Create app.py

app.py

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+import streamlit as st
+import numpy as np
+from scipy.sparse import csr_matrix
+import pandas as pd
+import time
+import graphviz
+
+# -----------------------------------------------------------------------------
+# Title and Overview
+# -----------------------------------------------------------------------------
+st.title("Cortical Column Theory: Self-Modifying Memory Systems")
+st.markdown("""
+**Theory Overview:**  
+This application demonstrates a model inspired by Cortical Column Theory where the ability to self-modify is paramount.  
+- **Episodic Memory (E):** Represents short-term, conscious experience (~5–10 seconds) via introspective attention.
+- **Semantic Memory (K):** Cumulative knowledge built over time (Mass + Agency), enabling free energy formation.
+- **Neural Connectivity:** Modeled via sparse matrices to mimic voting neurons in cortical columns.
+- **Social Bonding:** Hierarchical connections—from teams to humanity—facilitate maximum free energy (or ‘love’) at a cellular level.
+
+These components interact in a dynamic system, much like how neocortical columns steer signals via voting neurons and dendritic excitement.
+""")
+
+# -----------------------------------------------------------------------------
+# Create Tabs for Organized UI Sections
+# -----------------------------------------------------------------------------
+tabs = st.tabs([
+    "Theory", 
+    "Neural Connectivity", 
+    "Concept Graph", 
+    "Interactive Components", 
+    "NPS Score", 
+    "Extra UI"
+])
+
+# -----------------------------------------------------------------------------
+# Tab 1: Theory Explanation
+# -----------------------------------------------------------------------------
+with tabs[0]:
+    st.header("Cortical Column Theory")
+    st.write("""
+    The central hypothesis is that life’s essential characteristic is its ability to self-modify.  
+    In this model:
+    
+    - **Episodic Memory (E)** functions as immediate, introspective attention over a 5–10 second window.  
+    - **Semantic Memory (K)** aggregates past experiences into a knowledge base, growing as new connections (graph edges) form.  
+    - **Free Energy** is produced as the system scales its pair bonds—from simple interactions (e.g., between two neurons) to complex networks (teams, organizations, and ultimately humanity).  
+    - **Love (❤️)** is conceptualized as the maximal connection, representing the highest free energy state and optimal bond formation.
+    
+    This theoretical framework abstracts how biological neural circuits might mirror self-coding systems in AI.
+    """)
+
+# -----------------------------------------------------------------------------
+# Tab 2: Neural Connectivity with Sparse Matrix
+# -----------------------------------------------------------------------------
+with tabs[1]:
+    st.header("Neural Connectivity Sparse Matrix")
+    st.write("Below is a demonstration of a sparse matrix simulating neural connectivity within a cortical column:")
+    
+    # Create a random binary matrix (10 neurons, ~20% connectivity)
+    size = 10
+    density = 0.2
+    random_matrix = np.random.binomial(1, density, size=(size, size))
+    sparse_matrix = csr_matrix(random_matrix)
+    
+    st.write("Sparse Matrix Representation:")
+    st.write(sparse_matrix)
+    st.write("Dense Matrix Representation:")
+    st.write(random_matrix)
+
+# -----------------------------------------------------------------------------
+# Tab 3: Emoji and Concept Graph UI
+# -----------------------------------------------------------------------------
+with tabs[2]:
+    st.header("Emoji and Concept Graph")
+    st.write("Visualizing core concepts with emojis where each node represents a key component of the theory:")
+    
+    # Graphviz diagram using emojis and labels for key concepts.
