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 import numpy # 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) # Initialize MediaPipe Holistic mp_holistic = mp.solutions.holistic holistic = mp_holistic.Holistic(static_image_mode=True, min_detection_confidence=0.7) # Lists of landmark names for pose, face, and hands pose_landmarks = [name for name in mp_holistic.PoseLandmark.__members__] hand_landmarks = [name for name in mp_holistic.HandLandmark.__members__] def detect_landmarks(image_path): image = cv2.imread(image_path) image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) results = holistic.process(image_rgb) image_height, image_width, _ = image.shape keypoints = [] # Pose landmarks if results.pose_landmarks: for idx, landmark in enumerate(results.pose_landmarks.landmark): x = int(landmark.x * image_width) y = int(landmark.y * image_height) keypoints.append(('POSE_' + pose_landmarks[idx], (x, y))) # Compute chest point nose = results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE] left_shoulder = results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_SHOULDER] right_shoulder = results.pose_landmarks.landmark[mp_holistic.PoseLandmark.RIGHT_SHOULDER] left_hip = results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_HIP] right_hip = results.pose_landmarks.landmark[mp_holistic.PoseLandmark.RIGHT_HIP] chest_x = int((nose.x + left_shoulder.x + right_shoulder.x + left_hip.x + right_hip.x) / 5 * image_width) chest_y = int((nose.y + left_shoulder.y + right_shoulder.y + left_hip.y + right_hip.y) / 5 * image_height) keypoints.append(('CHEST', (chest_x, chest_y))) # Face landmarks if results.face_landmarks: for idx, landmark in enumerate(results.face_landmarks.landmark): x = int(landmark.x * image_width) y = int(landmark.y * image_height) keypoints.append(('FACE_LANDMARK_' + str(idx), (x, y))) # Left hand landmarks if results.left_hand_landmarks: for idx, landmark in enumerate(results.left_hand_landmarks.landmark): x = int(landmark.x * image_width) y = int(landmark.y * image_height) keypoints.append(('LEFT_HAND_' + hand_landmarks[idx], (x, y))) # Right hand landmarks if results.right_hand_landmarks: for idx, landmark in enumerate(results.right_hand_landmarks.landmark): x = int(landmark.x * image_width) y = int(landmark.y * image_height) keypoints.append(('RIGHT_HAND_' + hand_landmarks[idx], (x, y))) return keypoints def apply_touch_points(image_path, keypoints): image = cv2.imread(image_path) image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image_pil = Image.fromarray(image_rgb) draw = ImageDraw.Draw(image_pil) font = ImageFont.load_default() # Specify a better font if needed for name, point in keypoints: if 'CHEST' in name: draw.ellipse([point[0] - 10, point[1] - 10, point[0] + 10, point[1] + 10], fill='blue') draw.text((point[0] + 15, point[1]), name, fill='blue', font=font) else: draw.ellipse([point[0] - 5, point[1] - 5, point[0] + 5, point[1] + 5], fill='red') draw.text((point[0] + 10, point[1]), name, fill='red', font=font) return image_pil # Example usage image_path = 'path_to_your_image.jpg' keypoints = detect_landmarks(image_path) if keypoints: output_image = apply_touch_points(image_path, keypoints) output_image.show() else: print("No landmarks detected in the image.") # Function to create a sensation map def create_sensation_map(width, height, keypoints): sensation_map = np.random.rand(height, width, 12) * 0.5 + 0.5 x_grid, y_grid = np.meshgrid(np.arange(width), np.arange(height)) for kp in keypoints: kp_x, kp_y = kp dist = np.sqrt((x_grid - kp_x) ** 2 + (y_grid - kp_y) ** 2) influence = np.exp(-dist / 100) sensation_map[:, :, :12] *= 1 + (influence[..., np.newaxis]) * 1.2 return sensation_map # Function to create heatmap for a specific sensation type def create_heatmap(sensation_map, sensation_type): plt.figure(figsize=(10, 15)) sns.heatmap(sensation_map[:, :, sensation_type], cmap='viridis') plt.axis('off') buf = io.BytesIO() plt.savefig(buf, format='png') buf.seek(0) plt.close() heatmap_img = Image.open(buf) return heatmap_img # Function to generate AI response 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 ### Streamlit UI Logic ### # Initialize touch_x and touch_y with None or placeholder values touch_x, touch_y = None, None uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) if uploaded_file is not None: # Save and read the uploaded 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) # 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 the image with touch points fig, ax = plt.subplots() ax.imshow(processed_image) clicked_points = [] def onclick(event): global touch_x, touch_y # Use global to update the outer variables if event.xdata and event.ydata: touch_x, touch_y = int(event.xdata), int(event.ydata) clicked_points.append((touch_x, touch_y)) st.write(f"Clicked point: ({touch_x}, {touch_y})") # Display sensation values at the clicked point sensation = sensation_map[touch_y, touch_x] st.write("### Sensory Data Analysis") st.write(f"Pain: {sensation[0]:.2f} | Pleasure: {sensation[1]:.2f} | Pressure: {sensation[2]:.2f}") st.write(f"Temperature: {sensation[3]:.2f} | Texture: {sensation[4]:.2f} | EM Field: {sensation[5]:.2f}") st.write(f"Tickle: {sensation[6]:.2f} | Itch: {sensation[7]:.2f} | Quantum: {sensation[8]:.2f}") st.write(f"Neural: {sensation[9]:.2f} | Proprioception: {sensation[10]:.2f} | Synesthesia: {sensation[11]:.2f}") fig.canvas.mpl_connect('button_press_event', onclick) # Display the plot in Streamlit st.pyplot(fig) # Heatmap 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) # Simulate interaction if st.button("Simulate Interaction") and clicked_points: touch_x, touch_y = clicked_points[-1] # Interaction logic here... # Calculate average pressure across the image average_pressure = np.mean(sensation_map[:, :, 2]) # Pressure channel st.write(f"Average Pressure across the image: {average_pressure:.2f}") # Create a futuristic data display if touch_x is not None and touch_y is not None: location_str = f"({touch_x:.1f}, {touch_y:.1f})" else: location_str = "(no interaction yet)" data_display = ( "```\n" "+---------------------------------------------+\n" f"| Pressure : {average_pressure:.2f}".ljust(45) + "|\n" f"| Temperature : {np.mean(sensation_map[:, :, 3]):.2f}°C".ljust(45) + "|\n" f"| Texture : {np.mean(sensation_map[:, :, 4]):.2f}".ljust(45) + "|\n" f"| EM Field : {np.mean(sensation_map[:, :, 5]):.2f} μT".ljust(45) + "|\n" f"| Quantum State: {np.mean(sensation_map[:, :, 8]):.2f}".ljust(45) + "|\n" "+---------------------------------------------+\n" f"| Location: {location_str}".ljust(45) + "|\n" f"| Pain Level : {np.mean(sensation_map[:, :, 0]):.2f}".ljust(45) + "|\n" f"| Pleasure : {np.mean(sensation_map[:, :, 1]):.2f}".ljust(45) + "|\n" f"| Tickle : {np.mean(sensation_map[:, :, 6]):.2f}".ljust(45) + "|\n" f"| Itch : {np.mean(sensation_map[:, :, 7]):.2f}".ljust(45) + "|\n" f"| Proprioception: {np.mean(sensation_map[:, :, 10]):.2f}".ljust(44) + "|\n" f"| Synesthesia : {np.mean(sensation_map[:, :, 11]):.2f}".ljust(45) + "|\n" f"| Neural Response: {np.mean(sensation_map[:, :, 9]):.2f}".ljust(43) + "|\n" "+---------------------------------------------+\n" "```" ) # Display the futuristic data display using Streamlit's code block feature 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.")