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
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;
    }
</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 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

# 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
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",
    )

# 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.")