Update app.py

app.py

@@ -5,9 +5,35 @@ from PIL import Image, ImageDraw, ImageFont
 import time
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import io
+import base64
+from streamlit_drawable_canvas import st_canvas
+
+# 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
+st.markdown("""
+<style>
+    body {
+        color: #E0E0E0;
+        background-color: #0E1117;
+    }
+    .stApp {
+        background-image: linear-gradient(135deg, #0E1117 0%, #1A1F2C 100%);
+    }
+    .stButton>button {
+        color: #00FFFF;
+        border-color: #00FFFF;
+        border-radius: 20px;
+    }
+    .stSlider>div>div>div>div {
+        background-color: #00FFFF;
+    }
+</style>
+""", unsafe_allow_html=True)
 
 # Constants
-AVATAR_WIDTH, AVATAR_HEIGHT = 400, 800
+AVATAR_WIDTH, AVATAR_HEIGHT = 600, 800
 
 # Set up DialoGPT model
 @st.cache_resource
@@ -19,214 +45,212 @@ def load_model():
 tokenizer, model = load_model()
 
 # Simulated Sensor Classes
-class Sensors:
+class AdvancedSensors:
     @staticmethod
-    def measure_pressure(base_sensitivity, duration):
-        return base_sensitivity * (1 - np.exp(-duration / 2))
+    def measure_pressure(base_sensitivity, pressure, duration):
+        return base_sensitivity * pressure * (1 - np.exp(-duration / 2))
 
     @staticmethod
-    def measure_temperature(base_temp, duration):
-        return base_temp + 5 * (1 - np.exp(-duration / 3))
+    def measure_temperature(base_temp, pressure, duration):
+        return base_temp + 10 * pressure * (1 - np.exp(-duration / 3))
 
     @staticmethod
     def measure_texture(x, y):
-        textures = ["smooth", "rough", "bumpy", "silky", "grainy", "soft", "hard", "moist", "dry", "fuzzy"]
+        textures = ["nano-smooth", "quantum-rough", "neuro-bumpy", "plasma-silky", "graviton-grainy", "zero-point-soft", "dark-matter-hard", "bose-einstein-condensate"]
         return textures[hash((x, y)) % len(textures)]
 
     @staticmethod
-    def measure_em_field(x, y):
-        return np.sin(x/50) * np.cos(y/50) * 10
+    def measure_em_field(x, y, sensitivity):
+        return (np.sin(x/30) * np.cos(y/30) + np.random.normal(0, 0.1)) * 10 * sensitivity
+
+    @staticmethod
+    def measure_quantum_state(x, y):
+        states = ["superposition", "entangled", "decoherent", "quantum tunneling", "quantum oscillation"]
+        return states[hash((x, y)) % len(states)]
 
 # Create more detailed sensation map for the avatar
 def create_sensation_map(width, height):
-    sensation_map = np.zeros((height, width, 8))  # pain, pleasure, pressure, temp, texture, em, tickle, itch
+    sensation_map = np.zeros((height, width, 10))  # pain, pleasure, pressure, temp, texture, em, tickle, itch, quantum, neural
     for y in range(height):
         for x in range(width):
-            # Head
-            if 150 < x < 250 and 50 < y < 200:
-                sensation_map[y, x] = [0.7, 0.5, 0.8, 0.6, 0.9, 0.9, 0.3, 0.4]
-            # Face
-            elif 160 < x < 240 and 80 < y < 180:
-                sensation_map[y, x] = [0.9, 0.7, 1.0, 0.8, 1.0, 1.0, 0.5, 0.6]
-            # Neck
-            elif 175 < x < 225 and 200 < y < 250:
-                sensation_map[y, x] = [0.8, 0.6, 0.9, 0.7, 0.8, 0.8, 0.7, 0.5]
-            # Torso
-            elif 150 < x < 250 and 250 < y < 500:
-                sensation_map[y, x] = [0.5, 0.6, 0.7, 0.8, 0.6, 0.7, 0.5, 0.3]
-            # Arms
-            elif (100 < x < 150 or 250 < x < 300) and 250 < y < 500:
-                sensation_map[y, x] = [0.6, 0.5, 0.9, 0.7, 0.8, 0.6, 0.7, 0.4]
-            # Hands
-            elif (75 < x < 125 or 275 < x < 325) and 450 < y < 525:
-                sensation_map[y, x] = [0.8, 0.7, 1.0, 0.9, 1.0, 0.8, 0.9, 0.7]
-            # Legs
-            elif 150 < x < 250 and 500 < y < 700:
-                sensation_map[y, x] = [0.7, 0.4, 0.8, 0.6, 0.7, 0.5, 0.6, 0.5]
-            # Feet
-            elif 150 < x < 250 and 700 < y < 800:
-                sensation_map[y, x] = [0.9, 0.6, 1.0, 0.8, 0.9, 0.7, 1.0, 0.8]
-            else:
-                sensation_map[y, x] = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
+            # Base sensitivities
+            base_sensitivities = np.random.rand(10) * 0.5 + 0.5
+            
+            # Enhance certain areas
+            if 200 < x < 400 and 100 < y < 200:  # Head
+                base_sensitivities *= 1.5
+            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
+            
+            sensation_map[y, x] = base_sensitivities
     
