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 # Set page config st.set_page_config(page_title="NeuraSense AI - Cyberpunk Edition", page_icon="🧠", layout="wide") # Enhanced Custom CSS for a hyper-cyberpunk realistic look custom_css = """ """ # 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 - Cyberpunk Edition") # 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 # New function to create heatmap def create_heatmap(sensation_map): overall_sensitivity = np.mean(sensation_map, axis=2) fig, ax = plt.subplots(figsize=(10, 15)) sns.heatmap(overall_sensitivity, cmap='YlOrRd', alpha=0.7, cbar=False, ax=ax) ax.set_axis_off() buf = BytesIO() plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0) buf.seek(0) heatmap_img = Image.open(buf) plt.close(fig) return heatmap_img def create_avatar_with_heatmap(): avatar_img = create_avatar() heatmap_img = create_heatmap(avatar_sensation_map) heatmap_img = heatmap_img.resize((AVATAR_WIDTH, AVATAR_HEIGHT)) 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 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_with_heatmap, 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.""")