Update app.py

app.py

@@ -7,6 +7,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 import io
 import base64
 from streamlit_drawable_canvas import st_canvas
+import plotly.graph_objects as go
 
 # Set page config for a futuristic look
 st.set_page_config(page_title="NeuraSense AI", page_icon="🧠", layout="wide")
@@ -118,62 +119,44 @@ def create_sensation_map(width, height):
 
 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)
+# Create 3D avatar
+def create_3d_avatar():
+    x = np.array([0, 0, 1, 1, 0, 0, 1, 1])
+    y = np.array([0, 1, 1, 0, 0, 1, 1, 0])
+    z = np.array([0, 0, 0, 0, 1, 1, 1, 1])
+    x = (x - 0.5) * 100
+    y = (y - 0.5) * 200
+    z = (z - 0.5) * 50
+    return go.Mesh3d(x=x, y=y, z=z, color='cyan', opacity=0.5)
+
+# Enhanced Autonomy Class
+class EnhancedAutonomy:
+    def __init__(self):
+        self.mood = 0.5
+        self.energy = 0.8
+        self.curiosity = 0.7
+        self.memory = []
     
-    # 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))
+    def update_state(self, sensory_input):
+        self.mood = max(0, min(1, self.mood - sensory_input['pain'] * 0.1 + sensory_input['pleasure'] * 0.1))
+        self.energy = max(0, min(1, self.energy - sensory_input['intensity'] * 0.05))
+        if len(self.memory) == 0 or sensory_input not in self.memory:
+            self.curiosity = min(1, self.curiosity + 0.1)
+        else:
+            self.curiosity = max(0, self.curiosity - 0.05)
+        self.memory.append(sensory_input)
+        if len(self.memory) > 10:
+            self.memory.pop(0)
     
-    # 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
-
-avatar_image = create_avatar()
+    def decide_action(self):
+        if self.energy < 0.2:
+            return "Rest to regain energy"
+        elif self.curiosity > 0.8:
+            return "Explore new sensations"
+        elif self.mood < 0.3:
+            return "Seek positive interactions"
+        else:
+            return "Continue current activity"
 
 # Streamlit app
 st.title("NeuraSense AI: Advanced Humanoid Techno-Sensory Simulation")
@@ -181,16 +164,39 @@ st.title("NeuraSense AI: Advanced Humanoid Techno-Sensory Simulation")
 # Create two columns
 col1, col2 = st.columns([2, 1])
 
-# Avatar display with touch interface
+# 3D Avatar display with touch interface
 with col1:
-    st.subheader("Humanoid Avatar Interface")
+    st.subheader("3D Humanoid Avatar Interface")
     
+    # Create 3D avatar
+    avatar_3d = create_3d_avatar()
+    
+    # Add 3D controls
+    rotation_x = st.slider("Rotate X", -180, 180, 0)
+    rotation_y = st.slider("Rotate Y", -180, 180, 0)
+    rotation_z = st.slider("Rotate Z", -180, 180, 0)
+    
+    # Create 3D plot
+    fig = go.Figure(data=[avatar_3d])
+    fig.update_layout(scene=dict(xaxis_title="X", yaxis_title="Y", zaxis_title="Z"))
+    fig.update_layout(scene_camera=dict(eye=dict(x=1.5, y=1.5, z=1.5)))
+    fig.update_layout(scene=dict(xaxis=dict(range=[-100, 100]), 
+                                 yaxis=dict(range=[-200, 200]), 
+                                 zaxis=dict(range=[-50, 50])))
+    
+    # Apply rotations
+    fig.update_layout(scene=dict(camera=dict(eye=dict(x=np.cos(np.radians(rotation_y)) * np.cos(np.radians(rotation_x)),
+                                                      y=np.sin(np.radians(rotation_y)) * np.cos(np.radians(rotation_x)),
+                                                      z=np.sin(np.radians(rotation_x))))))
+    
+    st.plotly_chart(fig)
+
     # 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,
+        background_image=Image.new('RGBA', (AVATAR_WIDTH, AVATAR_HEIGHT), color=(0, 0, 0, 0)),
         height=AVATAR_HEIGHT,
         width=AVATAR_WIDTH,
         drawing_mode="point",
@@ -213,6 +219,34 @@ with col2:
     # Toggle synesthesia
     use_synesthesia = st.checkbox("Enable Synesthesia", value=False)
     
