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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 io | |
| import base64 | |
| from streamlit_drawable_canvas import st_canvas | |
| import streamlit as st | |
| # 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 | |
| custom_css = """ | |
| <style> | |
| @import url('https://fonts.googleapis.com/css2?family=Orbitron:wght@400;700&display=swap'); | |
| body { | |
| color: #E0E0E0; | |
| background-color: #0E1117; | |
| font-family: 'Orbitron', sans-serif; | |
| } | |
| .stApp { | |
| background-image: | |
| radial-gradient(circle at 10% 20%, rgba(0, 255, 255, 0.1) 0%, transparent 20%), | |
| radial-gradient(circle at 90% 80%, rgba(0, 255, 255, 0.1) 0%, transparent 20%), | |
| linear-gradient(135deg, #0E1117 0%, #1A1F2C 100%); | |
| animation: pulse 10s infinite alternate; | |
| } | |
| @keyframes pulse { | |
| 0% { background-position: 0% 50%; } | |
| 100% { background-position: 100% 50%; } | |
| } | |
| .stButton>button { | |
| color: #00FFFF; | |
| border: 2px solid #00FFFF; | |
| border-radius: 30px; | |
| background: linear-gradient(45deg, #1A1F2C, #2C3E50); | |
| box-shadow: 0 0 15px rgba(0, 255, 255, 0.5); | |
| transition: all 0.3s ease; | |
| } | |
| .stButton>button:hover { | |
| transform: scale(1.05); | |
| box-shadow: 0 0 25px rgba(0, 255, 255, 0.8); | |
| } | |
| .stSlider>div>div>div>div { | |
| background-color: #00FFFF; | |
| box-shadow: 0 0 10px #00FFFF; | |
| } | |
| .stTextArea, .stNumberInput, .stSelectbox { | |
| background-color: rgba(26, 31, 44, 0.8); | |
| color: #00FFFF; | |
| border: 2px solid #00FFFF; | |
| border-radius: 15px; | |
| backdrop-filter: blur(5px); | |
| transition: all 0.3s ease; | |
| } | |
| .stTextArea:focus, .stNumberInput:focus, .stSelectbox:focus { | |
| box-shadow: 0 0 20px rgba(0, 255, 255, 0.8); | |
| transform: translateY(-2px); | |
| } | |
| .stProgress > div > div { | |
| background-color: #00FFFF; | |
| background-image: linear-gradient(45deg, rgba(255,255,255,.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,.15) 50%, rgba(255,255,255,.15) 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; } | |
| } | |
| .stCheckbox > label > div { | |
| border-color: #00FFFF; | |
| } | |
| .stCheckbox > label > div[data-checked="true"] { | |
| background-color: #00FFFF; | |
| } | |
| /* Futuristic scrollbar */ | |
| ::-webkit-scrollbar { | |
| width: 10px; | |
| } | |
| ::-webkit-scrollbar-track { | |
| background: #1A1F2C; | |
| } | |
| ::-webkit-scrollbar-thumb { | |
| background: #00FFFF; | |
| border-radius: 5px; | |
| } | |
| ::-webkit-scrollbar-thumb:hover { | |
| background: #00CCCC; | |
| } | |
| /* Glowing text effect for headers */ | |
| h1, h2, h3 { | |
| text-shadow: 0 0 10px #00FFFF, 0 0 20px #00FFFF, 0 0 30px #00FFFF; | |
| } | |
| </style> | |
| """ | |
| # Apply the custom CSS | |
| st.markdown(custom_css, unsafe_allow_html=True) | |
| # Constants | |
| AVATAR_WIDTH, AVATAR_HEIGHT = 600, 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) | |
| # 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 = ( | |
| "```\n" | |
| "+---------------------------------------------+\n" | |
| "| Pressure : " + f"{measured_pressure:.2f}".ljust(23) + "|\n" | |
| "| Temperature : " + f"{measured_temp:.2f}°C".ljust(23) + "|\n" | |
| "| Texture : " + f"{measured_texture}".ljust(23) + "|\n" | |
| "| EM Field : " + f"{measured_em:.2f} μT".ljust(23) + "|\n" | |
| "| Quantum State: " + f"{quantum_state:.2f}".ljust(23) + "|\n" | |
| "+---------------------------------------------+\n" | |
| "| Pain Level : " + f"{pain_level:.2f}".ljust(23) + "|\n" | |
| "| Pleasure : " + f"{pleasure_level:.2f}".ljust(23) + "|\n" | |
| "| Tickle : " + f"{tickle_level:.2f}".ljust(23) + "|\n" | |
| "| Itch : " + f"{itch_level:.2f}".ljust(23) + "|\n" | |
| "| Proprioception: " + f"{proprioception:.2f}".ljust(22) + "|\n" | |
| "| Synesthesia : " + f"{synesthesia}".ljust(23) + "|\n" | |
| "| Neural Response: " + f"{neural_response:.2f}".ljust(21) + "|\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.""") | |