     return sensation_map
 
 avatar_sensation_map = create_sensation_map(AVATAR_WIDTH, AVATAR_HEIGHT)
 
-# Create more detailed human-like avatar
+# Create futuristic human-like avatar
 def create_avatar():
-    img = Image.new('RGB', (AVATAR_WIDTH, AVATAR_HEIGHT), color='white')
+    img = Image.new('RGBA', (AVATAR_WIDTH, AVATAR_HEIGHT), color=(0,0,0,0))
     draw = ImageDraw.Draw(img)
     
-    # Head
-    draw.ellipse([150, 50, 250, 200], fill='beige', outline='black')
-    # Hair
-    draw.polygon([(150, 120), (200, 30), (250, 120)], fill='brown')
-    # Eyes
-    draw.ellipse([175, 100, 195, 120], fill='white', outline='black')
-    draw.ellipse([205, 100, 225, 120], fill='white', outline='black')
-    draw.ellipse([182, 107, 188, 113], fill='blue')
-    draw.ellipse([212, 107, 218, 113], fill='blue')
-    # Nose
-    draw.polygon([(200, 130), (190, 150), (210, 150)], fill='beige', outline='black')
-    # Mouth
-    draw.arc([185, 160, 215, 180], start=0, end=180, fill='red', width=2)
-    
-    # Neck
-    draw.rectangle([175, 200, 225, 250], fill='beige', outline='black')
-    
-    # Body
-    draw.rectangle([150, 250, 250, 500], fill='lightblue', outline='black')
-    
-    # Arms
-    draw.rectangle([100, 250, 150, 500], fill='lightblue', outline='black')
-    draw.rectangle([250, 250, 300, 500], fill='lightblue', outline='black')
+    # Body outline
+    draw.polygon([(300, 100), (200, 250), (250, 600), (300, 750), (350, 600), (400, 250)], fill=(0, 255, 255, 100), outline=(0, 255, 255, 255))
     
-    # Hands
-    draw.ellipse([75, 450, 125, 525], fill='beige', outline='black')
-    draw.ellipse([275, 450, 325, 525], fill='beige', outline='black')
+    # Head
+    draw.ellipse([250, 50, 350, 150], fill=(0, 255, 255, 100), outline=(0, 255, 255, 255))
     
-    # Legs
-    draw.rectangle([150, 500, 200, 700], fill='navy', outline='black')
-    draw.rectangle([200, 500, 250, 700], fill='navy', outline='black')
+    # 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))
     
-    # Feet
-    draw.ellipse([140, 700, 210, 800], fill='beige', outline='black')
-    draw.ellipse([190, 700, 260, 800], fill='beige', outline='black')
+    # Neural network lines
+    for _ in range(50):
+        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("Advanced Humanoid Techno-Sensory Simulation")
+st.title("NeuraSense AI: Advanced Humanoid Techno-Sensory Simulation")
 
 # Create two columns
 col1, col2 = st.columns([2, 1])
 
-# Avatar display with crosshair
+# Avatar display with touch interface
 with col1:
-    st.subheader("Humanoid Avatar")
-    
-    # Touch input
-    touch_x = st.slider("Touch X coordinate", 0, AVATAR_WIDTH, AVATAR_WIDTH // 2)
-    touch_y = st.slider("Touch Y coordinate", 0, AVATAR_HEIGHT, AVATAR_HEIGHT // 2)
-    
-    # Add crosshair to avatar image
-    avatar_with_crosshair = avatar_image.copy()
-    draw = ImageDraw.Draw(avatar_with_crosshair)
-    draw.line((touch_x - 10, touch_y, touch_x + 10, touch_y), fill="red", width=2)
-    draw.line((touch_x, touch_y - 10, touch_x, touch_y + 10), fill="red", width=2)
+    st.subheader("Humanoid Avatar Interface")
     
