app.py

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 io
import base64
from streamlit_drawable_canvas import st_canvas



# 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;
   }
</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, 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)



# 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



avatar_sensation_map = create_sensation_map(AVATAR_WIDTH, AVATAR_HEIGHT)



# 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])



# 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_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)
  
   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.")