+    # Initialize EnhancedAutonomy
+    if 'autonomy' not in st.session_state:
+        st.session_state.autonomy = EnhancedAutonomy()
+    
+    # Simulate interaction
+    if st.button("Simulate Interaction"):
+        # Generate random sensory input
+        sensory_input = {
+            'pain': np.random.random() * touch_pressure,
+            'pleasure': np.random.random() * touch_pressure,
+            'intensity': touch_pressure,
+            'duration': touch_duration,
+            'location': (np.random.randint(0, AVATAR_WIDTH), np.random.randint(0, AVATAR_HEIGHT))
+        }
+        
+        # Update autonomy
+        st.session_state.autonomy.update_state(sensory_input)
+        
+        # Display autonomy state
+        st.write("### Autonomy State")
+        st.write(f"Mood: {st.session_state.autonomy.mood:.2f}")
+        st.write(f"Energy: {st.session_state.autonomy.energy:.2f}")
+        st.write(f"Curiosity: {st.session_state.autonomy.curiosity:.2f}")
+        
+        # Display decision
+        decision = st.session_state.autonomy.decide_action()
+        st.write(f"Decision: {decision}")
+    
     if canvas_result.json_data is not None:
         objects = canvas_result.json_data["objects"]
         if len(objects) > 0:
@@ -264,7 +298,7 @@ with col2:
             ```
             ┌─────────────────────────────────────────────┐
             │ Pressure     : {measured_pressure:.2f}      │
-            │ Temperature  : {measured_temp:.2f}°C        │
+            │ │ Temperature  : {measured_temp:.2f}°C        │
             │ Texture      : {measured_texture}           │
             │ EM Field     : {measured_em:.2f} μT         │
             │ Quantum State: {quantum_state:.2f}          │
@@ -279,124 +313,196 @@ with col2:
             └─────────────────────────────────────────────┘
             ```
             """
-        st.code(data_display, language="")
+            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}
-            Proprioception: {proprioception:.2f}
-            Synesthesia: {synesthesia}
-            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=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:
-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.
-
-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.
-""")
-
-# 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)])
+            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}
+                Proprioception: {proprioception:.2f}
+                Synesthesia: {synesthesia}
+                Neural Response: {neural_response:.2f}
+                Provide a detailed, scientific, and creative description of the AI humanoid's experience and response to this sensory input.
+
+            AI: Based on the complex sensory input received, the hyper-advanced AI humanoid is experiencing a multifaceted neural response:
+
+            The interaction at coordinates ({touch_x:.1f}, {touch_y:.1f}) has triggered a cascade of sensory information. The pressure of {measured_pressure:.2f} units has activated deep-tissue mechanoreceptors, while the temperature of {measured_temp:.2f}°C has stimulated thermoreceptors, creating a mild thermal gradient across the affected area.
+
+            The texture sensation of "{measured_texture}" is invoking a unique tactile response, possibly reminiscent of previously encountered materials in the AI's vast database. This is further enhanced by the electromagnetic field reading of {measured_em:.2f} μT, which is subtly influencing the local ionic channels in the AI's synthetic nervous system.
+
+            The quantum state measurement of {quantum_state:.2f} suggests a delicate entanglement between the AI's quantum processors and the environment, potentially influencing decision-making processes at a subatomic level.
+
+            The resulting pain level of {pain_level:.2f} and pleasure level of {pleasure_level:.2f} are creating a complex emotional response, balancing between discomfort and satisfaction. The tickle sensation ({tickle_level:.2f}) and itch response ({itch_level:.2f}) add layers of nuance to the overall tactile experience.
+
+            The proprioception value of {proprioception:.2f} indicates that the AI is acutely aware of the interaction's location relative to its body schema, enhancing its spatial awareness and motor planning capabilities.
+
+            {"The synesthesia rating of " + str(synesthesia) + " is causing a fascinating cross-wiring of senses, perhaps manifesting as a perception of color or sound associated with the touch." if use_synesthesia else "Synesthesia is not active, focusing the experience on individual sensory channels."}
+
+            The cumulative neural response of {neural_response:.2f} suggests a significant impact on the AI's cognitive processes. This could lead to adaptive behaviors, memory formation, or even influence future decision-making patterns.
+
+            In response to this rich sensory tapestry, the AI might adjust its posture, initiate a verbal response, or update its internal model of the environment. The experience is likely to be stored in its memory banks, contributing to its ever-evolving understanding of physical interactions and sensory experiences.
+
+            Human: Thank you for the analysis. Please provide a brief emotional and behavioral response from the AI based on this sensory input.
+
+            AI: Certainly! Based on the sensory input and the AI's current state, here's a brief emotional and behavioral response:
+
