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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 | |
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: | |
def measure(x, y, sensitivity): | |
return np.sin(x/20) * np.cos(y/20) * sensitivity * np.random.normal(1, 0.1) | |
class NanoThermalSensor: | |
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" | |
] | |
def measure(x, y): | |
return AdaptiveTextureSensor.textures[hash((x, y)) % len(AdaptiveTextureSensor.textures)] | |
class EMFieldSensor: | |
def measure(x, y, sensitivity): | |
return (np.sin(x / 30) * np.cos(y / 30) + np.random.normal(0, 0.1)) * 10 * sensitivity | |
class NeuralNetworkSimulator: | |
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 | |
# Create and display avatar with heatmap | |
st.subheader("Avatar with Sensation Heatmap") | |
# You need to define sensation_map and sensation_type before this | |
sensation_map = np.random.rand(AVATAR_HEIGHT 600, AVATAR_WIDTH, 300) # Example random sensation map | |
sensation_type = 0 # Example sensation type (0 for Pain) | |
avatar_with_heatmap = create_avatar_with_heatmap(sensation_map, sensation_type) | |
st.image(avatar_with_heatmap, use_column_width=True) | |
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 | |
def create_avatar_with_heatmap(sensation_map, sensation_type): | |
# Create the base avatar | |
avatar_img = create_avatar() | |
# Create a heatmap | |
heatmap_img = create_heatmap(sensation_map, sensation_type) | |
# Resize heatmap to match avatar size | |
heatmap_img = heatmap_img.resize((AVATAR_WIDTH, AVATAR_HEIGHT)) | |
# 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) | |
# Combine avatar and heatmap | |
combined_img = Image.alpha_composite(avatar_img.convert('RGBA'), heatmap_img.convert('RGBA')) | |
return combined_img | |
# Streamlit app | |
st.title("NeuraSense AI: Advanced Humanoid Techno-Sensory Simulation") | |
# 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.""") | |