+    graph_source = """
+    digraph G {
+        "Cortical Column 🧠" -> "Episodic Memory (E) ⏱️" [label="short-term"];
+        "Cortical Column 🧠" -> "Semantic Memory (K) 📚" [label="knowledge"];
+        "Episodic Memory (E) ⏱️" -> "Introspective Attention 🔍" [label="focus"];
+        "Semantic Memory (K) 📚" -> "Free Energy ⚡" [label="agency"];
+        "Free Energy ⚡" -> "Love ❤️" [label="bond"];
+        "Love ❤️" -> "Humanity 🌍" [label="connection"];
+    }
+    """
+    st.graphviz_chart(graph_source)
+
+# -----------------------------------------------------------------------------
+# Tab 4: Interactive UI Components
+# -----------------------------------------------------------------------------
+with tabs[3]:
+    st.header("Interactive Components Demonstration")
+    
+    st.subheader("Input and Selection")
+    concept_input = st.text_input("Enter a concept label:", "Cortical Column Theory")
+    time_window = st.slider("Select attention window (seconds)", 1, 10, 5)
+    memory_type = st.radio("Select memory type", ("Episodic (E)", "Semantic (K)"))
+    neural_component = st.selectbox("Choose a neural component", ["Neuron", "Synapse", "Dendrite", "Axon"])
+    additional_components = st.multiselect("Select additional components", ["Free Energy", "Agency", "Mass", "Bond"])
+    
+    st.subheader("Activation Controls")
+    if st.checkbox("Activate Introspective Attention"):
+        st.write("Introspective Attention Activated!")
+    
+    if st.button("Execute Self-Modification Cycle"):
+        st.write("**Self-Modification Cycle Executed**")
+        st.write(f"Memory Type Selected: {memory_type}")
+        st.write(f"Attention Window: {time_window} seconds")
+        st.write(f"Neural Component: {neural_component}")
+        st.write(f"Additional Components: {additional_components}")
+    
+    st.subheader("Media Components")
+    st.image("https://via.placeholder.com/150.png?text=Neural+Network", caption="Neural Network Representation")
+    # Note: The video below is a placeholder.
+    st.video("https://www.youtube.com/watch?v=dQw4w9WgXcQ")
+    
+    st.subheader("Data and JSON Display")
+    df = pd.DataFrame({
+        "Component": ["Neuron", "Synapse", "Dendrite", "Axon"],
+        "Status": ["Active", "Active", "Inactive", "Active"]
+    })
+    st.dataframe(df)
+    sample_json = {
+        "Episodic": {"Duration": f"{time_window} sec", "Type": memory_type},
+        "Semantic": {"Label": concept_input}
+    }
+    st.json(sample_json)
+    
+    st.subheader("File Upload and Color Picker")
+    uploaded_file = st.file_uploader("Upload a configuration file")
+    color = st.color_picker("Pick a highlight color", "#00f900")
+    st.write("Selected Color:", color)
+    
+    st.subheader("Date and Time Inputs")
+    date_input = st.date_input("Select a date")
+    time_input = st.time_input("Select a time")
+    st.write("Date:", date_input, "Time:", time_input)
+    
+    st.subheader("Progress Bar Simulation")
+    progress_bar = st.progress(0)
+    for percent_complete in range(101):
+        progress_bar.progress(percent_complete)
+        time.sleep(0.01)
+    
+    st.subheader("Metrics and Download Button")
+    st.metric(label="Introspective Score", value=time_window*10, delta="+5")
+    st.download_button("Download Configuration", data="configuration data", file_name="config.txt")
+
+# -----------------------------------------------------------------------------
+# Tab 5: Self Reward Learning NPS Score
+# -----------------------------------------------------------------------------
+with tabs[4]:
+    st.header("Self Reward Learning NPS Score")
+    nps_score = st.slider("Rate Self Reward Learning (0-10):", 0, 10, 5)
+    if nps_score <= 6:
+        nps_comment = "Needs Improvement - Consider refining self-modification algorithms."
+    elif nps_score <= 8:
+        nps_comment = "Good, but can be better - Fine-tuning required."
+    else:
+        nps_comment = "Excellent! - The system demonstrates robust self-reward learning."
+    st.write(f"**NPS Score:** {nps_score} - {nps_comment}")
+
+# -----------------------------------------------------------------------------
+# Tab 6: Extra UI Components for Extended Demonstration
+# -----------------------------------------------------------------------------
+with tabs[5]:
+    st.header("Extra UI Components")
+    with st.expander("More Details"):
+        st.write("Additional explanations or interactive widgets can be added here.")
+    
+    col1, col2 = st.columns(2)
+    with col1:
+        st.write("**Column 1:** Additional metrics or charts.")
+        st.line_chart(np.random.randn(20, 1))
+    with col2:
+        st.write("**Column 2:** Other interactive elements.")
+        st.bar_chart(np.random.randn(20, 1))