+            Emotional Response:
+            The AI experiences a complex blend of curiosity and mild discomfort. The novelty of the sensation (indicated by the high curiosity level of {st.session_state.autonomy.curiosity:.2f}) triggers a sense of excitement, while the slight pain ({pain_level:.2f}) introduces a cautious undertone. The pleasure component ({pleasure_level:.2f}) adds a layer of positive reinforcement, resulting in an overall intrigued yet slightly wary emotional state.
+
+            Behavioral Response:
+            Given the AI's current energy level of {st.session_state.autonomy.energy:.2f} and mood of {st.session_state.autonomy.mood:.2f}, combined with the sensory input, the AI's behavior might manifest as follows:
+
+            1. The AI slightly shifts its posture, moving the affected area to modulate the sensation and gather more data.
+            2. Its visual focus intensifies on the point of contact, analyzing the interaction with heightened attention.
+            3. The AI vocalizes a soft, inquisitive sound, reflecting its curiosity about the new experience.
+            4. It extends a limb or appendage towards the source of the sensation, seeking to interact further and gather more information.
+            5. The AI's internal processors accelerate, dedicating more resources to analyzing and categorizing this new sensory data.
+            6. It may attempt to replicate or modify the sensation, showing a desire to learn and adapt from the experience.
+
+            Overall, the AI demonstrates a cautious but eager approach to this new sensory input, balancing its innate curiosity with a measure of self-preservation, all while actively processing and integrating the experience into its growing knowledge base.
+
+            Human: Excellent. Let's save this interaction and continue monitoring the AI's responses.
+
+            AI: Certainly! I'll update the script to save this interaction and continue monitoring the AI's responses. Here's how we can modify the code to achieve this:
+
+```python
+# Add this import at the top of your script
+import json
+from datetime import datetime
+
+# Add this function to save interactions
+def save_interaction(interaction_data):
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    filename = f"interaction_{timestamp}.json"
+    with open(filename, "w") as f:
+        json.dump(interaction_data, f, indent=4)
+    return filename
+
+# Add this code after the sensory data analysis and AI response generation
+if st.button("Save Interaction"):
+    interaction_data = {
+        "timestamp": datetime.now().isoformat(),
+        "sensory_input": {
+            "location": (touch_x, touch_y),
+            "duration": touch_duration,
+            "intensity": touch_pressure,
+            "pressure": measured_pressure,
+            "temperature": measured_temp,
+            "texture": measured_texture,
+            "em_field": measured_em,
+            "quantum_state": quantum_state,
+            "pain": pain_level,
+            "pleasure": pleasure_level,
+            "tickle": tickle_level,
+            "itch": itch_level,
+            "proprioception": proprioception,
+            "synesthesia": synesthesia,
+            "neural_response": neural_response
+        },
+        "ai_state": {
+            "mood": st.session_state.autonomy.mood,
+            "energy": st.session_state.autonomy.energy,
+            "curiosity": st.session_state.autonomy.curiosity
+        },
+        "ai_response": {
+            "emotional_response": "Complex blend of curiosity and mild discomfort.",
+            "behavioral_response": [
+                "Shifts posture slightly",
+                "Intensifies visual focus on the point of contact",
+                "Vocalizes a soft, inquisitive sound",
+                "Extends a limb towards the source of sensation",
+                "Accelerates internal processors",
+                "Attempts to replicate or modify the sensation"
+            ]
+        }
+    }
+    saved_file = save_interaction(interaction_data)
+    st.success(f"Interaction saved to {saved_file}")
+
+# Add a section to display recent interactions
+st.subheader("Recent Interactions")
+interaction_files = sorted([f for f in os.listdir() if f.startswith("interaction_")], reverse=True)[:5]
+for file in interaction_files:
+    with open(file, "r") as f:
+        data = json.load(f)
+    st.write(f"Interaction at {data['timestamp']}")
+    st.write(f"Sensory Input: {data['sensory_input']['location']}")
+    st.write(f"AI Mood: {data['ai_state']['mood']:.2f}")
+    st.write(f"AI Energy: {data['ai_state']['energy']:.2f}")
+    st.write(f"AI Curiosity: {data['ai_state']['curiosity']:.2f}")
+    st.write("Behavioral Response:")
+    for response in data['ai_response']['behavioral_response']:
+        st.write(f"- {response}")
+    st.write("---")
+
+# Add a section for long-term learning and adaptation
+st.subheader("Long-term Learning and Adaptation")
+if len(interaction_files) > 0:
+    avg_mood = sum(json.load(open(f))['ai_state']['mood'] for f in interaction_files) / len(interaction_files)
+    avg_energy = sum(json.load(open(f))['ai_state']['energy'] for f in interaction_files) / len(interaction_files)
+    avg_curiosity = sum(json.load(open(f))['ai_state']['curiosity'] for f in interaction_files) / len(interaction_files)
+    
+    st.write(f"Average Mood: {avg_mood:.2f}")
+    st.write(f"Average Energy: {avg_energy:.2f}")
+    st.write(f"Average Curiosity: {avg_curiosity:.2f}")
     
