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 import gc import accelerate # 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 = """ """ # 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 @st.cache_resource def load_tokenizer(): return AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium") @st.cache_resource def load_model(): model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium", device_map="auto", torch_dtype=torch.float16) return model tokenizer = load_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) # 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') # Instead of displaying, save to a buffer buf = io.BytesIO() plt.savefig(buf, format='png') buf.seek(0) plt.close() # Close the figure to free up memory # Create an image from the buffer heatmap_img = Image.open(buf) return heatmap_img 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(show_heatmap=True): # Load avatar image avatar_img = Image.open("avatar.png").resize((AVATAR_WIDTH, AVATAR_HEIGHT)) if not show_heatmap: return avatar_img # Return the avatar image without heatmap # 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 # Create and display avatar with optional heatmap st.subheader("Avatar with Optional Sensation Heatmap") avatar_with_heatmap = create_avatar_with_heatmap(show_heatmap) st.image(avatar_with_heatmap, use_column_width=True) # 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) # Add this with your other UI elements show_heatmap = st.checkbox("Show Sensation Heatmap", value=True) 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.""") # After processing torch.cuda.empty_cache() gc.collect()