-    # Display avatar with crosshair
-    st.image(avatar_with_crosshair, use_column_width=True)
+    # 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("Touch Controls")
+    st.subheader("Neural Interface Controls")
     
     # Touch duration
-    touch_duration = st.slider("Touch duration (seconds)", 0.1, 5.0, 1.0, 0.1)
+    touch_duration = st.slider("Interaction Duration (s)", 0.1, 5.0, 1.0, 0.1)
     
     # Touch pressure
-    touch_pressure = st.slider("Touch pressure", 0.1, 2.0, 1.0, 0.1)
+    touch_pressure = st.slider("Interaction Intensity", 0.1, 2.0, 1.0, 0.1)
     
-    if st.button("Apply Touch"):
-        sensation = avatar_sensation_map[touch_y, touch_x]
-        pain, pleasure, pressure_sens, temp_sens, texture_sens, em_sens, tickle_sens, itch_sens = sensation
-
-        measured_pressure = Sensors.measure_pressure(pressure_sens * touch_pressure, touch_duration)
-        measured_temp = Sensors.measure_temperature(37, touch_duration)
-        measured_texture = Sensors.measure_texture(touch_x, touch_y)
-        measured_em = Sensors.measure_em_field(touch_x, touch_y) * em_sens
-
-        # Calculate overall sensation
-        pain_level = pain * measured_pressure
-        pleasure_level = pleasure * (measured_temp - 37) / 5
-        tickle_level = tickle_sens * (1 - np.exp(-touch_duration / 0.5))
-        itch_level = itch_sens * (1 - np.exp(-touch_duration / 1.5))
-
-        st.write(f"Touch applied at ({touch_x}, {touch_y}) for {touch_duration:.1f} seconds")
-        st.write(f"Pressure: {measured_pressure:.2f}")
-        st.write(f"Temperature: {measured_temp:.2f}°C")
-        st.write(f"Texture: {measured_texture}")
-        st.write(f"Electromagnetic field: {measured_em:.2f}")
-        st.write(f"Pain level: {pain_level:.2f}")
-        st.write(f"Pleasure level: {pleasure_level:.2f}")
-        st.write(f"Tickle level: {tickle_level:.2f}")
-        st.write(f"Itch level: {itch_level:.2f}")
-
-        # Generate description
-        prompt = f"""Human: Describe the sensation when touched at ({touch_x}, {touch_y}) for {touch_duration:.1f} seconds with these measurements:
-        Pressure: {measured_pressure:.2f}
-        Temperature: {measured_temp:.2f}°C
-        Texture: {measured_texture}
-        Electromagnetic field: {measured_em:.2f}
-        Resulting in:
-        Pain: {pain_level:.2f}, Pleasure: {pleasure_level:.2f}, Tickle: {tickle_level:.2f}, Itch: {itch_level:.2f}
-        Avatar:"""
-        
-        input_ids = tokenizer.encode(prompt, return_tensors="pt")
-        output = model.generate(input_ids, max_length=200, 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("Avatar: ")[-1].strip()
-        
-        st.write("Avatar's response:")
-        st.write(response)
+    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 = sensation
+
+            measured_pressure = AdvancedSensors.measure_pressure(pressure_sens, touch_pressure, touch_duration)
+            measured_temp = AdvancedSensors.measure_temperature(37, touch_pressure, touch_duration)
+            measured_texture = AdvancedSensors.measure_texture(touch_x, touch_y)
+            measured_em = AdvancedSensors.measure_em_field(touch_x, touch_y, em_sens)
+            quantum_state = AdvancedSensors.measure_quantum_state(touch_x, touch_y)
+
+            # 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))
+            neural_response = neural_sens * (measured_pressure + measured_temp - 37) / 10
+
+            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 = f"""
+            ```
+            ┌─────────────────────────────────────────────┐
+            │ Pressure     : {measured_pressure:.2f}      │
+            │ Temperature  : {measured_temp:.2f}°C        │
+            │ Texture      : {measured_texture}           │
+            │ EM Field     : {measured_em:.2f} μT         │
+            │ Quantum State: {quantum_state}              │
+            ├─────────────────────────────────────────────┤
+            │ Pain Level   : {pain_level:.2f}             │
+            │ Pleasure     : {pleasure_level:.2f}         │
+            │ Tickle       : {tickle_level:.2f}           │
+            │ Itch         : {itch_level:.2f}             │
