation_type], cmap='viridis')
    
def create_heatmap(sensation_map, sensation_type):
    plt.figure(figsize=(10, 15))
    sns.heatmap(sensation_map[:, :, sensation_type], cmap='viridis')
    plt.title(f'{["Pain", "Pleasure", "Pressure", "Temperature", "Texture", "EM Field", "Tickle", "Itch", "Quantum", "Neural", "Proprioception", "Synesthesia"][sensation_type]} Sensation Map')
    plt.axis('off')
    
    buf = io.BytesIO()
    plt.savefig(buf, format='png')
    buf.seek(0)
    plt.close()
    
    return Image.open(buf)

def generate_ai_response(keypoints, sensation_map):
    num_keypoints = len(keypoints)
    avg_sensations = np.mean(sensation_map, axis=(0, 1))
    
    response = f"I detect {num_keypoints} key points on the humanoid figure. "
    response += "The average sensations across the body are:\n"
    for i, sensation in enumerate(["Pain", "Pleasure", "Pressure", "Temperature", "Texture", "EM Field", 
                                   "Tickle", "Itch", "Quantum", "Neural", "Proprioception", "Synesthesia"]):
        response += f"{sensation}: {avg_sensations[i]:.2f}\n"
    
    return response

# Streamlit app
st.title("NeuraSense AI - Humanoid Touch Point Detection")

uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Read the image
    image = Image.open(uploaded_file)
    image_np = np.array(image)
    
    # Detect humanoid keypoints
    keypoints = detect_humanoid(image_np)
    
    # Apply touch points to the image
    processed_image = apply_touch_points(image.copy(), keypoints)
    
    # Display the processed image
    st.image(processed_image, caption='Processed Image with Touch Points', use_column_width=True)
    
    # Create sensation map
    sensation_map = create_sensation_map(image.width, image.height, keypoints)
    
    # Display heatmaps for different sensations
    sensation_types = ["Pain", "Pleasure", "Pressure", "Temperature", "Texture", "EM Field", 
                       "Tickle", "Itch", "Quantum", "Neural", "Proprioception", "Synesthesia"]
    
    selected_sensation = st.selectbox("Select a sensation to view:", sensation_types)
    heatmap = create_heatmap(sensation_map, sensation_types.index(selected_sensation))
    st.image(heatmap, use_column_width=True)

    # Generate AI response based on the image and sensations
    if st.button("Generate AI Response"):
        response = generate_ai_response(keypoints, sensation_map)
        st.write("AI Response:", response)


   


# 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)
  
def generate_neural_network_lines(img, draw):
    # 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
    

# Create and display avatar with heatmap
st.subheader("Avatar with Sensation Heatmap")
avatar_with_heatmap = create_avatar_with_heatmap()
st.image(avatar_with_heatmap, use_column_width=True)
# Create avatar function
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
 # Adjust alpha channel of heatmap
    data = np.array(heatmap_img)
    if data.shape[2] == 3:  # If RGB, add an alpha channel
        data = np.concatenate([data, np.full((data.shape[0], data.shape[1], 1), 255, dtype=np.uint8)], axis=2)
    data[:, :, 3] = data[:, :, 3] * 0.5  # Reduce opacity to 50%
    heatmap_img = Image.fromarray(data)
    
    combined_img = Image.alpha_composite(avatar_img.convert('RGBA'), heatmap_img)
    return combined_img

# Streamlit app
st.title("NeuraSense AI: Advanced Humanoid Techno-Sensory Simulation")



# Create two columns

# Create three columns
col1, col2, col3 = st.columns(3)



# Avatar display with touch interface
with col1:
   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_with_heatmap,
        height=AVATAR_HEIGHT,
        width=AVATAR_WIDTH,
        drawing_mode="point",
        key="canvas",
    )


with col3:
    st.subheader("Sensation Heatmap")
    heatmap = create_heatmap(avatar_sensation_map)
    st.image(heatmap, use_column_width=True)
        # 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)
    
    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 = (
    "```\n"
    "+---------------------------------------------+\n"
    f"| Pressure     : {measured_pressure:.2f}".ljust(45) + "|\n"
    f"| Temperature  : {measured_temp:.2f}°C".ljust(45) + "|\n"
    f"| Texture      : {measured_texture}".ljust(45) + "|\n"
    f"| EM Field     : {measured_em:.2f} μT".ljust(45) + "|\n"
    f"| Quantum State: {quantum_state:.2f}".ljust(45) + "|\n"
    "+---------------------------------------------+\n"
    f"| Pain Level   : {pain_level:.2f}".ljust(45) + "|\n"
    f"| Pleasure     : {pleasure_level:.2f}".ljust(45) + "|\n"
    f"| Tickle       : {tickle_level:.2f}".ljust(45) + "|\n"
    f"| Itch         : {itch_level:.2f}".ljust(45) + "|\n"
    f"| Proprioception: {proprioception:.2f}".ljust(44) + "|\n"
    f"| Synesthesia  : {synesthesia}".ljust(45) + "|\n"
    f"| Neural Response: {neural_response:.2f}".ljust(43) + "|\n"
    "+---------------------------------------------+\n"
    "```"
)

st.code(data_display, language="")

# Generate description
prompt = (
    "Human: Analyze the sensory input for a hyper-advanced AI humanoid:\n"
    "    Location: (" + str(round(touch_x, 1)) + ", " + str(round(touch_y, 1)) + ")\n"
    "    Duration: " + str(round(touch_duration, 1)) + "s, Intensity: " + str(round(touch_pressure, 2)) + "\n"
    "    Pressure: " + str(round(measured_pressure, 2)) + "\n"
    "    Temperature: " + str(round(measured_temp, 2)) + "°C\n"
    "    Texture: " + measured_texture + "\n"
    "    EM Field: " + str(round(measured_em, 2)) + " μT\n"
    "    Quantum State: " + str(quantum_state) + "\n"
    "    Resulting in:\n"
    "    Pain: " + str(round(pain_level, 2)) + ", Pleasure: " + str(round(pleasure_level, 2)) + "\n"
    "    Tickle: " + str(round(tickle_level, 2)) + ", Itch: " + str(round(itch_level, 2)) + "\n"
    "    Proprioception: " + str(round(proprioception, 2)) + "\n"
    "    Synesthesia: " + synesthesia + "\n"
    "    Neural Response: " + str(round(neural_response, 2)) + "\n"
    "    Provide a detailed, scientific analysis of the AI's experience.\n"
    "    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:")

capabilities = [
    "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."
]

for capability in capabilities:
    st.write(capability)

st.write("The AI's responses are generated using an advanced language model, providing detailed scientific analysis of its sensory experiences.")
st.write("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.""")