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_path): image = cv2.imread(image_path) 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_path, keypoints): image = cv2.imread(image_path) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image = Image.fromarray(image) 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') 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 uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) if uploaded_file is not None: # Read the image image_path = 'temp.jpg' with open(image_path, 'wb') as f: f.write(uploaded_file.getvalue()) # Detect humanoid keypoints keypoints = detect_humanoid(image_path) # Apply touch points to the image processed_image = apply_touch_points(image_path, keypoints) # Display the processed image st.image(processed_image, caption='Processed Image with Touch Points', use_column_width=True) # Create sensation map image = cv2.imread(image_path) image_height, image_width, _ = image.shape 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) # 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) # Constants AVATAR_WIDTH = 50 # Reduced size AVATAR_HEIGHT = 75 # Reduced size # Function to generate sensation data on-the-fly def generate_sensation_data(i, j): return np.random.rand() # Simplified sensation map st.subheader("Neuro-Sensory Map") titles = [ 'Pain', 'Pleasure', 'Pressure', 'Temperature', 'Texture', 'Tickle', 'Itch', 'Proprioception', 'Synesthesia' ] # Generate and display maps one at a time for title in titles: fig, ax = plt.subplots(figsize=(5, 5)) sensation_map = np.array([[generate_sensation_data(i, j) for j in range(AVATAR_WIDTH)] for i in range(AVATAR_HEIGHT)]) im = ax.imshow(sensation_map, cmap='plasma') ax.set_title(title) fig.colorbar(im, ax=ax) st.pyplot(fig) plt.close(fig) # Close the figure to free up memory st.write("The neuro-sensory maps illustrate the varying sensitivities across the AI's body. Brighter areas indicate heightened responsiveness to specific stimuli.") # Add information about the AI's capabilities st.subheader("NeuraSense AI: Advanced Sensory Capabilities") capabilities = [ "1. High-Precision Pressure Sensors", "2. Advanced Thermal Detectors", "3. Adaptive Texture Analysis", "4. Neural Network Integration", "5. Proprioception Simulation", "6. Synesthesia Emulation", "7. Tickle and Itch Simulation", "8. Adaptive Pain and Pleasure Modeling" ] for capability in capabilities: st.write(capability) # Interactive sensory exploration st.subheader("Interactive Sensory Exploration") exploration_type = st.selectbox("Choose a sensory exploration:", ["Synesthesia Experience", "Proprioceptive Mapping"]) if 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)]) buf = io.BytesIO() plt.figure(figsize=(5, 5)) plt.imshow(proprioceptive_map, cmap='coolwarm') plt.savefig(buf, format='png') plt.close() # Close the figure to free up memory proprioceptive_image = Image.open(buf) st.image(proprioceptive_image, use_column_width=True) # Footer st.write("---") st.write("NeuraSense AI: Advanced Sensory Simulation v4.0") st.write("Disclaimer: This is an advanced simulation and does not represent current technological capabilities.")