+            │ Neural Response: {neural_response:.2f}      │
+            └─────────────────────────────────────────────┘
+            """
+            st.code(data_display, language="")
+
+            # Generate description
+            prompt = f"""Human: Analyze the sensory input for a hyper-advanced AI humanoid:
+            Location: ({touch_x:.1f}, {touch_y:.1f})
+            Duration: {touch_duration:.1f}s, Intensity: {touch_pressure:.2f}
+            Pressure: {measured_pressure:.2f}
+            Temperature: {measured_temp:.2f}°C
+            Texture: {measured_texture}
+            EM Field: {measured_em:.2f} μT
+            Quantum State: {quantum_state}
+            Resulting in:
+            Pain: {pain_level:.2f}, Pleasure: {pleasure_level:.2f}
+            Tickle: {tickle_level:.2f}, Itch: {itch_level:.2f}
+            Neural Response: {neural_response:.2f}
+            Provide a detailed, scientific analysis of the AI's experience.
+            AI:"""
+            
+            input_ids = tokenizer.encode(prompt, return_tensors="pt")
+            output = model.generate(input_ids, max_length=300, 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("Sensation Map Visualization")
-fig, axs = plt.subplots(2, 4, figsize=(20, 10))
-titles = ['Pain', 'Pleasure', 'Pressure', 'Temperature', 'Texture', 'EM Field', 'Tickle', 'Itch']
+st.subheader("Quantum Neuro-Sensory Map")
+fig, axs = plt.subplots(2, 5, figsize=(20, 8))
+titles = ['Pain', 'Pleasure', 'Pressure', 'Temperature', 'Texture', 'EM Field', 'Tickle', 'Itch', 'Quantum', 'Neural']
 
 for i, title in enumerate(titles):
-    ax = axs[i // 4, i % 4]
-    im = ax.imshow(avatar_sensation_map[:, :, i], cmap='viridis')
+    ax = axs[i // 5, i % 5]
+    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 sensation map shows the sensitivity of different body parts to various stimuli. Brighter colors indicate higher sensitivity.")
+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 some context about the avatar's sensory capabilities
-st.subheader("Avatar Sensory Capabilities")
+# Add information about the AI's advanced capabilities
+st.subheader("NeuraSense AI: Cutting-Edge Sensory Capabilities")
 st.write("""
-This advanced humanoid avatar is equipped with cutting-edge sensory technology:
+This hyper-advanced AI humanoid incorporates revolutionary sensory technology:
 
-1. Pressure Sensors: Highly sensitive to touch, with increased sensitivity in hands, feet, and face.
-2. Temperature Sensors: Can detect slight changes in temperature, simulating human thermal perception.
-3. Texture Analysis: Capable of distinguishing between various textures, from smooth to rough, soft to hard, and more.
-4. Electromagnetic Field Detection: Mimics the subtle EM sensitivity some humans report.
-5. Pain and Pleasure Processing: Simulates the complex interplay of pain and pleasure responses.
-6. Tickle Sensation: Replicates the unique tickle response, which can be pleasurable or uncomfortable.
-7. Itch Simulation: Reproduces the sensation of itching, which can be triggered by light touch or prolonged contact.
+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. Tickle and Itch Simulation: Replicates these unique sensations with quantum-level precision.
 
-The avatar's responses are generated using an advanced language model, attempting to describe the sensations in human-like terms. This simulation demonstrates the potential for creating highly responsive and realistic artificial sensory systems.
+The AI's responses are generated using an advanced language model, providing detailed scientific analysis of its sensory experiences. This simulation showcases the potential for creating incredibly sophisticated and responsive artificial sensory systems that go beyond human capabilities.
 """)
 
 # Footer
 st.write("---")
-st.write("Advanced Humanoid Techno-Sensory Simulation v2.0")
-st.write("Disclaimer: This is a simulation and does not represent actual human sensory experiences.")
+st.write("NeuraSense AI: Quantum-Enhanced Sensory Simulation v3.0")
+st.write("Disclaimer: This is an advanced simulation and does not represent current technological capabilities.")