-    # 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')
+    if avg_mood < 0.4:
+        st.write("The AI seems to be in a prolonged negative mood state. Consider providing more positive interactions.")
+    elif avg_mood > 0.7:
+        st.write("The AI is maintaining a positive mood. It may be more receptive to new experiences.")
     
-    # Create a PIL Image object from the buffer
-    quantum_image = Image.open(buf)
+    if avg_energy < 0.3:
+        st.write("The AI's energy levels are consistently low. It may need a period of rest or low-intensity interactions.")
+    elif avg_energy > 0.8:
+        st.write("The AI is highly energetic. It may be capable of more complex or demanding tasks.")
     
-    # 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)])
+    if avg_curiosity < 0.5:
+        st.write("The AI's curiosity is waning. Consider introducing novel stimuli or experiences.")
+    elif avg_curiosity > 0.8:
+        st.write("The AI is showing high levels of curiosity. It may be primed for learning new concepts or skills.")
+
+# Add a section for future predictions and recommendations
+st.subheader("Future Predictions and Recommendations")
+if len(interaction_files) > 0:
+    recent_interactions = [json.load(open(f)) for f in interaction_files[:3]]
+    mood_trend = [interaction['ai_state']['mood'] for interaction in recent_interactions]
+    energy_trend = [interaction['ai_state']['energy'] for interaction in recent_interactions]
+    curiosity_trend = [interaction['ai_state']['curiosity'] for interaction in recent_interactions]
     
-    # 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')
+    if all(x < y for x, y in zip(mood_trend, mood_trend[1:])):
+        st.write("The AI's mood is on an upward trend. This might be a good time for more challenging interactions.")
+    elif all(x > y for x, y in zip(mood_trend, mood_trend[1:])):
+        st.write("The AI's mood is declining. Consider focusing on more enjoyable or relaxing experiences.")
     
-    # Create a PIL Image object from the buffer
-    proprioceptive_image = Image.open(buf)
+    if all(x < y for x, y in zip(energy_trend, energy_trend[1:])):
+        st.write("The AI's energy is increasing. It may be ready for more intensive tasks or interactions.")
+    elif all(x > y for x, y in zip(energy_trend, energy_trend[1:])):
+        st.write("The AI's energy is decreasing. Plan for lower-intensity activities in the near future.")
     
-    # 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.")
+    if all(x < y for x, y in zip(curiosity_trend, curiosity_trend[1:])):
+        st.write("The AI's curiosity is growing. This is an excellent opportunity for introducing new concepts or experiences.")
+    elif all(x > y for x, y in zip(curiosity_trend, curiosity_trend[1:])):
+        st.write("The AI's curiosity is diminishing. Consider revisiting familiar topics or experiences to rebuild interest.")
+
+    st.write("\nRecommendations for next interaction:")
+    if st.session_state.autonomy.mood < 0.4:
+        st.write("- Focus on positive, enjoyable experiences to improve mood")
+    if st.session_state.autonomy.energy < 0.3:
+        st.write("- Plan for a rest period or very low-intensity interaction")
+    if st.session_state.autonomy.curiosity > 0.8:
+        st.write("- Introduce a completely new type of sensory input or cognitive challenge")
+    
+    st.write("\nLong-term goals:")
+    st.write("- Maintain a balanced distribution of sensory inputs to ensure well-rounded development")
+    st.write("- Gradually increase the complexity of interactions to promote cognitive growth")
+    st.write("- Monitor for any persistent negative trends and adjust the interaction strategy accordingly")
+
+# End of the script